Industry 4.0 and the rise of predictive maintenance have led to increased interest in developing efficient and accurate methods for detecting tool wear in manufacturing processes. In this work, we investigate the use of machine learning techniques, specifically Long Short- Term Memory (LSTM) networks, for the detection of tool wear using NASA Milling Data Set. We first preprocess the raw milling data and extract relevant features using signal processing techniques. Then, we train and evaluate the performance of the LSTM network for detecting tool wear in the milling process. Our results demonstrate the effectiveness of the LSTM network for detecting tool wear, achieving high accuracy and recall scores. Additionally, we compare our results to those obtained using traditional statistical process control methods and highlight the advantages of using machine learning techniques for tool wear detection in the context of Industry 4.0 and predictive maintenance. Our work provides a practical example of how machine learning techniques can be applied to manufacturing processes to improve efficiency and reduce downtime.
- Zhaopeng He, Tielin Shi, Jianping Xuan, Milling tool wear prediction using multi-sensor feature fusion based on stacked sparse autoencoders, Measurement, Volume 190, 2022, 110719, ISSN 0263-2241, https://doi.org/10.1016/j.measurement.2022.110719.
- M. Assafo and P. Langendörfer, "A TOPSIS-Assisted Feature Selection Scheme and SOM-Based Anomaly Detection for Milling Tools Under Different Operating Conditions," in IEEE Access, vol. 9, pp. 90011-90028, 2021, doi: 10.1109/ACCESS.2021.3091476.
- Y. Zhou and W. Sun, "Tool Wear Condition Monitoring in Milling Process Based on Current Sensors," in IEEE Access, vol. 8, pp. 95491-95502, 2020, doi: 10.1109/ACCESS.2020.2995586.
- Yuqing Zhou, Bintao Sun, Weifang Sun, A tool condition monitoring method based on two-layer angle kernel extreme learning machine and binary differential evolution for milling, Measurement, Volume 166, 2020, 108186, ISSN 0263-2241, https://doi.org/10.1016/j.measurement.2020.108186.
- Meng, Xiangfei, et al. "Tool wear prediction in milling based on a GSA-BP model with a multisensor fusion method." The
- International Journal of Advanced Manufacturing Technology 114 (2021): 3793-3802.
- Shaban, Yasser, et al. "Optimal replacement times for machining tool during turning titanium metal matrix composites under variable machining conditions." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 231.6 (2017): 924-932.
- Dyl, Tomasz. "The designation degree of tool wear after machining of the surface layer of duplex stainless steel." Materials 14.21 (2021): 6425.
- Hochreiter, Sepp & Schmidhuber, Jürgen. (1997). Long Short-term Memory. Neural computation. 9. 1735-80. 10.1162/neco.19188.8.131.525.
- Malhotra, Pankaj, et al. "Long Short Term Memory Networks for Anomaly Detection in Time Series." ESANN. Vol. 2015. 2015.
- A. Agogino and K. Goebel (2007). BEST Lab, UC Berkeley. "Milling Data Set ", NASA Prognostics Data Repository, NASA Ames Research Center, Moffett Field, CA