Vibration welding of thermoplastics

Vibration welding (also known as linear or friction welding) refers to a process in which two workpieces are brought in contact under pressure, and a reciprocating motion (vibration) is applied along the common interface in order to generate heat. The resulting heat melts the workpieces, and they become welded when the vibration stops and the interface cools. Most machinery operates at 120 Hz, although equipment is available that runs between 100–240 Hz. Vibration can be achieved either through linear vibration welding, which uses a one dimensional back and forth motion, or orbital vibration welding which moves the pieces in small orbits relative to each other. Linear vibration welding is more common due to simpler and relatively cheaper machinery required.
Vibration welding is often used for larger applications where the parts to be joined have relatively flat seams, although the process can accommodate some out of plane curvature. Recently, the automotive industry has made extensive use of the process to produce parts like manifolds and lighting assemblies whose complex geometries prevent single component molding processes.
Advantages and disadvantages.
Vibration welding has numerous advantages over other conventional plastic welding processes. Since the heat is created at an interface, the molten polymers are not exposed to open air, preventing oxidation and contamination of the weld during the process. No filler material is required, and when welding components of the same material the joint can be expected to be just as strong as the bulk material. Heating is localized to the interface, decreasing the chances of material degradation seen with other processes which require a heat source well above the melt temperature of the material. The process itself is cost effective, with no consumables and short cycle times. Vibration welding produces virtually no smoke or fume, requires little surface preparation, and works well for a multitude of applications, making it well suited to mass production environments.
Vibration welding does have its drawbacks, however. The process does not lend itself well to low modulus thermoplastics or to joints between plastics with relatively high differences in melting temperatures. Vibration welding requires part specific fixturing and joint designs, and the part will be exposed to rigorous vibration during the welding cycle which may damage sensitive or miniature components. The finished weld will be surrounded by a significant amount of flash, which must be removed if appearance is an issue. Alternatively, joint geometries which hide the excess flash can be used. Lastly, the process is not well suited to welding of anything other than relatively flat joints.
Vibration welding process.
The vibration welding process consists of four steps: solid friction, transient flow, steady state flow, and solidification.
Solid friction.
In this first stage, vibration is commenced between two cold parts pressed together at a constant pressure. The frictional energy causes the polymers to heat. In this stage there is no weld penetration as melting has not yet occurred.
Transient flow.
In the transient flow step the polymer's surface begins to melt. The melt layer thickness quickly grows, causing the frictional forces to decrease. This decrease in friction decreases the heat input to the system, and a lateral flow of molten material begins to occur.
Steady state flow.
In this phase the melting rate of the material matches the flow of material extruded at the lateral surfaces. The material flow and thickness of the melt layer become constant. This is the step that determines the quality of the weld. This step is maintained until the desired ‘melt down’ thickness (thickness of the molten material) is achieved. At that time the vibration is stopped and the weld is allowed to cool.
Solidification.
During solidification the vibration is stopped, while pressure is maintained on the workpieces until no more molten material remains. Once cooled to room temperature, the joint should have near the strength of the bulk material. Pressure is only relieved once the joint reaches an acceptable strength.
Equipment.
A vibration welding machine is essentially a vertical machine press in which one side has been modified to vibrate.  The main components are the vibrating assembly, a lifting table, and a tooling fixture.
Vibrating assembly.
The vibrating assembly is a moving element driven either by hydraulics or more commonly, electromagnets. In the electromagnetic version, the heart of this assembly is a tuned spring-mass system powered by electrical coils acting on oppositely charged lamination stacks. The frequency of the electrical charges is matched to the mechanical frequency of the system. Although the amplitude can be adjusted on the machine the frequency can only be changed by changing the mass of the vibrating assembly. The moving portion of the tooling is affixed to the vibrating assembly.
Lifting table.
The lifting table is a hydraulic assembly attached to the fixed portion of the tooling. The lifting table brings the workpieces together, and applies pressure between the moving and stationary portions of the tooling.
Tooling.
Tooling refers to the fixtures which are attached to the vibrating assembly and lifting table that hold the work pieces in place. Tooling is application specific, and must allow for workpieces to be quickly switched out after every welding cycle. It is imperative that the tooling matches the workpieces closely enough to prevent any relative motion between the tooling and the workpieces, as this would reduce the amplitude of the weld and lower heat input as well as dimensional tolerances.
Process variables.
The vibration welding process has five main variables: frequency, amplitude, pressure, time, and depth.
Frequency.
Frequency refers to how many times per second a vibration cycle is completed. Most machinery runs at 120 Hz, although machinery is available that runs from 100–240 Hz. Frequency is dependent on the mass of the vibrating assembly, and as such can only be changed by switching out components of the assembly.
Amplitude.
Amplitude refers to the distance traveled during each vibratory cycle. Higher amplitudes tend to be used with lower frequencies, and vice versa. Higher amplitudes increase heat input at the cost of cleanliness and dimensional tolerances, making them more useful for larger parts. Lower amplitudes range from 0.7-1.8mm, while higher amplitudes describe cycles that cover 2-4mm.
Pressure.
Pressure is the primary controller of melt layer thickness, and must be kept within an optimal range in order to produce quality joints. Although pressure can vary between 0.5-20MPa across different materials and geometries, the tolerances for a given application are quite tight. Too little pressure will prevent sufficient heat generation, while too much pressure can cause all of the molten material to squeeze out of the joint. Both scenarios will result in a weak weld. Pressure is controlled by the lifting table.
Time.
The length of time that vibration is applied to the workpiece is another key factor. Time is directly proportional to heat generation and material loss to flash. Processes can be either time or depth controlled, with most modern processes being depth controlled. A depth controlled process will have a variable time, and vice versa.
Depth.
Depth refers to the distance traveled by the workpieces after vibration is started. Sometimes referred to as displacement, it is directly related to the amount of material loss to flash. In general depth should be kept close to or above the thickness of the melt layer at the beginning of the steady state stage. After this value, more depth only results in loss of material without an accompanying rise in joint strength.
Weld design.
Weld design for vibration welding must include a relatively large flat surface, although some out of plane curvature can be accommodated for. The most common type of joint is a butt joint, where two flat pieces with the same cross section are welded together. Variations on this joint can include u-flanges, tongue and groove joints, and even double tongue and groove joints. When appearances are important, flash traps can be used. Flash traps refer to  hollow areas in the cross section next to the weld area that collect the flash and hide it from view.


Parametric programming

Parametric programming is a type of mathematical optimization, where the optimization problem is solved as a function of one or multiple parameters. Developed in parallel to sensitivity analysis, its earliest mention can be found in a thesis from 1952. Since then, there have been considerable developments for the cases of multiple parameters, presence of integer variables as well as nonlinearities.
Notation.
In general, the following optimization problem is considered
where formula_2 is the optimization variable, formula_3 are the parameters, formula_4 is the objective function and formula_5 denote the constraints. formula_6 denotes a function whose output is the optimal value of the objective function formula_7. The set formula_8 is generally referred to as parameter space.
The optimal value (i.e. result of solving the optimization problem) is obtained by evaluating the function with an argument formula_3.
Classification.
Depending on the nature of formula_4 and formula_5 and whether the optimization problem features integer variables, parametric programming problems are classified into different sub-classes:
Applications.
In control theory generally and in process industries.
The connection between parametric programming and model predictive control for process manufacturing, established in 2000, has contributed to an increased interest in the topic. Parametric programming supplies the idea that optimization problems can be parametrized as functions that can be evaluated (similar to a lookup table). This in turns allows the optimization algorithms in optimal controllers to be implemented as pre-computed (off-line) mathematical functions, which may in some cases be simpler and faster to evaluate than solving a full optimization problem on-line. This also opens up the possibility of creating optimal controllers on chips (MPC on chip). However, the off-line parametrization of optimal solutions runs into the curse of dimensionality as the number of possible solutions grows with the dimensionality and number of constraints in the problem.
In CNC programming.
Parametric programming in the context of CNC (computer numerical control) is defining part-cutting cycles in terms of variables with reassignable values rather than via hardcoded/hardwired instances. An archetypically simple example is writing a G-code program to machine a family of washers: there is often no need to write 15 programs for 15 members of the family with various hole diameters, outer diameters, thicknesses, and materials, when it is practical instead to write 1 program that calls various variables and reads their current values from a table of assignments. The program then instructs the machine slides and spindles to move to various positions at various velocities, accordingly, addressing not only the sizes of the part (i.e., OD, ID, thickness) but also even the speeds and feeds needed for any given material (e.g., low-carbon steel, high-carbon steel; stainless steel of whichever grade; bronze, brass, or aluminum of whichever grade; polymer of whichever type). Custom Macros are often used in such programming.


