Datasets:

saluslab
/

HM-SYNC

@@ -53,7 +53,7 @@ task_ids: []
 ## Dataset Overview
 This dataset contains a collection of observed interactions between humans and an advanced manufacturing machine, specifically a Wire Arc Additive Manufacuturing (WAAM) machine. The motivations for collecting this dataset, the contents of this dataset, and some ideas for how to analyze and use this dataset can be found below.
-Additionally, the paper introducing this dataset is undergoing review for publication to the American Society of Mechanical Engineers(ASME)’s Journal of Mechanical Design (JMD) special issue: “Cultivating Datasets for Engineering Design”. If accepted, the paper will be referenced here.
 ### Motivation for this Dataset
 The engineering design process for any solution or product is essential to ensure quality results and standards. However, this process can be very tedious and require many re-iterations, especially if it involves manufacturing a product. If engineers and designers are designing a product to be manufactured, but are disconnected from the realities of their available manufacturing capabilities, there can be many redesign iterations stemming from this misunderstanding between design specifications and production / supply chain abilities. Design for Manufacturing (DfM) is a style of design that, relying on accurate simulation and modeling of available manufacturing processes,  takes into account the product manufacturing when designing products such that the design reiteration inefficiency is improved. To improve the transparency between manufacturing and design, establishing methods to understand and quantify the various steps in the manufacturing process is crucial. Within this effort, and in manufacturing, one of the most difficult aspects to understand and quantify is the interactions of humans and machinery. While manufacturing is undergoing immense change due to automation technologies and robotics, humans still play a central role in operations, however their behaviors / actions and how it influences the manufacturing process is poorly understood. This dataset attempts to support the understanding of humans in manufacturing by observing realistic interactions between humans and an advanced manufacturing machine.

 ## Dataset Overview
 This dataset contains a collection of observed interactions between humans and an advanced manufacturing machine, specifically a Wire Arc Additive Manufacuturing (WAAM) machine. The motivations for collecting this dataset, the contents of this dataset, and some ideas for how to analyze and use this dataset can be found below.
+Additionally, the paper introducing this dataset is published in the American Society of Mechanical Engineers(ASME)’s Journal of Mechanical Design (JMD) special issue: “HM-SYNC: A Multimodal Dataset of Human Interactions With Advanced Manufacturing Machinery”.
 ### Motivation for this Dataset
 The engineering design process for any solution or product is essential to ensure quality results and standards. However, this process can be very tedious and require many re-iterations, especially if it involves manufacturing a product. If engineers and designers are designing a product to be manufactured, but are disconnected from the realities of their available manufacturing capabilities, there can be many redesign iterations stemming from this misunderstanding between design specifications and production / supply chain abilities. Design for Manufacturing (DfM) is a style of design that, relying on accurate simulation and modeling of available manufacturing processes,  takes into account the product manufacturing when designing products such that the design reiteration inefficiency is improved. To improve the transparency between manufacturing and design, establishing methods to understand and quantify the various steps in the manufacturing process is crucial. Within this effort, and in manufacturing, one of the most difficult aspects to understand and quantify is the interactions of humans and machinery. While manufacturing is undergoing immense change due to automation technologies and robotics, humans still play a central role in operations, however their behaviors / actions and how it influences the manufacturing process is poorly understood. This dataset attempts to support the understanding of humans in manufacturing by observing realistic interactions between humans and an advanced manufacturing machine.