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Predicting the quality of a machined workpiece with a variational autoencoder approach

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Abstract

In this article, it is shown that a machine learning approach based only on data from sensors (vibration and current consumption) can be used to predict the geometric dimensioning and tolerancing quality measurement values of machined workpieces in an industrial context. First, a methodology based on a variational autoencoder approach is used, and then a metric based on the concept of Euclidean distance and the 2D latent space produced by the variational autoencoder is proposed. The proposed variational autoencoder regression model is shown capable of predicting the quality measurement values, with a mean square error of \(5.2573\times {10}^{-4}\) mm. The proposed measurement system also displays a confidence interval of ± 0.05 mm. Moreover, the resulting 2D latent space is capable of distributing and structuring data based on the quality level and of providing a quick visual support. Compared to the t-SNE method, this latent space displays a better structure. Furthermore, the proposed Euclidean distance metric is correlated to the quality level in both the predicted and observed subsets. This work is also based on an industrial dataset, thus increasing its potential for technological transfer; that in turn allows a better monitoring of the machining process, as well as the prediction of the workpiece quality.

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Notes

  1. Technical specifications are provided at https://www.pcb.com/products?model=356a33.

  2. Technical specifications are provided at https://www.ifm.com/ca/en/product/VSA004.

  3. Technical specifications are provided at https://www.lem.com/en/lf-210ssp5.

References

  • Abellan-Nebot, J. V., & Romero Subirón, F. (2010). A review of machining monitoring systems based on artificial intelligence process models. The International Journal of Advanced Manufacturing Technology, 47(1), 237–257.

    Article  Google Scholar 

  • Ahmad, M. I., Yusof, Y., Daud, M. E., Latiff, K., Kadir, A. Z. A., & Saif, Y. (2020). Machine monitoring system: A decade in review. The International Journal of Advanced Manufacturing Technology, 108, 1–15.

    Google Scholar 

  • Antoni, J. (2009). Cyclostationarity by examples. Mechanical Systems and Signal Processing, 23(4), 987–1036.

    Article  Google Scholar 

  • ASME. (2018). Dimensioning and tolerancing; engineering drawing and related documentation practices. American Society of Mechanical Engineers.

    Google Scholar 

  • Bakker, O. J., Ratchev, S. M., & Popov, A. A. (2015). Towards a condition-monitoring framework for quality assurance in intelligent multistage manufacturing environment. IFAC-PapersOnLine, 48(3), 2089–2094.

    Article  Google Scholar 

  • Bampoula, X., Siaterlis, G., Nikolakis, N., & Alexopoulos, K. (2021). A deep learning model for predictive maintenance in cyber-physical production systems using LSTM autoencoders. Sensors, 21(3), 972.

    Article  Google Scholar 

  • Bao, W., Yue, J., & Rao, Y. (2017). A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLoS ONE, 12(7), e0180944.

    Article  Google Scholar 

  • Baur, M., Albertelli, P., & Monno, M. (2020). A review of prognostics and health management of machine tools. The International Journal of Advanced Manufacturing Technology, 107(5), 2843–2863.

    Article  Google Scholar 

  • Benardos, P. G., & Vosniakos, G. C. (2003). Predicting surface roughness in machining: A review. International Journal of Machine Tools and Manufacture, 43(8), 833–844.

    Article  Google Scholar 

  • Chadha, G. S., Rabbani, A., & Schwung, A. Comparison of semi-supervised deep neural networks for anomaly detection in industrial processes. In 2019 IEEE 17th international conference on industrial informatics (INDIN), Helsinki, Finland, 22–25 July 2019 2019 (Vol. 1, pp. 214–219).

  • Chen, Y., Jin, Y., & Jiri, G. (2018). Predicting tool wear with multi-sensor data using deep belief networks. The International Journal of Advanced Manufacturing Technology, 99(5), 1–10.

    Google Scholar 

  • Cheng, F., He, Q. P., & Zhao, J. (2019). A novel process monitoring approach based on variational recurrent autoencoder. Computers & Chemical Engineering, 129, 106515.

    Article  Google Scholar 

  • Doersch, C. (2016). Tutorial on variational autoencoders. arXiv:1606.05908.

  • Duo, A., Basagoiti, R., Arrazola, P. J., Aperribay, J., & Cuesta, M. (2019). The capacity of statistical features extracted from multiple signals to predict tool wear in the drilling process. The International Journal of Advanced Manufacturing Technology, 102(5), 2133–2146.