Dlib

Dlib is a general purpose cross-platform software library written in the programming language C++. Its design is heavily influenced by ideas from design by contract and component-based software engineering. Thus it is, first and foremost, a set of independent software components. It is open-source software released under a Boost Software License.
Since development began in 2002, Dlib has grown to include a wide variety of tools. As of 2016, it contains software components for dealing with networking, threads, graphical user interfaces, data structures, linear algebra, machine learning, image processing, data mining, XML and text parsing, numerical optimization, Bayesian networks, and many other tasks. In recent years, much of the development has been focused on creating a broad set of statistical machine learning tools and in 2009 Dlib was published in the "Journal of Machine Learning Research". Since then it has been used in a wide range of domains.


End-to-end delay

End-to-end delay or one-way delay (OWD) refers to the time taken for a packet to be transmitted across a network from source to destination. It is a common term in IP network monitoring, and differs from round-trip time (RTT) in that only path in the one direction from source to destination is measured.
Measurement.
The ping utility measures the RTT, that is, the time to go and come back to a host. Half the RTT is often used as an approximation of OWD but this assumes that the forward and back paths are the same in terms of congestion, number of hops, or quality of service (QoS). This is not always a good assumption. To avoid such problems, the OWD may be measured directly.
Direct.
OWDs may be measured between two points "A" and "B" of an IP network through the use of synchronized clocks; "A" records a timestamp on the packet and sends it to "B", which notes the receiving time and calculates the OWD as their difference. The transmitted packets need to be identified at source and destination in order to avoid packet loss or packet reordering. However, this method suffers several limitations, such as requiring intensive cooperation between both parties, and the accuracy of the measured delay is subject to the synchronization precision.
The Minimum-Pairs Protocol is an example by which several cooperating entities, "A", "B", and "C", could measure OWDs between one of them and a fourth less cooperative one (e.g., between "B" and "X").
Estimate.
Transmission between two network nodes may be asymmetric, and the forward and reverse delays are not equal. Half the RTT value is the average of the forward and reverse delays and so may be sometimes used as an approximation to the end-to-end delay. The accuracy of such an estimate depends on the nature of delay distribution in both directions. As delays in both directions become more symmetric, the accuracy increases.
The probability mass function (PMF) of absolute error, "E", between the smaller of the forward and reverse OWDs and their average (i.e., RTT/2) can be expressed as a function of the network delay distribution as follows:
where "a" and "b" are the forward and reverse edges, and "fy(z)" is the PMF of delay of edge "z" (that is, "fy(z) = Pr{delay on edge z = y}).
Delay components.
End-to-end delay in networks comes from several sources including transmission delay, propagation delay, processing delay and queuing delay.


CRS Robotics

CRS Robotics Corporation (currently operating as Thermo CRS Limited) was a robotics company based out of Burlington, Ontario, Canada. CRS Robotics designed, manufactured, distributed, and serviced human scale articulated robots, and laboratory automation systems. Human scale robots have approximately the same reach, speed, the range of motion, the degree of articulation and lifting capacity as a human being and are designed specifically to perform tasks that are hazardous, highly repetitive or generally unsuited for humans. Laboratory Automation applications are used to speed the effort of drug discovery for pharmaceutical and biotechnology customers.
CRS Robotics was notable in the field of automated lab systems due to their developments in high throughput and ultra-high throughput automated systems. Among other things, these developments included their advanced scheduling software, called POLARA, which was an open and extensible platform for the management and control of complex automated systems. As an example, a "good portion of the work" for "the preliminary map of the human genetic code was performed on CRS Automated Lab Systems".
The company commenced operations in 1982 as an engineering firm providing consulting services to Canadian machine tool manufacturers in the area of machine controls. The company sold its first robot, the M1 small robot system, in 1985. The company shipped its first laboratory automation system in 1997. In 1998, they introduced the F3 Robot, a 6-axis robotic arm.
The company changed its name to CRS Robotics Corporation in 1994. In 1995, the company completed an initial public offering. The company was listed on the Toronto Stock Exchange.
In May 2002, the company was acquired 100% by the Thermo Electron Corporation, (NYSE: TMO), and its name was subsequently changed to Thermo CRS. After the acquisition, the company still continues to develop laboratory automation systems.


Charles L. Coffin

Charles L. Coffin of Detroit was awarded for an arc welding process using a metal electrode. This was the first time that metal melted from the electrode carried across the arc to deposit filler metal in the joint to make a weld. Two years earlier, Nikolay Slavyanov presented the same idea of transferring metal across an arc, but to cast metal in a mold.


