Datasets:

AI4Manufacturing
/

PHM2010

	---
	license: cc-by-4.0
	task_categories:
	- image-classification
	pretty_name: PHM 2010 Milling Tool Wear — Angle-Folded Cutting Force (signal→VLM, Category C)
	tags:
	- tool-condition-monitoring
	- milling
	- cutting-force
	- signal-to-image
	- compute-then-check
	---
	# PHM 2010 milling — is this cutter still fit to cut? (reasoning track)

	Part of the AI4Manufacturing FORGE corpus (Category C, task T-C1), and the corpus's first machining dataset. Each record is one cut's axial cutting force folded onto a single spindle revolution: the horizontal axis is rotation angle over a full 360 degrees, the vertical axis is newtons. Three flutes cut once each per turn, so a sharp cutter shows three modest humps and a worn one shows the same three, swinging much further.

	That representation is the one a stated rule can be applied to, which is why it carries the reasoning track. Only the axial force folds into a countable per-flute structure: on the same cutter and the same section, vibration's hump count moves 9 -> 6 and drifts in phase, and acoustic emission has no per-flute structure at all — only a level that rises two orders of magnitude. The other channels ship as `PHM2010-perception`.

	The query states the limit, and that is deliberate. A cutter has no characteristic fault frequency to look up: tooth passing is present on a brand-new tool, because cutting is the periodic impact, and wear raises existing lines rather than creating one. So the question is not which fault but has this crossed the limit — and the limit is a shop-floor convention, stated rather than derived. What the image supplies is the number to compare against it. `reasoning` is empty here; the `PHM2010-annotated` sibling fills it.

	Records: 858 (splits {'train': 597, 'test': 261}); labels {'serviceable': 646, 'worn': 212}; by cutter {'c1': 293, 'c4': 304, 'c6': 261}.

	What was filtered. 87 of 945 cuts are held back from this track: those where the stated rule, applied to the image, does not land on the gold label. A record where it does not would teach a model to compute correctly and then ignore its own arithmetic. The filter uses gold, so this track is easier than the raw dataset, and what it removes is the boundary — cuts sitting near 74.5 N. The four `PHM2010-perception` configs are not filtered and carry the whole 945-cut population.

	## Rig
	\| \| \|
	\|---\|---\|
	\| Process \| dry high-speed milling of stainless steel HRC52, down-milling \|
	\| Cutter \| 3-flute tungsten-carbide ball-nose end mill, 6 mm diameter \|
	\| Cut \| 0.2 mm axial depth, 0.125 mm radial depth, 10400 rpm, 1555 mm/min feed \|
	\| Sensors \| Kistler 3-axis dynamometer + 3-axis accelerometer + acoustic-emission RMS \|
	\| Sampling \| 50 kHz, 7 channels; a cut is 2.5-5 s \|
	\| Population \| 3 instrumented cutters x 315 cuts = 945 records \|
	\| Wear label \| flank wear VB per flute, read offline on a LEICA MZ12 microscope \|

	## Schema (7-field unified record)
	\| field \| here \|
	\|---\|---\|
	\| `query` \| the paraphrased question; for the reasoning track it also states the decision rule \|
	\| `image` \| the rendered PNG, bytes embedded \|
	\| `annot` \| `worn` or `serviceable` \|
	\| `reasoning` \| `None` — filled by the annotation pass, not here \|
	\| `cate` / `task` \| `C` / `T-C1` (signal fault classification) \|
	\| `metadata` \| JSON: cutter, cut index, measured period and rpm, wear in um (all three flutes), the measured peak-to-valley and the threshold it was compared with, split \|

	## Splits
	`train` / `test` = by cutter, never by cut. See caveats 1-2 for why a per-cut split is not usable on this dataset.

	## Provenance & calibration
	Produced by `forge_agent/examples/phm2010_milling/convert.py` (forge_agent `e8726e30f2`) and packaged by `forge_model/PHM2010/convert_phm2010.py` (forge_model `7985ec9066`).

	Gold. `worn` = mean flank wear >= 125.0 um. Chosen by us, from label-only properties; see caveat 3.

	The measurement in the query. Peak-to-valley of the angle-folded axial force. The threshold, 74.5 N, is fitted on the TRAIN cutters (c1, c4) only and applied blind to c6 — the same discipline as this corpus's other calibrated gates. Fitting it across all three cutters instead reaches a flattering number by fitting on the test set; both are recorded in `provenance.json`.

