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|pdfUrl=https://ceur-ws.org/Vol-2491/abstract35.pdf
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==None==
https://ceur-ws.org/Vol-2491/abstract35.pdf
A Machine Learning-Based Approach for
Predicting Tool Wear in Industrial Milling
Processes?
Mathias Van Herreweghe1 , Mathias Verbeke2 ,
Wannes Meert1 , and Tom Jacobs3
1
Dept. of Computer Science, KU Leuven, Leuven, Belgium
2
Data and AI Competence Lab, Sirris, Brussels, Belgium
3
Precision Manufacturing Lab, Sirris, Diepenbeek, Belgium
Keywords: Tool wear prediction · Industrial Milling Processes · Temporal Con-
volutional Network · Gradient Boosting Machine.
1 Introduction
In industrial machining processes, the wear of a tool has a significant influence on
the quality of the produced part. Therefore, predicting wear upfront can result
in significant improvements of machining processes. This paper investigates the
applicability of machine learning approaches for predicting tool wear in indus-
trial milling processes based on real-world sensor data on exerted cutting forces,
acoustic emission and acceleration. This is an extended abstract of the full paper
presented at the ECML/PKDD 2019 Workshop on IoT Stream for Data Driven
Predictive Maintenance [4].
2 Model selection
As the goal of this paper is to test the industrial applicability of machine learning
for tool wear prediction, the methods were selected based on their computation
speed (to enable near-real time prediction) as well as their accuracy, were we put
a threshold error margin of 20 µm in order to be industrially relevant. Gradient
Boosting Machine was selected due to its accuracy in predicting the tool wear
as well as its computation speed for predictions on new input data. As a second
model, Temporal Convolutional Network (TCN) was selected due to its ability
to exploit the temporal properties of the data, which voids the need for manual
feature engineering.
3 Experimental validation
The validation was performed using the PHM 2010 tool wear prediction dataset
as a benchmark, as well as using a proprietary dataset gathered from an indus-
trial milling machine. Each of these datasets is divided into three subsets, of
?
Copyright c 2019 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
2 Van Herreweghe et al.
which each time two subsets were used for training the model and the remaining
one for testing.
The results expressed in terms of mean absolute error (MAE) for both
datasets are shown in Table 1. The rows marked with a star indicate that for this
model the hyperparameters were optimized using grid search. The other models
were trained using the default hyperparameters of the scikit-learn [2] (GBM) and
Keras [3] (TCN) implementations respectively. The results are given in micro-
meter and are rounded to 1 decimal. The results are the average of 3 predictions
with the same parameters.
Table 1. MAE for different models on the benchmark data (C1, C4, C6) and the
industrial data (I1, I2, I3)
Model C1 C4 C6 I1 I2 I3
GBM 10.9 14.4 13.7 17.5 22.3 16.6
GBM* 10.9 14.4 13.7 13.7 21.1 16.6
TCN 9.3 10.9 16.4 14.3 24.1 14.3
TCN* 9.5 10.9 8.5 13.3 20.8 12.8
Overall, the TCN with optimized hyperparameters obtains the best results
for the benchmark dataset. The results of the mean MAE are 2.5 µm worse than
the state-of-the-art models. For these models, however, no additional statistics
regarding the number of runs that were required to obtain these results are
provided. Furthermore, these results need to be interpreted with caution because
at least one of the papers applies randomized cross-validation across the different
(time-dependent) subsets. As pointed out by Bergmeir, Hyndman and Koo [1],
this is an incorrect approach when dealing with time series data. The TCN also
achieves the lowest mean standard deviation of errors per predicted set. This
means that the size of the errors within a prediction does not differ that much
from one another. Also for the industrial dataset, the TCN obtains the best
results for all calculated statistics.
4 Conclusions
In this paper, we investigated the applicability of two machine learning meth-
ods for predicting tool wear in industrial milling processes using sensor data on
exerted cutting forces, acoustic emission and acceleration. To this end, the use
of Gradient Boosting Machines and Temporal Convolutional Networks was vali-
dated on both a benchmark dataset as well as on a real-world industrial dataset.
The results show that both methods are able to predict the tool wear within an
industrially-relevant error margin of 20 µm in an acceptable computation time.
Acknowledgements This work was partially supported by Flanders Innovation
& Entrepreneurship (VLAIO) through the SBO project HYMOP (150033) and
by the Brussels-Capital Region - Innoviris through the TeamUp ROADMAP
Tool Wear Prediction in Industrial Milling Processes 3
project. Part of the computational resources and services used in this work were
provided by the VSC (Flemish Supercomputer Center), funded by the Research
Foundation - Flanders (FWO) and the Flemish Government - department EWI.
References
1. Bergmeir, C., Hyndman, R.J., Koo, B.: A note on the validity of cross-validation for
evaluating autoregressive time series prediction. Computational Statistics & Data
Analysis 120(C), 70–83 (Apr 2018). https://doi.org/10.1016/j.csda.2017.11.003
2. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O.,
Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A.,
Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine
learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011)
3. Rémy, P.: Keras TCN (2018), “https://github.com/philipperemy/keras-tcn”, [On-
line; May 26, 2019]
4. Van Herreweghe, M., Verbeke, M., Meert, W., Jacobs, T.: A Machine Learning-
Based Approach for Predicting Tool Wear in Industrial Milling Processes. In:
Ribeiro, R., Bifet, A., Gama, J., Holst, A., Pashami, S. (eds.) ECML/PKDD 2019
Workshop on IoT Stream for Data Driven Predictive Maintenance (2019)