Industrial control system

An industrial control system (ICS) is an electronic control system and associated instrumentation used for industrial process control. Control systems can range in size from a few modular panel-mounted controllers to large interconnected and interactive distributed control systems (DCSs) with many thousands of field connections. Control systems receive data from remote sensors measuring process variables (PVs), compare the collected data with desired setpoints (SPs), and derive command functions that are used to control a process through the final control elements (FCEs), such as control valves.
Larger systems are usually implemented by supervisory control and data acquisition (SCADA) systems, or DCSs, and programmable logic controllers (PLCs), though SCADA and PLC systems are scalable down to small systems with few control loops. Such systems are extensively used in industries such as chemical processing, pulp and paper manufacture, power generation, oil and gas processing, and telecommunications.
Discrete controllers.
The simplest control systems are based around small discrete controllers with a single control loop each. These are usually panel mounted which allows direct viewing of the front panel and provides means of manual intervention by the operator, either to manually control the process or to change control setpoints. Originally these would be pneumatic controllers, a few of which are still in use, but nearly all are now electronic.
Quite complex systems can be created with networks of these controllers communicating using industry-standard protocols. Networking allow the use of local or remote SCADA operator interfaces, and enables the cascading and interlocking of controllers. However, as the number of control loops increase for a system design there is a point where the use of a programmable logic controller (PLC) or distributed control system (DCS) is more manageable or cost-effective.
Distributed control systems.
A distributed control system (DCS) is a digital process control system (PCS) for a process or plant, wherein controller functions and field connection modules are distributed throughout the system. As the number of control loops grows, DCS becomes more cost effective than discrete controllers. Additionally, a DCS provides supervisory viewing and management over large industrial processes. In a DCS, a hierarchy of controllers is connected by communication networks, allowing centralised control rooms and local on-plant monitoring and control.
A DCS enables easy configuration of plant controls such as cascaded loops and interlocks, and easy interfacing with other computer systems such as production control. It also enables more sophisticated alarm handling, introduces automatic event logging, removes the need for physical records such as chart recorders and allows the control equipment to be networked and thereby located locally to the equipment being controlled to reduce cabling.
A DCS typically uses custom-designed processors as controllers and uses either proprietary interconnections or standard protocols for communication. Input and output modules form the peripheral components of the system.
The processors receive information from input modules, process the information and decide control actions to be performed by the output modules. The input modules receive information from sensing instruments in the process (or field) and the output modules transmit instructions to the final control elements, such as control valves.
The field inputs and outputs can either be continuously changing analog signals e.g. current loop or 2 state signals that switch either "on" or "off", such as relay contacts or a semiconductor switch.
Distributed control systems can normally also support Foundation Fieldbus, PROFIBUS, HART, Modbus and other digital communication buses that carry not only input and output signals but also advanced messages such as error diagnostics and status signals.
SCADA systems.
Supervisory control and data acquisition (SCADA) is a control system architecture that uses computers, networked data communications and graphical user interfaces for high-level process supervisory management. The operator interfaces which enable monitoring and the issuing of process commands, such as controller setpoint changes, are handled through the SCADA supervisory computer system. However, the real-time control logic or controller calculations are performed by networked modules which connect to other peripheral devices such as programmable logic controllers and discrete PID controllers which interface to the process plant or machinery.
The SCADA concept was developed as a universal means of remote access to a variety of local control modules, which could be from different manufacturers allowing access through standard automation protocols. In practice, large SCADA systems have grown to become very similar to distributed control systems in function, but using multiple means of interfacing with the plant. They can control large-scale processes that can include multiple sites, and work over large distances. This is a commonly-used architecture industrial control systems, however there are concerns about SCADA systems being vulnerable to cyberwarfare or cyberterrorism attacks.
The SCADA software operates on a supervisory level as control actions are performed automatically by RTUs or PLCs. SCADA control functions are usually restricted to basic overriding or supervisory level intervention. A feedback control loop is directly controlled by the RTU or PLC, but the SCADA software monitors the overall performance of the loop. For example, a PLC may control the flow of cooling water through part of an industrial process to a set point level, but the SCADA system software will allow operators to change the set points for the flow. The SCADA also enables alarm conditions, such as loss of flow or high temperature, to be displayed and recorded.
Programmable logic controllers.
PLCs can range from small modular devices with tens of inputs and outputs (I/O) in a housing integral with the processor, to large rack-mounted modular devices with a count of thousands of I/O, and which are often networked to other PLC and SCADA systems. They can be designed for multiple arrangements of digital and analog inputs and outputs, extended temperature ranges, immunity to electrical noise, and resistance to vibration and impact. Programs to control machine operation are typically stored in battery-backed-up or non-volatile memory.
History.
Process control of large industrial plants has evolved through many stages. Initially, control was from panels local to the process plant. However this required personnel to attend to these dispersed panels, and there was no overall view of the process. The next logical development was the transmission of all plant measurements to a permanently-staffed central control room. Often the controllers were behind the control room panels, and all automatic and manual control outputs were individually transmitted back to plant in the form of pneumatic or electrical signals. Effectively this was the centralisation of all the localised panels, with the advantages of reduced manpower requirements and consolidated overview of the process.
However, whilst providing a central control focus, this arrangement was inflexible as each control loop had its own controller hardware so system changes required reconfiguration of signals by re-piping or re-wiring. It also required continual operator movement within a large control room in order to monitor the whole process. With the coming of electronic processors, high-speed electronic signalling networks and electronic graphic displays it became possible to replace these discrete controllers with computer-based algorithms, hosted on a network of input/output racks with their own control processors. These could be distributed around the plant and would communicate with the graphic displays in the control room. The concept of "distributed control" was realised.
The introduction of distributed control allowed flexible interconnection and re-configuration of plant controls such as cascaded loops and interlocks, and interfacing with other production computer systems. It enabled sophisticated alarm handling, introduced automatic event logging, removed the need for physical records such as chart recorders, allowed the control racks to be networked and thereby located locally to plant to reduce cabling runs, and provided high-level overviews of plant status and production levels. For large control systems, the general commercial name "distributed control system" (DCS) was coined to refer to proprietary modular systems from many manufacturers which integrated high-speed networking and a full suite of displays and control racks.
While the DCS was tailored to meet the needs of large continuous industrial processes, in industries where combinatorial and sequential logic was the primary requirement, the PLC evolved out of a need to replace racks of relays and timers used for event-driven control. The old controls were difficult to re-configure and debug, and PLC control enabled networking of signals to a central control area with electronic displays. PLCs were first developed for the automotive industry on vehicle production lines, where sequential logic was becoming very complex. It was soon adopted in a large number of other event-driven applications as varied as printing presses and water treatment plants.
SCADA's history is rooted in distribution applications, such as power, natural gas, and water pipelines, where there is a need to gather remote data through potentially unreliable or intermittent low-bandwidth and high-latency links. SCADA systems use open-loop control with sites that are widely separated geographically. A SCADA system uses remote terminal units (RTUs) to send supervisory data back to a control centre. Most RTU systems always had some capacity to handle local control while the master station is not available. However, over the years RTU systems have grown more and more capable of handling local control.
The boundaries between DCS and SCADA/PLC systems are blurring as time goes on. The technical limits that drove the designs of these various systems are no longer as much of an issue. Many PLC platforms can now perform quite well as a small DCS, using remote I/O and are sufficiently reliable that some SCADA systems actually manage closed-loop control over long distances. With the increasing speed of today's processors, many DCS products have a full line of PLC-like subsystems that weren't offered when they were initially developed.
In 1993, with the release of IEC-1131, later to become IEC-61131-3, the industry moved towards increased code standardization with reusable, hardware-independent control software. For the first time, object-oriented programming (OOP) became possible within industrial control systems. This led to the development of both programmable automation controllers (PAC) and industrial PCs (IPC). These are platforms programmed in the five standardized IEC languages: ladder logic, structured text, function block, instruction list and sequential function chart. They can also be programmed in modern high-level languages such as C or C++. Additionally, they accept models developed in analytical tools such as MATLAB and Simulink. Unlike traditional PLCs, which use proprietary operating systems, IPCs utilize Windows IoT. IPC's have the advantage of powerful multi-core processors with much lower hardware costs than traditional PLCs and fit well into multiple form factors such as DIN rail mount, combined with a touch-screen as a panel PC, or as an embedded PC. New hardware platforms and technology have contributed significantly to the evolution of DCS and SCADA systems, further blurring the boundaries and changing definitions.
Security.
SCADA and PLCs are vulnerable to cyber attack. The U.S. Government Joint Capability Technology Demonstration (JCTD) known as MOSAICS (More Situational Awareness for Industrial Control Systems) is the initial demonstration of cybersecurity defensive capability for critical infrastructure control systems. MOSAICS addresses the Department of Defense (DOD) operational need for cyber defense capabilities to defend critical infrastructure control systems from cyber attack, such as power, water and wastewater, and safety controls, affect the physical environment. The MOSAICS JCTD prototype will be shared with commercial industry through Industry Days for further research and development, an approach intended to lead to an innovative, game-changing capabilities for cybersecurity for critical infrastructure control systems.


Process variable

In control theory, a process variable (PV; also process value or process parameter) is the current measured value of a particular part of a process which is being monitored or controlled. An example of this would be the temperature of a furnace. The current temperature is the process variable, while the desired temperature is known as the set-point (SP).
Control system use.
Measurement of process variables is essential in control systems to controlling a process. The value of the process variable is continuously monitored so that control may be exerted.
Four commonly measured variables that affect chemical and physical processes are: pressure, temperature, level and flow. but there are in fact a large number of measurement quantities which for international purposes use the International System of Units (SI)
The SP-PV error is used to exert control on a process so that the value of PV equals the value of the SP. A classic use of this is in the
PID controller.


Fuzzy relation

A fuzzy relation is the cartesian product of mathematical fuzzy sets. Two fuzzy sets are taken as input, the fuzzy relation is then equal to the cross product of the sets which is created by vector multiplication. Usually, a rule base is stored in a matrix notation which allows the fuzzy controller to update its internal values.
From a historical perspective, the first fuzzy relation was mentioned in the year 1971 by Lotfi A. Zadeh.
A practical approach to describe a fuzzy relation is based on a 2d table. At first, a table is created which consists of fuzzy values from 0..1. The next step is to apply the if-then-rules to the values. The resulting numbers are stored in the table as an array.
Fuzzy relations can be utilized in fuzzy databases.


IEC 62325

IEC 62325 is a set of standards related to deregulated energy market communications, based on the Common Information Model. IEC 62325 is a part of the International Electrotechnical Commission's (IEC) Technical Committee 57 (TC57) reference architecture for electric power systems, and is the responsibility of Working Group 16 (WG16).
Standard documents.
IEC 62325 consists of the following parts, detailed in separate IEC 62325 standard documents:


Electron-beam freeform fabrication

Electron-beam freeform fabrication (EBF3) is an additive manufacturing process that builds near-net-shape parts. It requires far less raw material and finish machining than traditional manufacturing methods. EBF3 is done in a vacuum chamber where an electron beam is focused on a constantly feeding source of metal, which is melted and applied as called for by a three-dimensional layered drawing - one layer at a time - on top of a rotating metallic substrate until the part is complete.
History.
The use of electron beam welding for additive manufacturing was first developed by Vivek Davee in 1995 as part of his PhD thesis at MIT. The process was referred to as electron beam solid freeform fabrication (EBSFF). A team at NASA Langley Research Center (LaRC) led by Karen Taminger developed the process, calling it electron beam freeform fabrication (EBF3). EBF3 is a NASA-patented additive manufacturing process designed to build near-net-shape parts requiring less raw material and finish machining than traditional manufacturing methods. EBF3 is a process by which NASA plans to build metal parts in zero-gravity environments; this layer-additive process uses an electron beam and a solid wire feedstocks to fabricate metallic parts. Future astronauts stationed on the Moon or Mars may be able to employ EBF3 to produce replacement parts locally rather than relying on parts launched from Earth, possibly even mining feedstock from the surrounding soils. The aviation industry has the most potential for the procedure, say experts at the NASA LaRC, because there should be significant progress made in reducing machining waste byproducts. Typically, an aircraft maker would start with a 6,000-pound block of titanium and use thousands of litres of cutting fluid to reduce it to a 300-pound item, leaving 5,700 pounds of material that needed to be recycled. According to Taminger, "With EBF3 you can build up the same part using only 350 pounds of titanium and machine away just 50 pounds to get the part into its final configuration. And the EBF3 process uses much less electricity to create the same part."
Process.
The operational concept of EBF3 is to build a near-net-shape metal part directly from a computer-aided design (CAD) file. Current computer-aided machining practices start with a CAD model and use a post-processor to write the machining instructions (G-code) defining the cutting tool paths needed to make the part. EBF3 uses a similar process, starting with a CAD model, numerically reducing it into layers, then using a post-processor to write the G-code defining the deposition path and process parameters for the EBF3 equipment. It uses a focused electron beam in a vacuum environment to create a molten pool on a metallic substrate. The beam is translated by the surface of the substrate while the metal wire is fed into the molten pool. The deposit solidifies immediately after the electron beam has passed, having sufficient structural strength to support itself. The sequence is repeated in a layer-additive manner to produce a near-net-shape part needing only finish machining. The EBF3 process is scalable for components from fractions of an inch to tens of feet in size, limited mainly by the size of the vacuum chamber and the amount of wire feedstock available.