	\| cutter \| cuts \| rule agrees with gold \| majority baseline \| missed worn \| false worn \| \|
	\|---\|---\|---\|---\|---\|---\|---\|
	\| `c1` \| 315 \| 93% \| 71% \| 22 \| 0 \| train \|
	\| `c4` \| 315 \| 97% \| 78% \| 0 \| 11 \| train \|
	\| `c6` \| 315 \| 83% \| 59% \| 54 \| 0 \| held out \|

	The threshold does not transfer cleanly between cutters — per leave-one-out fold it lands at different values, because the three cutters' healthy baselines differ about fourfold. That is a property of the rig, and it is why the rule is absolute newtons rather than a ratio to a cutter's own first cut: at equal wear the ratios spread 3.5x while the absolute forces spread 1.5x.

	Query pool. Domain `cutting_force`, angle_profile 30, spectrogram 30, scalogram 30, waveform 30, reshaped 30 paraphrases.

	## Caveats
	1. The wear labels are interpolated, not measured per cut. The archive ships 2835 values and the source paper says each cut was read under a microscope. The values do not support that: none ever decreases, 231 repeat the previous cut exactly, and two of the nine flutes are reproduced to 0.0017 um by piecewise-linear interpolation through 15 cut indices. The test that settles it is not monotonicity — NASA Ames' genuinely per-cut-measured milling set is monotone too, at a higher rank correlation — it is predictability: hide a value and predict it from its neighbours, which costs 0.0017 um here and 12.5 um there, a factor of 7494. A microscope does not read to a thousandth of a micrometre.
	2. Which is why the split is by cutter and the cut index never appears in an image. On a random per-cut split a nearest-neighbour model that reads no sensor at all matches the published deep models. Nothing rendered here encodes how far into a cutter's life a record sits.
	3. The wear threshold is ours. The archive contains no threshold, no pass/fail column and no documentation — it was a regression challenge scored in micrometres. ISO 8688-2's 300 um end-milling criterion is never reached (the run stopped at 315 cuts; worst flute 234.7 um). We chose the limit from label-only properties, not from any detector's accuracy.
	4. Three cutters is at most three folds. One is held out. This says nothing about transfer to another machine, another workpiece or another cutter geometry.
	5. The task is serviceability, not wear regression. Predicting the micrometre value on this dataset trains the interpolation formula back.

	## Source & license
	PHM Society 2010 Data Challenge, milling tool wear. Rig and protocol: Li X, Lim B S, Zhou J H et al., Mechanical Systems and Signal Processing 23(8), 2009, `doi:10.1016/j.ymssp.2009.06.008`. The official distribution host has lapsed; the archive used here is a byte-identical mirror, verified against four independent copies of the label files.

	---
	license: cc-by-4.0
	task_categories:
	- image-classification
	pretty_name: PHM 2010 Milling Tool Wear — Angle-Folded Cutting Force (signal→VLM, Category C)
	tags:
	- tool-condition-monitoring
	- milling
	- cutting-force
	- signal-to-image
	- compute-then-check
	---
	# PHM 2010 milling — is this cutter still fit to cut? (reasoning track)

	Part of the AI4Manufacturing FORGE corpus (Category C, task T-C1), and the corpus's first machining dataset. Each record is one cut's axial cutting force folded onto a single spindle revolution: the horizontal axis is rotation angle over a full 360 degrees, the vertical axis is newtons. Three flutes cut once each per turn, so a sharp cutter shows three modest humps and a worn one shows the same three, swinging much further.

	That representation is the one a stated rule can be applied to, which is why it carries the reasoning track. Only the axial force folds into a countable per-flute structure: on the same cutter and the same section, vibration's hump count moves 9 -> 6 and drifts in phase, and acoustic emission has no per-flute structure at all — only a level that rises two orders of magnitude. The other channels ship as `PHM2010-perception`.

	The query states the limit, and that is deliberate. A cutter has no characteristic fault frequency to look up: tooth passing is present on a brand-new tool, because cutting is the periodic impact, and wear raises existing lines rather than creating one. So the question is not which fault but has this crossed the limit — and the limit is a shop-floor convention, stated rather than derived. What the image supplies is the number to compare against it. `reasoning` is empty here; the `PHM2010-annotated` sibling fills it.

	Records: 858 (splits {'train': 597, 'test': 261}); labels {'serviceable': 646, 'worn': 212}; by cutter {'c1': 293, 'c4': 304, 'c6': 261}.

	What was filtered. 87 of 945 cuts are held back from this track: those where the stated rule, applied to the image, does not land on the gold label. A record where it does not would teach a model to compute correctly and then ignore its own arithmetic. The filter uses gold, so this track is easier than the raw dataset, and what it removes is the boundary — cuts sitting near 74.5 N. The four `PHM2010-perception` configs are not filtered and carry the whole 945-cut population.

