High-accurate Tool Wear Prediction With Convolutional Neural Network

We develop a model which can monitor single-machining tool wear, also known as TCM, from 7-dimensional signals better than any other traditional method.

Key to exceeding expert and traditional performance is a deep convolutional network which can map a sequence of signal to tool wear label along with PHM 2010 dataset similar with previous dataset of its kind.

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We train a 32-layer convolutional neural network (CNN) to predict tool wear in 7-dimensional sensor signals

The network takes as input a time-series of secondary-sampled sensor signal, and outputs a sequence of responding tool wear. The original signal is sampled at 50KHz. We arrive at an architecture which is 32 layers of convolution followed by a fully connected layer.

To make the optimization of such a deep model tractable, we use residual connections and batch-normalization. The depth increases both the non-linearity of the computation as well as the size of the context window for each regression task.

Each channel of signal is corresponding to:
1: Force (N) in X dimension
2: Force (N) in Y dimension
3: Force (N) in Z dimension
4: Vibration (g) in X dimension
5: Vibration (g) in Y dimension
6: Vibration (g) in Z dimension
7: AE-RMS (V)

The data is collected a dataset of 3 tools under the same machining circumstance

The PHM data is sampled at a frequency of 50000Hz and have 8GB size. In this machining condition, the spindle speed of the cutter was 10400 RPM; feed rate was 1555 mm/min; Y depth of cut (radial) was 0.125 mm; Z depth of cut (axial) was 0.2 mm.

You may find more details about this experiment at HERE

The model outperforms all of competitors in PHM 2010 Contest

Although we have no data about test data, according to the score on known dataset, loss of this model is ~1000 times less than the 1st prize winner, which shows a great accuracy of CNN.

Loss compared with all of competitors

The smaller, the better.

alias	score	catagory
kidozh	393	student
gtl-phm	55000	professional
UNO-PHM	57000	professional
PathFinder	64000	student
Projector	120000	professional
Shirazi	150000	student
Maverick	250000	student
westlake	320000	professional
DCO	1300000	professional
COSMI	3700000	student
scr	5200000	professional
Fall2010NYTX	5700000	student
myc	210000000	student
Tardec	2300000000	professional
hcbdy	16000000000	student
ITRI	18000000000	professional
sniknam	19000000000	professional
EchoLake	33000000000	professional
Luckbox	110000000000	student
phm415	2200000000000	professional
Meliksah	2900000000000	professional
gtisyeprog	4100000000000	student
rsm	45000000000000	professional
EECSUTK	nan	professional
Binatix	nan	professional
Rezvish	nan	student
Heracles	nan	professional
Kitae	nan	student

Given that more than 900 run are recorded in PHM data, high-accuracy prediction by CNN can monitor tool wear and decrease the need of prior experience. Furthermore, we hope that this technology dealt with sensor signal enables more widespread use of deep learning in places where access to experts is difficult.