The Israel Association for Automatic Control (IAAC) is a national member organization of the International Federation of Automatic Control (IFAC). IAAC was one of the first member organizations of IFAC, and was founded in the early sixties by control system researchers from the Technion – Israel Institute of Technology, and from Rafael Advanced Defense Systems Ltd.
The mission of IAAC is the advancement of science, applications, and education in the area of automatic control, with an emphasis of the needs of Israel. To meet this goal, IAAC pro-actively organizes conferences, lectures, courses, and workshops; promotes information exchange between the various organizations involved in automatic control; promotes co-operations between organizations involved in automatic control in Israel and abroad.


Adevs

adevs is a C++ library for building discrete event simulations. Adevs based on the Discrete Event System Specification DEVS and Dynamic DEVS modeling formalisms; it supports parallel discrete event simulation and a runtime system for OpenModelica. Adevs is developed by Jim Nutaro.
Adevs is free software and releases before the 2.8 release were released under GNU LGPL 2.0. The adevs 2.8 version is released under a BSD license.


Kharitonov's theorem

Kharitonov's theorem is a result used in control theory to assess the stability of a dynamical system when the physical parameters of the system are not known precisely. When the coefficients of the characteristic polynomial are known, the Routh–Hurwitz stability criterion can be used to check if the system is stable (i.e. if all roots have negative real parts). Kharitonov's theorem can be used in the case where the coefficients are only known to be within specified ranges. It provides a test of stability for a so-called interval polynomial, while Routh–Hurwitz is concerned with an ordinary polynomial.
Definition.
An interval polynomial is the family of all polynomials
where each coefficient formula_2 can take any value in the specified intervals
It is also assumed that the leading coefficient cannot be zero: formula_4.
Theorem.
An interval polynomial is stable (i.e. all members of the family are stable) if and only if the four so-called Kharitonov polynomials
are stable.
What is somewhat surprising about Kharitonov's result is that although in principle we are testing an infinite number of polynomials for stability, in fact we need to test only four. This we can do using Routh–Hurwitz or any other method. So it only takes four times more work to be informed about the stability of an interval polynomial than it takes to test one ordinary polynomial for stability.
Kharitonov's theorem is useful in the field of robust control, which seeks to design systems that will work well despite uncertainties in component behavior due to measurement errors, changes in operating conditions, equipment wear and so on.


Map-based controller

In the field of control engineering, a map-based controller is a controller whose outputs are based on values derived from a pre-defined lookup table. The inputs to the controller are usually values taken from one or more sensors and are used to index the output values in the lookup table. By effectively placing the transfer function as discrete entries within a lookup table, engineers free to modify smaller sections or update the whole list of entries as required.


Rockman Industries

Rockman Industries, formerly Rockman Cycles limited, is an Indian auto components manufacturer, based in New Delhi, India. The company is one of India's largest auto component manufacturers. Rockman Industries is primarily engaged in the manufacturing of aluminum die casting components, machined and painted assemblies, auto chains and parts. In January 2017, Rockman Industries entered the carbon composites sector with the acquisition of Moldex Composites, a UK-India carbon composite design and manufacturing company. Rockman was founded in 1960 and is led by Suman Kant Munjal, Chairman and Ujjwal Munjal, Managing Director.
History.
A part of the Hero Group, Rockman Industries (formerly Rockman Cycles limited.) was set up in 1960 and started to manufacture bicycle chains and hubs for Hero Cycles. In 1999, it diversified into high pressure aluminium die cast components and automotive chains for Hero MotoCorp (Erstwhile Hero Honda). In 2005 it closed the bicycle chains and hubs business and from November 2005, is only manufacturing die casting components and auto parts. In 2008, Rockman Industries set up a new auto components plant in Uttaranchal. In February 2014, Rockman Industries acquired Sargam Die Casting company and started its new facility at Bawal (Haryana). In January 2017, Rockman Industries acquired a majority stake in Moldex Composites to enter the aerospace, motorsport and high-end auto component manufacturing space. In 2019, company inaugurated two new plants at Vadodara and Tirupati. In Tirupati plant company is manufacturing four wheeler alloy wheels.
Manufacturing units.
Rockman Industries has eight manufacturing plants at Ludhiana, Haridwar, Chennai , Bawal, Surat, Vadodara and Tirupati. In Ludhiana they have two plants, one for chains and the other for aluminium die casting products. All other locations manufacture die casting components. Surat's unit is manufacturing advanced composites parts.
Customers.
Rockman Industries supplies its products to Hero MotoCorp and various global automotive companies including: TVS, Honda, Royal Enfield, Revolt, Ather, Hyundai, Kia, Ford Motor, Mahindra, Tata, Bosch, Stanadyne, Dana, Denso, Nemak, PSA AVTEC, KSP Automotive, Continental, Magna, Hanon System, Wabco, Mando, Getrag, BorgWarner, iwis and others.


Hoist controller

A hoist controller is the controller for a hoist. The term is used primarily in the context of electrically operated hoists, but it is apparent that the control systems of many 20th century steam hoists also incorporated controllers of significant complexity. Consider the control system of the Quincy Mine No. 2 Hoist. This control system included interlocks to close the throttle valve at the end of trip and to prevent opening the throttle again until the winding engine was reversed. The control system also incorporated a governor to control the speed of the hoist and indicator wheels to show the hoist operator the positions of the skips in the mine shaft.
The hoist controllers for modern electric mining hoists have long included such features as automatic starting of the hoist when the weight of coal or ore in the skip reaches a set point, automatic acceleration of the hoist to full speed and automatic deceleration at the end of travel.
Hoist controllers need both velocity and absolute position references taken, typically taken from the winding drum of the hoist. Modern hoist controllers replace many of the mechanical analog mechanisms of earlier controllers with digital control systems.


TurboPrint

TurboPrint is a closed source printer driver system for Linux, AmigaOS and MorphOS. It supports a number of printers that don't yet have a free driver, and fuller printer functionality on some printer models. In recent versions, it integrates with the CUPS printing system.


Production equipment control

Production equipment control involves production equipment that resides in the shop floor of a manufacturing company and its purpose is to produce goods of a wanted quality when provided with production resources of a required quality. In modern production lines the production equipment is fully automated using industrial control methods and involves limited unskilled labour participation. Modern production equipment consists of mechatronic modules that are integrated according to a control architecture. The most widely known architectures involve hierarchy, polyarchy, hetaerarchy and hybrid. The methods for achieving a technical effect are described by control algorithms, which may or may not utilize formal methods in their design.


Lyman filament extruder

 The Lyman filament extruder is a device for making 3-D printer filament suitable for use in 3-D printers like the RepRap. It is named after its developer Hugh Lyman and was the winner of the Desktop Factory Competition.
The goal in the competition was to build an open source filament extruder for less than $250 in components that can take ABS or PLA resin pellets, mix them with colorant, and extrude enough 1.75 mm diameter ± 0.05 mm filament that can be wrapped on a 1 kg spool. The machine must use the Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0) license.
The use of DIY filament extruders like the Lyman can significantly reduce the cost of printing with 3-D printers. The Lyman filament extruder was designed to handle pellets, but can also be used to make filament from other sources of plastic such as post-consumer waste like other RecycleBots. Producing plastic filament from recycled plastic has a significant positive environmental impact.


Fixed position assembly

Fixed position assembly refers to an assembly system or situation in which the product does not move while being assembled, this configuration is usually contrasted in operations management and industrial engineering with assembly lines. Dimensioning this system is very simple: considering CP as productive capacity and T as average assembly time, then N, number of working stations, is given by N=CP*T.


