Remaining Tool Life Prediction: Comparison
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Please note this is a comparison between Version 3 by Fanny Huang and Version 2 by Joachim Friedhoff.

The increasing demand for customized products is a core driver of novel automation concepts in Industry 4.0. For the case of machining complex free-form workpieces, e.g., in die making and mold making, individualized manufacturing is already the industrial practice. The varying process conditions and demanding machining processes lead to a high relevance of machining domain experts and a low degree of manufacturing flow automation. In order to increase the degree of automation, online process monitoring and the prediction of the quality-related remaining cutting tool life is indispensable.

  • remaining tool life prediction
  • individualized production

1. Introduction

With the advancement of Industry 4.0, the demand for highly customized products is increasing. A growing proportion of single-part and small-batch production manifests this in the manufacturing industry. The resulting frequent machine and process reconfigurations increase the susceptibility to process errors, which is unacceptable for applications requiring a high product quality and reliability. Die making and mold making combine high product quality requirements and a dominant share of individualized production [1,2][1][2]. The core technology in die making and mold making is machining, particularly multi-axis milling, for manufacturing complex free-form workpieces [3]. The decisive quality parameters are their dimensional accuracy and surface roughness, which are significantly influenced by the wear of the cutting tools [4,5,6][4][5][6]. Even minor deviations from the specification can lead to defective end products, e.g., in injection molding or die-casting processes. Therefore, monitoring the machining processes and tool wear is essential to avoid scrap and rework [7].
In recent years, remaining tool life prediction based on sensor-driven process monitoring has been increasingly investigated in this context [8]. The remaining tool life prediction enables a joint estimation of the current tool condition based on the monitoring data and the duration until quality-related specifications of the process are violated. Thus, the quality and productivity of machining processes become controllable and the job-shop scheduling is simplified due to increased plannability [9]. Simultaneously, process-integrated sensors allow for reductions in time-consuming measurements using manufacturing metrology.
However, single-part and small-batch production conditions have made the development of remaining tool life prediction methodologies considerably difficult [10]. In particular, frequent changes in the workpiece geometries and process parameters do not allow for the direct inference of the tool condition from the sensor data due to a lack of comparability. Furthermore, the prediction of the remaining tool life is affected by the increased uncertainty regarding future process conditions. Therefore, previous approaches mainly focus on series production under constant process conditions, implying that the used prediction models are not adaptable.

2. Remaining Tool Life Prediction

The state-of-the-art methods in sensor-driven remaining tool life prediction comprise two main approaches: direct [11] and criterion-based [12]. Direct methods use models generating a temporal output from the process-describing sensor data. The tool state and the decision threshold regarding the end of the tool life are, therefore, only implicitly part of the model and cannot be extracted or set separately. Criterion-based methods integrate an intermediate step via a tool life criterion to indicate the tool condition. The subsequent extrapolation of the tool life criterion allows for the setting of arbitrary decision thresholds. In addition, the tool condition is directly available for further applications, e.g., for integration into a simulation. Due to its comprehensive significance for the process quality, the tool condition and the end of tool life are usually determined based on tool wear [13]. Alternatively, quality parameters, such as the workpiece surface roughness, can be used as tool life criteria.
The data basis for the remaining tool life models is generated using the state-of-the-art sensor types in process monitoring [8]. The monitoring variables are the cutting force [14], vibration [15], drive current and power [16] or machine tool controller signals [17]. Since the sensor data provide the input for the predictions, either purely data-based [18] or hybrid physics- and data-based models [19] are used. In the area of data-based remaining tool life prediction, machine learning (ML) models and particularly neural network architectures, like convolutional neural networks (CNNs) [20], temporal convolutional networks (TCNs) [21] or long short-term memory (LSTM) networks [22], are current research topics due to their high adaptivity, accuracy and suitability for temporal predictions.
Their underlying production scenarios and datasets are the most significant distinguishing characteristics of the prediction models. A remaining tool life dataset comprises the sensor and target data over the life cycle of multiple tools. Possible variants of datasets are shown in Figure 1, depending on the respective degree of process condition variations during single and multiple tool life cycles. Process conditions refer to influencing factors, i.e., the tool shape and material; workpiece shape and material; cutting parameters; machine tool design and its condition or tool path; and process kinematics. While the dataset variants I, e.g., Ref. [23], and II, e.g., Ref. [24], mainly represent series production, the combinations III [25] and IV describe the individualized production scenario.
Figure 1. Possible dataset variants in the area of remaining tool life prediction depending on the degree of process condition variations during single and multiple tool life cycles.
Previous work on sensor-driven remaining tool life prediction mainly investigates series production scenarios [8]. Individualized production, i.e., the variation in process conditions during a tool life cycle, is hardly considered. A single approach analyzes varying cutting parameters during the tool life cycle [25]. However, the same workpiece is manufactured repeatedly. In [9], a methodology for small-batch production is developed using a dataset containing several identical cutting operation sequences. The approach of Matsumura et al. [26], while considering varying workpieces during the tool life cycle, requires direct wear measurements and is thus outside the scope of sensor-driven predictions.
Overall, sensor-driven remaining tool life prediction has not yet been analyzed under variable cutting parameters and workpiece geometries during single and multiple tool life cycles. Datasets according to variant III, like [25], do not include the implied degree of process condition variation, and datasets according to variant IV do not exist. Therefore, it is still unknown whether the feature extraction methodologies for sensor data under fixed process conditions are applicable. Furthermore, previous remaining tool life prediction models do not mitigate the uncertainty due to variable future process conditions.
Several papers investigate the use of AutoML methods to make the benefits of ML-based models even easier to apply to tool condition monitoring [6,27,28,29][6][27][28][29]. AutoML leverages the autonomous adaptation of models to changing process conditions, especially in individualized production. However, with increased autonomy in the generation of models, their validity must be ensured. Although the first approaches to explaining ML models in the context of machining process monitoring exist [30,31,32,33][30][31][32][33], methods combining AutoML and model explainability are missing so far.