	## Rig
	\| \| \|
	\|---\|---\|
	\| Process \| dry high-speed milling of stainless steel HRC52, down-milling \|
	\| Cutter \| 3-flute tungsten-carbide ball-nose end mill, 6 mm diameter \|
	\| Cut \| 0.2 mm axial depth, 0.125 mm radial depth, 10400 rpm, 1555 mm/min feed \|
	\| Sensors \| Kistler 3-axis dynamometer + 3-axis accelerometer + acoustic-emission RMS \|
	\| Sampling \| 50 kHz, 7 channels; a cut is 2.5-5 s \|
	\| Population \| 3 instrumented cutters x 315 cuts = 945 records \|
	\| Wear label \| flank wear VB per flute, read offline on a LEICA MZ12 microscope \|

	## Schema (7-field unified record)
	\| field \| here \|
	\|---\|---\|
	\| `query` \| the paraphrased question; for the reasoning track it also states the decision rule \|
	\| `image` \| the rendered PNG, bytes embedded \|
	\| `annot` \| `worn` or `serviceable` \|
	\| `reasoning` \| `None` — filled by the annotation pass, not here \|
	\| `cate` / `task` \| `C` / `T-C1` (signal fault classification) \|
	\| `metadata` \| JSON: cutter, cut index, measured period and rpm, wear in um (all three flutes), the measured peak-to-valley and the threshold it was compared with, split \|

	## Splits
	`train` / `test` = by cutter, never by cut. See caveats 1-2 for why a per-cut split is not usable on this dataset.

	## Provenance & calibration
	Produced by `forge_agent/examples/phm2010_milling/convert.py` (forge_agent `e8726e30f2`) and packaged by `forge_model/PHM2010/convert_phm2010.py` (forge_model `7985ec9066`).

	Gold. `worn` = mean flank wear >= 125.0 um. Chosen by us, from label-only properties; see caveat 3.

	The measurement in the query. Peak-to-valley of the angle-folded axial force. The threshold, 74.5 N, is fitted on the TRAIN cutters (c1, c4) only and applied blind to c6 — the same discipline as this corpus's other calibrated gates. Fitting it across all three cutters instead reaches a flattering number by fitting on the test set; both are recorded in `provenance.json`.

	\| cutter \| cuts \| rule agrees with gold \| majority baseline \| missed worn \| false worn \| \|
	\|---\|---\|---\|---\|---\|---\|---\|
	\| `c1` \| 315 \| 93% \| 71% \| 22 \| 0 \| train \|
	\| `c4` \| 315 \| 97% \| 78% \| 0 \| 11 \| train \|
	\| `c6` \| 315 \| 83% \| 59% \| 54 \| 0 \| held out \|

	The threshold does not transfer cleanly between cutters — per leave-one-out fold it lands at different values, because the three cutters' healthy baselines differ about fourfold. That is a property of the rig, and it is why the rule is absolute newtons rather than a ratio to a cutter's own first cut: at equal wear the ratios spread 3.5x while the absolute forces spread 1.5x.

	Query pool. Domain `cutting_force`, angle_profile 30, spectrogram 30, scalogram 30, waveform 30, reshaped 30 paraphrases.

	## Caveats
	1. The wear labels are interpolated, not measured per cut. The archive ships 2835 values and the source paper says each cut was read under a microscope. The values do not support that: none ever decreases, 231 repeat the previous cut exactly, and two of the nine flutes are reproduced to 0.0017 um by piecewise-linear interpolation through 15 cut indices. The test that settles it is not monotonicity — NASA Ames' genuinely per-cut-measured milling set is monotone too, at a higher rank correlation — it is predictability: hide a value and predict it from its neighbours, which costs 0.0017 um here and 12.5 um there, a factor of 7494. A microscope does not read to a thousandth of a micrometre.
	2. Which is why the split is by cutter and the cut index never appears in an image. On a random per-cut split a nearest-neighbour model that reads no sensor at all matches the published deep models. Nothing rendered here encodes how far into a cutter's life a record sits.
	3. The wear threshold is ours. The archive contains no threshold, no pass/fail column and no documentation — it was a regression challenge scored in micrometres. ISO 8688-2's 300 um end-milling criterion is never reached (the run stopped at 315 cuts; worst flute 234.7 um). We chose the limit from label-only properties, not from any detector's accuracy.
	4. Three cutters is at most three folds. One is held out. This says nothing about transfer to another machine, another workpiece or another cutter geometry.
	5. The task is serviceability, not wear regression. Predicting the micrometre value on this dataset trains the interpolation formula back.

	## Source & license
	PHM Society 2010 Data Challenge, milling tool wear. Rig and protocol: Li X, Lim B S, Zhou J H et al., Mechanical Systems and Signal Processing 23(8), 2009, `doi:10.1016/j.ymssp.2009.06.008`. The official distribution host has lapsed; the archive used here is a byte-identical mirror, verified against four independent copies of the label files.