Agent mining

Agent mining is an interdisciplinary area that synergizes multiagent systems with data mining and machine learning.
The interaction and integration between multiagent systems and data mining have a long history. The very early work on agent mining focused on agent-based knowledge discovery, agent-based distributed data mining, and agent-based distributed machine learning, and using data mining to enhance agent intelligence.
The International Workshop on Agents and Data Mining Interaction has been held for more than 10 times, co-located with the International Conference on Autonomous Agents and Multi-Agent Systems. Several proceedings are available from Springer Lecture Notes in Computer Science.


Moving horizon estimation

Moving horizon estimation (MHE) is an optimization approach that uses a series of measurements observed over time, containing noise (random variations) and other inaccuracies, and produces estimates of unknown variables or parameters. Unlike deterministic approaches, MHE requires an iterative approach that relies on linear programming or nonlinear programming solvers to find a solution.
MHE reduces to the Kalman filter under certain simplifying conditions. A critical evaluation of the extended Kalman filter and the MHE found that the MHE improved performance at the cost of increased computational expense. Because of the computational expense, MHE has generally been applied to systems where there are greater computational resources and moderate to slow system dynamics. However, in the literature there are some methods to accelerate this method.
Overview.
The application of MHE is generally to estimate measured or unmeasured states of dynamical systems. Initial conditions and parameters within a model are adjusted by MHE to align measured and predicted values. MHE is based on a finite horizon optimization of a process model and measurements. At time the current process state is sampled and a minimizing strategy is computed (via a numerical minimization algorithm) for a relatively short time horizon in the past: formula_1. Specifically, an online or on-the-fly calculation is used to explore state trajectories that find (via the solution of Euler–Lagrange equations) an objective-minimizing strategy until time formula_2. Only the last step of the estimation strategy is used, then the process state is sampled again and the calculations are repeated starting from the time-shifted states, yielding a new state path and predicted parameters. The estimation horizon keeps being shifted forward and for this reason the technique is called moving horizon estimation. Although this approach is not optimal, in practice it has given very good results when compared with the Kalman filter and other estimation strategies.
Principles of MHE.
Moving horizon estimation (MHE) is a multivariable estimation algorithm that uses:
to calculate the optimum states and parameters.
The optimization estimation function is given by:
formula_3
without violating state or parameter constraints (low/high limits)
With:
formula_4 = "i" -th model predicted variable (e.g. predicted temperature)
formula_5 = "i" -th measured variable (e.g. measured temperature)
formula_6 = "i" -th estimated parameter (e.g. heat transfer coefficient)
formula_7 = weighting coefficient reflecting the relative importance of measured values formula_5
formula_9 = weighting coefficient reflecting the relative importance of prior model predictions formula_10
formula_11 = weighting coefficient penalizing relative big changes in formula_6
Moving horizon estimation uses a sliding time window. At each sampling time the window moves one step forward. It estimates the states in the window by analyzing the measured output sequence and uses the last estimated state out of the window, as the prior knowledge.


Rotary table

A rotary table is a precision work positioning device used in metalworking. It enables the operator to drill or cut work at exact intervals around a fixed (usually horizontal or vertical) axis. Some rotary tables allow the use of index plates for indexing operations, and some can also be fitted with dividing plates that enable regular work positioning at divisions for which indexing plates are not available. A rotary fixture used in this fashion is more appropriately called a dividing head (indexing head).
Construction.
The table shown is a manually operated type. Powered tables under the control of CNC machines are now available, and provide a fourth axis to CNC milling machines. Rotary tables are made with a solid base, which has provision for clamping onto another table or fixture. The actual table is a precision-machined disc to which the work piece is clamped (T slots are generally provided for this purpose). This disc can rotate freely, for indexing, or under the control of a worm (handwheel), with the worm wheel portion being made part of the actual table. High precision tables are driven by backlash compensating duplex worms.
The ratio between worm and table is generally 40:1, 72:1 or 90:1 but may be any ratio that can be easily divided exactly into 360°. This is for ease of use when indexing plates are available. A graduated dial and, often, a vernier scale enable the operator to position the table, and thus the work affixed to it with great accuracy.
A through hole is usually machined into the table. Most commonly, this hole is machined to admit a Morse taper center or fixture.
Use.
Rotary tables are most commonly mounted "flat", with the table rotating around a vertical axis, in the same plane as the cutter of a vertical milling machine. An alternate setup is to mount the rotary table on its end (or mount it "flat" on a 90° angle plate), so that it rotates about a horizontal axis. In this configuration a tailstock can also be used, thus holding the workpiece "between centers."
With the table mounted on a secondary table, the workpiece is accurately centered on the rotary table's axis, which in turn is centered on the cutting tool's axis. All three axes are thus coaxial. From this point, the secondary table can be offset in either the X or Y direction to set the cutter the desired distance from the workpiece's center. This allows concentric machining operations on the workpiece. Placing the workpiece eccentrically a set distance from the center permits more complex curves to be cut. As with other setups on a vertical mill, the milling operation can be either drilling a series of concentric, and possibly equidistant holes, or face or end milling either circular or semicircular shapes and contours.
A rotary table can be used:
Additionally, if converted to stepper motor operation, with a CNC milling machine and a tailstock, a rotary table allows many parts to be made on a mill that otherwise would require a lathe.
Applications.
Rotary tables have many applications, including being used in the manufacture and inspection process of important elements in aerospace, automation and scientific industries. The use of rotary tables stretches as far as the film and animation industry, being used to obtain accuracy and precision in filming and photography.


Sumitomo Precision Products

Sumitomo Precision Products Co., Ltd. is an integrated manufacturer of aerospace equipment, heat exchangers, hydraulic controls, wireless sensor networks, sensors, micro-electronics technology, and environmental systems. The aerospace division supplies its products to aerospace industries worldwide, including Boeing, Airbus, Bombardier, and Embraer.
History.
Formerly a division of Sumitomo Metal Industries, the aerospace business was spun off as Sumitomo Precision Products in 1961.


Elisra

Elisra Group is an Israeli manufacturer of high-tech electronic devices, mainly but not exclusively for military use. It makes equipment for electronic communication and surveillance, missile tracking and controlling systems, radar and lidar equipment. The group is composed of three companies: Elisra Electronic Systems, Tadiran Electronic Systems Ltd. and Tadiran Spectralink Ltd.
History.
Previously, Elisra was owned 70% by Elbit Systems and 30% by Israel Aerospace Industries (IAI); however, Elbit later acquired IAI's share and Elisra is now a wholly owned subsidiary of Elbit.
Elisra Electronic Systems supplies missile warning systems, active jammer systems, laser and IR detection systems for several kinds of aircraft and ground vessels of the Israel Defense Forces (IDF). Tadiran Electronic Systems manufactured the Tadiran Mastiff reconnaissance UAV, used by the IDF during the 1982 Lebanon War.


DESA company

Iran Heavy Diesel Manufacturing Company (DESA), is an Iranian company which is a manufacturer of heavy diesel engines from 200 to 3500 kW for railway, marine and power generation purposes.
History.
The "Iran Heavy Diesel Engine Mfg Co." (DESA) company was established in 1991 in Amol with the aim of developing industrial production of diesel engines; a factory of over 10,000m2 was constructed on a 80ha site, with facilities for quality testing and research and design.
The company obtained licenses to manufacture Wärtsilä engines and Ruston RK 215 engines under license from MAN. Wärtsilä engine assembly began in 1996, RK 215 engine production began in 2000.
In 2009 the company unveiled Iran's first indigenous heavy diesel engine, the D87. A dual fuel (gas / diesel) version is also to be produced.
Production.
The company produces diesel generators for permanent and emergency electricity supply. 104 units have been supplied to the Iran Telecommunications Company.
The company is a supplier to both the Iranian Islamic Republic Railways and the Raja Passenger Train Company.
DESA has manufactured the Ruston RK 215 diesel engine for the AD43C mainline diesel locomotive, of which 70 units were manufactured by Wagon Pars.
The company is also assembling 16V 4000 MTU type diesel engines for a contract for 150 IranRunner locomotives for passenger trains to be manufactured by Siemens and the MLC (Mapna Locomotive Engineering and Manufacturing Company). The first 30 units will be supplied by Siemens, the remaining 120 will be primarily manufactured domestically capacities and expertise over six years.
The company has also supplied 120 engines for trainsets, and 60 engines for railbuses.
The company also supplies engines for marine use, and dual fuel engines for powerplants.


Minimum energy control

In control theory, the minimum energy control is the control formula_1 that will bring a linear time invariant system to a desired state with a minimum expenditure of energy.
Let the linear time invariant (LTI) system be
with initial state formula_4. One seeks an input formula_5 so that the system will be in the state formula_6 at time formula_7, and for any other input formula_8, which also drives the system from formula_9 to formula_6 at time formula_7, the energy expenditure would be larger, i.e.,
To choose this input, first compute the controllability Gramian
Assuming formula_14 is nonsingular (if and only if the system is controllable), the minimum energy control is then
Substitution into the solution
verifies the achievement of state formula_6 at formula_7.