References

  1. Boos, W.; Kelzenberg, C.; Prümmer, M.; Goertz, D.; Boshof, J.; Horstkotte, R.; Ochel, T.; Lürken, C. Tooling in Germany 2020; WZL of RWTH Aachen, Fraunhofer IPT: Aachen, Germany, 2020.
  2. Boos, W.; Arntz, K.; Johannsen, L.; Prümmer, M.; Horstkotte, R.; Ganser, P.; Venek, T.; Gerretz, V. Erfolgreich Fräsen im Werkzeugbau; Fraunhofer IPT, WBA Aachener Werkzeubau Akademie: Aachen, Germany, 2018.
  3. Norberto López de Lacalle, L.; Lamikiz, A. Sculptured Surface Machining. In Machining—Fundamentals and Recent Advances; Davim, J.P., Ed.; Springer: London, UK, 2008; pp. 225–249.
  4. Möhring, H.-C.; Nguyen, Q.P.; Kuhlmann, A.; Lerez, C.; Nguyen, L.T.; Misch, S. Intelligent Tools for Predictive Process Control. Procedia CIRP 2016, 57, 539–544.
  5. Möhring, H.-C.; Eschelbacher, S.; Georgi, P. Fundamental investigation on the correlation between surface properties and acceleration data from a sensor integrated milling tool. Procedia Manuf. 2020, 52, 79–84.
  6. Denkena, B.; Dittrich, M.-A.; Lindauer, M.; Mainka, J.; Stürenburg, L. Using AutoML to Optimize Shape Error Prediction in Milling Processes. In Proceedings of the 2020 Machining Innovations Conference (2020), Online, 1–2 December 2020.
  7. Mohamed, A.; Hassan, M.; M’Saoubi, R.; Attia, H. Tool Condition Monitoring for High-Performance Machining Systems-A Review. Sensors 2022, 22, 2206.
  8. Sayyad, S.; Kumar, S.; Bongale, A.; Kamat, P.; Patil, S.; Kotecha, K. Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions. Int. J. Adv. Manuf. Technol. 2021, 115, 2683–2709.
  9. Denkena, B.; Krüger, M.; Schmidt, J. Condition-based tool management for small batch production. Int. J. Adv. Manuf. Technol. 2014, 74, 471–480.
  10. Arntz, C.; Brandstätter, T.C.; Dorißen, J.; Frye, M.; Krauß, J.; Krebs, L.; Holst, C.; Horstkotte, R.; Mende, H.; Schiller, S.; et al. Künstliche Intelligenz in der Einzel- und Kleinserienfertigung; Fraunhofer IPT: Aachen, Germany, 2021.
  11. Wang, W.; Wang, B.; Li, N.; Lei, Y.; Yan, T. Remaining Useful Life Prediction Based on Multi-channel Attention Bidirectional Long Short-term Memory Network. In Proceedings of the 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control (2021), Weihai, China, 13–15 August 2021; pp. 7–12.
  12. Sun, H.; Zhang, J.; Mo, R.; Zhang, X. In-process tool condition forecasting based on a deep learning method. Robot.-Comput.-Integr. Manuf. 2020, 64, 101924.
  13. Astakhov, V.P.; Davim, J.P. Tools (Geometry and Material) and Tool Wear. In Machining—Fundamentals and Recent Advances; Davim, J.P., Ed.; Springer: London, UK, 2008; pp. 29–59.
  14. Mebrahitom, A.G.; Seow, X.Y.; Azmir, A.; Tamiru, A.L. Remaining Tool Life Prediction Based on Force Sensors Signal During End Milling of Stavax ESR Steel. In Proceedings of the International Mechanical Engineering Congress and Exposition (2017), Tampa, FL, USA, 3–9 November 2017; pp. 1–7.
  15. Zhang, J.; Zeng, Y.; Starly, B. Recurrent Neural Networks with Long Term Temporal Dependencies in Machine Tool Wear Diagnosis and Prognosis. SN Appl. Sci. 2021, 3, 442.
  16. Drouillet, C.; Karandikar, J.; Nath, C.; Journeaux, A.-C.; El Mansori, M.; Kurfess, T. Tool Life Predictions in Milling using Spindle Power with the Neural Network Technique. J. Manuf. Process. 2016, 22, 161–168.
  17. An, Q.; Tao, Z.; Xu, X.; El Mansori, M.; Chen, M. A Data-driven Model for Milling Tool Remaining Useful Life Prediction with Convolutional and Stacked LSTM Network. Measurement 2020, 154, 107461.
