AI4Manufacturing/PHM2010

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.