Type-2 fuzzy sets and systems

Type-2 fuzzy sets and systems generalize standard Type-1 fuzzy sets and systems so that more uncertainty can be handled. From the beginning of fuzzy sets, criticism was made about the fact that the membership function of a type-1 fuzzy set has no uncertainty associated with it, something that seems to contradict the word "fuzzy", since that word has the connotation of much uncertainty. So, what does one do when there is uncertainty about the value of the membership function? The answer to this question was provided in 1975 by the inventor of fuzzy sets, Lotfi A. Zadeh, when he proposed more sophisticated kinds of fuzzy sets, the first of which he called a "type-2 fuzzy set". A type-2 fuzzy set lets us incorporate uncertainty about the membership function into fuzzy set theory, and is a way to address the above criticism of type-1 fuzzy sets head-on. And, if there is no uncertainty, then a type-2 fuzzy set reduces to a type-1 fuzzy set, which is analogous to probability reducing to determinism when unpredictability vanishes.
Type1 fuzzy systems are working with a fixed membership function, while in type-2 fuzzy systems the membership function is fluctuating. A fuzzy set determines how input values are converted into fuzzy variables.
Overview.
In order to symbolically distinguish between a type-1 fuzzy set and a type-2 fuzzy set, a tilde symbol is put over the symbol for the fuzzy set; so, A denotes a type-1 fuzzy set, whereas Ã denotes the comparable type-2 fuzzy set. When the latter is done, the resulting type-2 fuzzy set is called a "general type-2 fuzzy set" (to distinguish it from the special interval type-2 fuzzy set).
Zadeh didn't stop with type-2 fuzzy sets, because in that 1976 paper he also generalized all of this to type-"n" fuzzy sets. The present article focuses only on type-2 fuzzy sets because they are the "next step" in the logical progression from type-1 to type-"n" fuzzy sets, where "n" = 1, 2, ... . Although some researchers are beginning to explore higher than type-2 fuzzy sets, as of early 2009, this work is in its infancy.
The membership function of a general type-2 fuzzy set, Ã, is three-dimensional (Fig. 1), where the third dimension is the value of the membership function at each point on its two-dimensional domain that is called its "footprint of uncertainty"(FOU).
For an interval type-2 fuzzy set that third-dimension value is the same (e.g., 1) everywhere, which means that no new information is contained in the third dimension of an interval type-2 fuzzy set. So, for such a set, the third dimension is ignored, and only the FOU is used to describe it. It is for this reason that an interval type-2 fuzzy set is sometimes called a "first-order uncertainty" fuzzy set model, whereas a general type-2 fuzzy set (with its useful third-dimension) is sometimes referred to as a "second-order uncertainty" fuzzy set model.
The FOU represents the blurring of a type-1 membership function, and is completely described by its two bounding functions (Fig. 2), a lower membership function (LMF) and an upper membership function (UMF), both of which are type-1 fuzzy sets! Consequently, it is possible to use type-1 fuzzy set mathematics to characterize and work with interval type-2 fuzzy sets. This means that engineers and scientists who already know type-1 fuzzy sets will not have to invest a lot of time learning about general type-2 fuzzy set mathematics in order to understand and use interval type-2 fuzzy sets.
Work on type-2 fuzzy sets languished during the 1980s and early-to-mid 1990s, although a small number of articles were published about them. People were still trying to figure out what to do with type-1 fuzzy sets, so even though Zadeh proposed type-2 fuzzy sets in 1976, the time was not right for researchers to drop what they were doing with type-1 fuzzy sets to focus on type-2 fuzzy sets. This changed in the latter part of the 1990s as a result of Jerry Mendel and his student's works on type-2 fuzzy sets and systems. Since then, more and more researchers around the world are writing articles about type-2 fuzzy sets and systems.
Interval type-2 fuzzy sets.
Interval type-2 fuzzy sets have received the most attention because the mathematics that is needed for such sets—primarily Interval arithmetic—is much simpler than the mathematics that is needed for general type-2 fuzzy sets. So, the literature about interval type-2 fuzzy sets is large, whereas the literature about general type-2 fuzzy sets is much smaller. Both kinds of fuzzy sets are being actively researched by an ever-growing number of researchers around the world and have resulted in successful employment in a variety of domains such as robot control.
Formally, the following have already been worked out for interval type-2 fuzzy sets:
Interval type-2 fuzzy logic systems.
Type-2 fuzzy sets are finding very wide applicability in rule-based fuzzy logic systems (FLSs) because they let uncertainties be modeled by them whereas such uncertainties cannot be modeled by type-1 fuzzy sets. A block diagram of a type-2 FLS is depicted in Fig. 3. This kind of FLS is used in fuzzy logic control, fuzzy logic signal processing, rule-based classification, etc., and is sometimes referred to as a "function approximation" application of fuzzy sets, because the FLS is designed to minimize an error function.
The following discussions, about the four components in Fig. 3 rule-based FLS, are given for an interval type-2 FLS, because to-date they are the most popular kind of type-2 FLS; however, most of the discussions are also applicable for a general type-2 FLS.
Rules, that are either provided by subject experts or are extracted from numerical data, are expressed as a collection of IF-THEN statements, e.g.,
Fuzzy sets are associated with the terms that appear in the antecedents (IF-part) or consequents (THEN-part) of rules, and with the inputs to and the outputs of the FLS. Membership functions are used to describe these fuzzy sets, and in a type-1 FLS they are all type-1 fuzzy sets, whereas in an interval type-2 FLS at least one membership function is an interval type-2 fuzzy set.
An interval type-2 FLS lets any one or all of the following kinds of uncertainties be quantified:
In Fig. 3, measured (crisp) inputs are first transformed into fuzzy sets in the Fuzzifier block because it is fuzzy sets and not numbers that activate the rules which are described in terms of fuzzy sets and not numbers. Three kinds of fuzzifiers are possible in an interval type-2 FLS. When measurements are:
In Fig. 3, after measurements are fuzzified, the resulting input fuzzy sets are mapped into fuzzy output sets by the Inference block. This is accomplished by first quantifying each rule using fuzzy set theory, and by then using the mathematics of fuzzy sets to establish the output of each rule, with the help of an inference mechanism. If there are "M" rules then the fuzzy input sets to the Inference block will activate only a subset of those rules, where the subset contains at least one rule and usually way fewer than "M" rules. The inference is done one rule at a time. So, at the output of the Inference block, there will be one or more "fired-rule fuzzy output sets".
In most engineering applications of an FLS, a number (and not a fuzzy set) is needed as its final output, e.g., the consequent of the rule given above is "Rotate the valve a bit to the right." No automatic valve will know what this means because "a bit to the right" is a linguistic expression, and a valve must be turned by numerical values, i.e. by a certain number of degrees. Consequently, the fired-rule output fuzzy sets have to be converted into a number, and this is done in the Fig. 3 Output Processing block.
In a type-1 FLS, output processing, called "defuzzification", maps a type-1 fuzzy set into a number. There are many ways for doing this, e.g., compute the union of the fired-rule output fuzzy sets (the result is another type-1 fuzzy set) and then compute the center of gravity of the membership function for that set; compute a weighted average of the centers of gravity of each of the fired rule consequent membership functions; etc.
Things are somewhat more complicated for an interval type-2 FLS, because to go from an interval type-2 fuzzy set to a number (usually) requires two steps (Fig. 3). The first step, called "type-reduction", is where an interval type-2 fuzzy set is reduced to an interval-valued type-1 fuzzy set. There are as many type-reduction methods as there are type-1 defuzzification methods. An algorithm developed by Karnik and Mendel now known as the "KM algorithm" is used for type-reduction. Although this algorithm is iterative, it is very fast.
The second step of Output Processing, which occurs after type-reduction, is still called "defuzzification". Because a type-reduced set of an interval type-2 fuzzy set is always a finite interval of numbers, the defuzzified value is just the average of the two end-points of this interval.
It is clear from Fig. 3 that there can be two outputs to an interval type-2 FLS—crisp numerical values and the type-reduced set. The latter provides a measure of the uncertainties that have flowed through the interval type-2 FLS, due to the (possibly) uncertain input measurements that have activated rules whose antecedents or consequents or both are uncertain. Just as standard deviation is widely used in probability and statistics to provide a measure of unpredictable uncertainty about a mean value, the type-reduced set can provide a measure of uncertainty about the crisp output of an interval type-2 FLS.
Computing with words.
Another application for fuzzy sets has also been inspired by Zadeh — "Computing with Words". Different acronyms have been used for "computing with words," e.g., CW and CWW. According to Zadeh:
Of course, he did not mean that computers would actually compute using words—single words or phrases—rather than numbers. He meant that computers would be activated by words, which would be converted into a mathematical representation using fuzzy sets and that these fuzzy sets would be mapped by a CWW engine into some other fuzzy set after which the latter would be converted back into a word. A natural question to ask is: Which kind of fuzzy set—type-1 or type-2—should be used as a model for a word? Mendel has argued, on the basis of Karl Popper's concept of "falsificationism", that using a type-1 fuzzy set as a model for a word is scientifically incorrect. An interval type-2 fuzzy set should be used as a (first-order uncertainty) model for a word. Much research is underway about CWW.
Applications.
Type-2 fuzzy sets were applied in the following areas:
Software.
Freeware MATLAB implementations, which cover general and interval type-2 fuzzy sets and systems, as well as type-1 fuzzy systems, are available at: http://sipi.usc.edu/~mendel/software.
Software supporting discrete interval type-2 fuzzy logic systems is available at:
DIT2FLS Toolbox - http://dit2fls.com/projects/dit2fls-toolbox/
DIT2FLS Library Package - http://dit2fls.com/projects/dit2fls-library-package/
Java libraries including source code for type-1, interval- and general type-2 fuzzy systems are available at: http://juzzy.wagnerweb.net/.
Python library for type 1 and type 2 fuzzy sets is available at: https://github.com/carmelgafa/type2fuzzy
Python library for interval type 2 fuzzy sets and systems is available at: https://github.com/Haghrah/PyIT2FLS
An open source Matlab/Simulink Toolbox for Interval Type-2 Fuzzy Logic Systems is available at: http://web.itu.edu.tr/kumbasart/type2fuzzy.htm
External links.
There are two "IEEE Expert Now" multi-media modules that can be accessed from the IEEE at: http://www.ieee.org/web/education/Expert_Now_IEEE/Catalog/AI.html