  18. Nasir, V.; Sassani, F. A Review on Deep Learning in Machining and Tool Monitoring: Methods, Opportunities, and Challenges. IEEE Access 2021, 9, 110255–110286.
  19. Li, Y.; Xiang, Y.; Pan, B.; Shi, L. A Hybrid Remaining Useful Life Prediction Method for Cutting Tool considering the Wear State. Int. J. Adv. Manuf. Technol. 2022, 121, 3583–3596.
  20. Guo, L.; Yu, Y.; Gao, H.; Feng, T.; Liu, Y. Online Remaining Useful Life Prediction of Milling Cutters Based on Multisource Data and Feature Learning. IEEE Trans. Ind. Inform. 2022, 18, 5199–5208.
  21. Jia, W.; Wang, W.; Li, Z.; Li, H. Prediction of Tool Wear in Sculpture Surface by a new Fusion Method of Temporal Convolutional Network and Self-Attention. Int. J. Adv. Manuf. Technol. 2022, 121, 2565–2583.
  22. Liu, Y.; Hu, X.; Jin, J. Remaining Useful Life Prediction of Cutting Tools based on Deep Adversarial Transfer Learning. In Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition (2019), Beijing China, 23–25 October 2019; pp. 434–439.
  23. Li, X.; Lim, B.S.; Zhou, J.H.; Huang, S.; Phua, S.J.; Shaw, K.C.; Er, M.J. Fuzzy Neural Network Modelling for Tool Wear Estimation in Dry Milling Operation. In Proceedings of the Annual Conference of the PHM Society (2009), San Diego, CA, USA, 27 September–1 October 2009.
  24. Goebel, K. Management of Uncertainty for Sensor Validation, Sensor Fusion and Diagnosis in Sensor Driven Mechanical Systems Using Soft Computing Techniques; UC Berkeley: Berkeley, CA, USA, 1996.
  25. Zhou, J.-T.; Zhao, X.; Gao, J. Tool Remaining Useful Life Prediction Method based on LSTM under Variable Working Conditions. Int. J. Adv. Manuf. Technol. 2019, 104, 4715–4726.
  26. Matsumura, R.; Nishida, I.; Shirase, K. Tool Life Prediction in End Milling using a Combination of Machining Simulation and Tool Wear Progress Data. J. Adv. Mech. Des. Syst. Manuf. 2023, 17, JAMDSM0025.
  27. Zegarra, F.C.; Vargas-Machuca, J.; Coronado, A.M. Tool Wear and Remaining Useful Life (RUL) Prediction based on Reduced Feature Set and Bayesian Hyperparameter Optimization. Prod. Eng. 2022, 16, 465–480.
  28. Lutz, B.; Reisch, R.; Kißkalt, D.; Avci, B.; Regulin, D.; Knoll, A.; Franke, J. Benchmark of Automated Machine Learning with State-of-the-Art Image Segmentation Algorithms for Tool Condition Monitoring. Procedia Manuf. 2020, 51, 215–221.
  29. Kißkalt, D.; Mayr, A.; Lutz, B.; Rögele, A.; Franke, J. Streamlining the development of data-driven industrial applications by automated machine learning. Procedia CIRP 2020, 93, 401–406.
  30. Schmetz, A.; Vahl, C.; Zhen, Z.; Reibert, D.; Mayer, S.; Zontar, D.; Garcke, J.; Brecher, C. Decision Support by Interpretable Machine Learning in Acoustic Emission Based Cutting Tool Wear Prediction. In Proceedings of the 2021 IEEE International Conference on Industrial Engineering and Engineering Management (2021), Singapore, 13–16 December 2021; pp. 629–633.
  31. Sotubadi, S.V.; Liu, R.; Nguyen, V. Explainable AI for Tool Wear Prediction in Turning. In Proceedings of the ASME 2023 18th International Manufacturing Science and Engineering Conference (2023), New Brunswick, NJ, USA, 12–16 June 2023.
  32. Li, Y.; Wang, J.; Huang, Z.; Gao, R.X. Physics-informed Meta Learning for Machining Tool Wear Prediction. J. Manuf. Syst. 2022, 62, 17–27.
  33. Wang, J.; Li, Y.; Zhao, R.; Gao, R.X. Physics Guided Neural Network for Machining Tool Wear Prediction. J. Manuf. Syst. 2020, 57, 298–310.
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