Solid ground curing

Solid ground curing (SGC) is a photo-polymer-based additive manufacturing (or 3D printing) technology used for producing models, prototypes, patterns, and production parts, in which the production of the layer geometry is carried out by means of a high-powered UV lamp through a mask. As the basis of solid ground curing is the exposure of each layer of the model by means of a lamp through a mask, the processing time for the generation of a layer is independent of the complexity of the layer. SGC was developed and commercialized by Cubital Ltd. of Israel in 1986 in the alternative name of Soldier System. While the method offered good accuracy and a very high fabrication rate, it suffered from high acquisition and operating costs due to system complexity. This led to poor market
acceptance. While the company still exists, systems are no longer being sold. Nevertheless, it's still an interesting example of the many technologies other than stereolithography, its predeceasing rapid prototyping process that also utilizes photo-polymer materials. Though Objet Geometries Ltd. of Israel retains intellectual property of the process after the closure of Cubital Ltd. in 2002, the technology is no longer being produced.
Technology.
Solid ground curing utilizes the general process of hardening of photopolymers by a complete lighting and hardening of the entire surface, using specially prepared masks. In SGC process, each layer of the prototype is cured by exposing to an ultra violet (UV) lamp instead of by laser scanning. So that, every portion in a layer are simultaneously cured and do not require any post-curing processes. The process contains the following steps.
Advantages and disadvantages.
The primary advantage of the solid ground curing system is that it does not require a support structure since wax is used to fill the voids, highly accurate products can be obtained. The model produced by SGC process is comparatively accurate in the Z-direction because the layer is milled after each light-exposure process. Although it offers good accuracy coupled with high throughput, it produces too much waste and its operating costs are comparatively high due to system complexity.


Jawaharpur Super Thermal Power Station

Jawaharpur Vidyut Utpadan Nigam Ltd is an upcoming thermal power plant coming up at Town Malawan district Etah in Uttar Pradesh, India. The power plant is one of the coal-based power plants of Jawaharpur Vidyut Utpadan Nigam Ltd. (JVUNL), a 100% subsidiary of UP Rajya Vidyut Utpadan Nigam Limited (UPRVUNL)
The Contract was awarded to Doosan Power Systems India (DPSI).
Capacity.
The planned capacity of the power plant is 1320 MW (2x660MW). Expected commissioning in year 2021.


Friction stir spot welding

Friction stir spot welding is a pressure welding process that operates below the melting point of the workpieces. It is a variant of friction stir welding.
Process description.
In friction stir spot welding, individual spot welds are created by pressing a rotating tool with high force onto the top surface of two sheets that overlap each other in the lap joint. The frictional heat and the high pressure plastify the workpiece material, so that the tip of the pin plunges into the joint area between the two sheets and stirs-up the oxides. The pin of the tool is plunged into the sheets until the shoulder is in contact with the surface of the top sheet. The shoulder applies a high forging pressure, which bonds the components metallurgically without melting. After a short dwell time, the tool is pulled out of the workpieces again so that a spot weld can be made about every 5 seconds.
The tool consists of a rotating pin and a shoulder. The pin is the part of the tool that penetrates into the materials. Both the pin and the shoulder may be profiled to push the plasticized material in a particular direction and to efficiently break-up and disperse the oxide skins on the adjacent surfaces. After retracting the tool, a hole remains, when using one-piece tools, which have already proven themselves as very reliable in the automotive and the rail vehicle industry. Often the rotating tool is surrounded by a non-rotating clamping ring with which the workpieces are pressed firmly against each other before and during welding by applying a clamping force. The clamping ring can also be used to reduce the pressing out of plasticized material to avoid the formation of burrs or beads to apply inert gas or to cool the tool via compressed air.
The most important process parameters are the speed and contact pressure. This results in the plunge feed rate for a given workpiece material. Modern spot welding guns can be used either via position control or force control or via a product-specific programmed force-displacement control. Often, position control is used until a certain displacement is reached, and then the control system is switched to force control during the dwell time. Even during the force-controlled dwell time, certain position values can be specified, which should not be undermatched or exceeded.
Spot welding guns.
Friction stir spot welding is performed with a spot welding gun, which is mounted on a console, flanged to an articulated robot or manually operated with a balancer to the component.
Process advantages.
Friction spot welding is characterized by a number of process advantages. Any damage to the material caused by the extreme heat, such as that produced by laser or arc welding, will not occur. In particular, in the case of artificially aged aluminum alloys, the strength in the weld seam and the heat-affected zone is much higher than in conventional welding methods.
Industrial use.
Friction stir spot welds have a high strength, so they are even suitable for parts that are exposed to particularly high loads. In addition to automotive and rail vehicle construction, the aerospace industry is developing the process e.g. for welding cockpit doors for helicopters. In the electrical industry aluminum and copper can be friction stir spot welded. Other applications are in façade and furniture manufacture, where the low heat input, especially in anodized sheets, leads to excellent optical properties.


LaSalle's invariance principle

LaSalle's invariance principle (also known as the invariance principle, Barbashin-Krasovskii-LaSalle principle, or Krasovskii-LaSalle principle) is a criterion for the asymptotic stability of an autonomous (possibly nonlinear) dynamical system.
Global version.
Suppose a system is represented as
where formula_2 is the vector of variables, with
If a formula_4(see Smoothness) function formula_5 can be found such that
then the set of accumulation points of any trajectory is contained in formula_8 where formula_8 is the union of complete trajectories contained entirely in the set formula_10.
If we additionally have that the function formula_11 is positive definite, i.e.
and if formula_8 contains no trajectory of the system except the trivial trajectory formula_16 for formula_17, then the origin is asymptotically stable.
Furthermore, if formula_11 is radially unbounded, i.e.
then the origin is globally asymptotically stable.
Local version.
If
hold only for formula_24 in some neighborhood formula_25 of the origin, and the set
does not contain any trajectories of the system besides the trajectory formula_27, then the local version of the invariance principle states that the origin is locally asymptotically stable.
Relation to Lyapunov theory.
If formula_28 is negative definite, then the global asymptotic stability of the origin is a consequence of Lyapunov's second theorem. The invariance principle gives a criterion for asymptotic stability in the case when formula_29 is only negative semidefinite.
Examples.
Simple example.
Example taken from.
Consider the vector field formula_30 in the plane. The function formula_31 satisfies formula_32, and is radially unbounded, showing that the origin is globally asymptotically stable.
Pendulum with friction.
This section will apply the invariance principle to establish the local asymptotic stability of a simple system, the pendulum with friction. This system can be modeled with the differential equation
where formula_34 is the angle the pendulum makes with the vertical normal, formula_35 is the mass of the pendulum, formula_36 is the length of the pendulum, formula_37 is the friction coefficient, and "g" is acceleration due to gravity.
This, in turn, can be written as the system of equations
Using the invariance principle, it can be shown that all trajectories that begin in a ball of certain size around the origin formula_40 asymptotically converge to the origin. We define formula_41 as
This formula_41 is simply the scaled energy of the system. Clearly, formula_44 is positive definite in an open ball of radius formula_45 around the origin. Computing the derivative,
Observe that formula_47. If it were true that formula_48, we could conclude that every trajectory approaches the origin by Lyapunov's second theorem. Unfortunately, formula_49 and formula_50 is only negative semidefinite since formula_51 can be non-zero when formula_52. However, the set
which is simply the set
does not contain any trajectory of the system, except the trivial trajectory x = 0. Indeed, if at some time formula_55, formula_56, then because
formula_51 must be less than formula_45 away from the origin, formula_59 and formula_60. As a result, the trajectory will not stay in the set formula_61.
All the conditions of the local version of the invariance principle are satisfied, and we can conclude that every trajectory that begins in some neighborhood of the origin will converge to the origin as formula_62.
History.
The general result was independently discovered by J.P. LaSalle (then at RIAS) and N.N. Krasovskii, who published in 1960 and 1959 respectively. While LaSalle was the first author in the West to publish the general theorem in 1960, a special case of the theorem was communicated in 1952 by Barbashin and Krasovskii, followed by a publication of the general result in 1959 by Krasovskii.


Adaptive Binary Optimization

Adaptive Binary Optimization, (ABO), is a supposed lossless image compression algorithm by MatrixView Ltd. It uses a patented method to compress "the high correlation found in digital content signals" and additional compression with standard entropy encoding algorithms such as Huffman coding.


Galahad library

The Galahad library is a thread-safe library of packages for the solution of mathematical optimization problems. The areas covered by the library are unconstrained and bound-constrained optimization, quadratic programming, nonlinear programming, systems of nonlinear equations and inequalities, and non-linear least squares problems. The library is mostly written in the Fortran 90 programming language.
The name of the library originates from its major package for general nonlinear programming, LANCELOT-B, the successor of the original augmented Lagrangian package LANCELOT of Conn, Gould and Toint.
Other packages in the library include:
Packages in the GALAHAD library accept problems modeled in either the Standard Input Format (SIF), or the AMPL modeling language. For problems modeled in the SIF, the GALAHAD library naturally relies upon the CUTEr package, an optimization toolbox providing all low-level functionalities required by solvers.
The library is available on several popular computing platforms, including Compaq (DEC) Alpha, Cray, HP, IBM RS/6000, Intel-like PCs, SGI and Sun. It is designed to be easily adapted to other platforms. Support is provided for many operating systems, including Tru64, Linux, HP-UX, AIX, IRIX and Solaris, and for a variety of popular Fortran 90 compilers on these platforms and operating systems.
The GALAHAD Library is authored and maintained by N.I.M. Gould, D. Orban and Ph.L. Toint.


Multiscanning

Multiscanning is running multiple anti-malware or antivirus engines concurrently. Traditionally, only a single engine can actively scan a system at a given time. Using multiple engines simultaneously can result in conflicts that lead to system freezes and application failures. However, a number of security applications and application suites have optimized multiple engines to work together.
Reason.
Testing agencies published results showing that no single antivirus engine is 100% effective against every malware threat. Because each engine uses different scanning methodologies and updates their malware definition files at various frequencies, using multiple engines increases the likelihood of catching malware before it can affect a system or network.


Login VSI

Login VSI maximizes the end-user experience for all digital workspaces. Login VSI's flagship product, Login Enterprise, is an automated testing platform that predicts performance, ensures business continuity and reduces risk. Login Enterprise tests the desktop and applications as a whole, from pre-production through to production. Login Enterprise includes standard “out-of-the-box” application template workloads. The technology is used by large organizations using Citrix XenApp, Citrix XenDesktop, VMware Horizon and Microsoft RDS. Login VSI has over 400 customers in 50 countries.
Login VSI evolved from the consulting firm Login Consultants. The first product was commercially released in 2008 and was free. As large enterprises began adopting the product, the need to commercialize the product became evident. Login VSI was formed in 2012.
In 2016, Login VSI announced the public launch of its second product, Login PI. Login PI is an active monitoring tool that constantly runs a single virtual user, to monitor and safeguard a good performance and availability of virtual desktop infrastructures and associated business applications. Both products use virtual users (or synthetic users) to test systems, without the need for real users.
In 2018, Login VSI announced the Login VSI Enterprise Edition (a combination of Login VSI and Login PI), and the Login VSI Vendor Edition (focused on the industry-standard tests done by this audience).


Differential dynamic programming

Differential dynamic programming (DDP) is an optimal control algorithm of the trajectory optimization class. The algorithm was introduced in 1966 by Mayne and subsequently analysed in Jacobson and Mayne's eponymous book. The algorithm uses locally-quadratic models of the dynamics and cost functions, and displays quadratic convergence. It is closely related to Pantoja's step-wise Newton's method.
Finite-horizon discrete-time problems.
The dynamics
describe the evolution of the state formula_1 given the control formula_2 from time formula_3 to time formula_4. The "total cost" formula_5 is the sum of running costs formula_6 and final cost formula_7, incurred when starting from state formula_8 and applying the control sequence formula_9 until the horizon is reached:
where formula_11, and the formula_12 for formula_13 are given by . The solution of the optimal control problem is the minimizing control sequence
formula_14
"Trajectory optimization" means finding formula_15 for a particular formula_16, rather than for all possible initial states.
Dynamic programming.
Let formula_17 be the partial control sequence formula_18 and define the "cost-to-go" formula_19 as the partial sum of costs from formula_3 to formula_21:
The optimal cost-to-go or "value function" at time formula_3 is the cost-to-go given the minimizing control sequence:
Setting formula_25, the dynamic programming principle reduces the minimization over an entire sequence of controls to a sequence of minimizations over a single control, proceeding backwards in time:
This is the Bellman equation.
Differential dynamic programming.
DDP proceeds by iteratively performing a backward pass on the nominal trajectory to generate a new control sequence, and then a forward-pass to compute and evaluate a new nominal trajectory. We begin with the backward pass. If
is the argument of the formula_27 operator in , let formula_28 be the variation of this quantity around the formula_3-th formula_30 pair:
and expand to second order
 & Q_{\mathbf{x}\mathbf{u}}\\
1\\
\delta\mathbf{x}\\
The formula_28 notation used here is a variant of the notation of Morimoto where subscripts denote differentiation in denominator layout.
Dropping the index formula_3 for readability, primes denoting the next time-step formula_34, the expansion coefficients are
The last terms in the last three equations denote contraction of a vector with a tensor. Minimizing the quadratic approximation with respect to formula_36 we have
^* = \operatorname{argmin}\limits_{\delta \mathbf{u}}Q(\delta \mathbf{x},\delta
\mathbf{u})=-Q_{\mathbf{u}\mathbf{u}}^{-1}(Q_\mathbf{u}+Q_{\mathbf{u}\mathbf{x}}\delta \mathbf{x}),
giving an open-loop term formula_37 and a feedback gain term formula_38. Plugging the result back into , we now have a quadratic model of the value at time formula_3:
Recursively computing the local quadratic models of formula_41 and the control modifications formula_42, from formula_43 down to formula_44, constitutes the backward pass. As above, the Value is initialized with formula_25. Once the backward pass is completed, a forward pass computes a new trajectory:
The backward passes and forward passes are iterated until convergence.
Regularization and line-search.
Differential dynamic programming is a second-order algorithm like Newton's method. It therefore takes large steps toward the minimum and often requires regularization and/or line-search to achieve convergence
. Regularization in the DDP context means ensuring that the formula_47 matrix in is positive definite. Line-search in DDP amounts to scaling the open-loop control modification formula_48 by some formula_49.
Monte Carlo version.
Sampled differential dynamic programming (SaDDP) is a Monte Carlo variant of differential dynamic programming. It is based on treating the quadratic cost of differential dynamic programming as the energy of a Boltzmann distribution. This way the quantities of DDP can be matched to the statistics of a multidimensional normal distribution. The statistics can be recomputed from sampled trajectories without differentiation.
Sampled differential dynamic programming has been extended to Path Integral Policy Improvement with Differential Dynamic Programming. This creates a link between differential dynamic programming and path integral control, which is a framework of stochastic optimal control.
Constrained problems.
Interior Point Differential dynamic programming (IPDDP) is an interior-point method generalization of DDP that can address the optimal control problem with nonlinear state and input constraints.