    Article  Google Scholar 

  • Elattar, H. M., Elminir, H. K., & Riad, A. (2016). Prognostics: A literature review. Complex & Intelligent Systems, 2(2), 125–154.

    Article  Google Scholar 

  • Gensler, A., Henze, J., Sick, B., & Raabe, N. Deep Learning for solar power forecasting—An approach using AutoEncoder and LSTM Neural Networks. In 2016 IEEE international conference on systems, man, and cybernetics (SMC), Budapest, Hungary, 9–12 October 2016 (pp. 002858–002865).

  • Ghojogh, B., & Crowley, M. (2019). The theory behind overfitting, cross validation, regularization, bagging, and boosting: tutorial. arXiv:1905.12787.

  • Ghosal, A., Nandy, A., Das, A. K., Goswami, S., & Panday, M. A short review on different clustering techniques and their applications. In Emerging technology in modelling and graphics, Singapore, 2020. Advances in intelligent systems and computing (Vol. 937, pp. 69–83): Springer, Singapore.

  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. The MIT Press.

    Google Scholar 

  • Goyal, D., Mongia, C., & Sehgal, S. (2021). Applications of digital signal processing in monitoring machining processes and rotary components: A review. IEEE Sensors Journal, 21(7), 8780–8804.

    Article  Google Scholar 

  • Haidong, S., Hongkai, J., Xingqiu, L., & Shuaipeng, W. (2018). Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine. Knowledge-Based Systems, 140, 1–14.

    Article  Google Scholar 

  • Han, K., Wen, H., Shi, J., Lu, K.-H., Zhang, Y., Fu, D., et al. (2019a). Variational autoencoder: An unsupervised model for encoding and decoding fMRI activity in visual cortex. NeuroImage, 198, 125–136.

    Article  Google Scholar 

  • Han, T., Liu, C., Yang, W., & Jiang, D. (2019b). A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults. Knowledge-Based Systems, 165, 474–487.

    Article  Google Scholar 

  • He, K., Gao, M., & Zhao, Z. (2019). Soft computing techniques for surface roughness prediction in hard turning: A literature review. IEEE Access, 7, 89556–89569.

    Article  Google Scholar 

  • Hemmer, M., Klausen, A., Khang, H. V., Robbersmyr, K. G., & Waag, T. I. (2020). Health indicator for low-speed axial bearings using variational autoencoders. IEEE Access, 8, 35842–35852.

    Article  Google Scholar 

  • Huang, Y., Chen, C., & Huang, C. (2019). Motor fault detection and feature extraction using RNN-based variational autoencoder. IEEE Access, 7, 139086–139096.

    Article  Google Scholar 

  • Irgens, C. A feature based KBS for quality prediction of machined parts and products. In Computer integrated manufacturing, London, 1991 (pp. 385–396). Springer, London.

  • ISO. (2006). Statistical methods: Process performance anc capability statistics for measured qualilty characteristics. Genève, Suisse: International Organization for Standardization.

  • Janssens, O., Walle, R. V. D., Loccufier, M., & Hoecke, S. V. (2017). Deep learning for infrared thermal image based machine health monitoring. IEEE/ASME Transactions on Mechatronics, 23(1), 151–159.

    Article  Google Scholar 

  • Khorasani, A., & Yazdi, M. R. S. (2017). Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation. The International Journal of Advanced Manufacturing Technology, 93(1), 141–151.

    Article  Google Scholar 

  • Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv:1312.6114.

  • Kohler, D., & Weisz, J. -D. (2016). Industrie 4.0 Les défis de la transformation numérique du modèle industriel allemand. France.

  • Kuntoğlu, M., Aslan, A., Pimenov, D. Y., Usca, Ü. A., Salur, E., Gupta, M. K., et al. (2021). A review of indirect tool condition monitoring systems and decision-making methods in turning: Critical analysis and trends. Sensors, 21(1), 108.

    Article  Google Scholar 

  • Laloix, T., Iung, B., Voisin, A., & Romagne, E. (2016). Towards the control of product quality from the process deviation monitoring: Overview and investigation in automotive sector. IFAC-PapersOnLine, 49(28), 79–84.

    Article  Google Scholar 

  • Lamraoui, M., Thomas, M., El Badaoui, M., & Girardin, F. Cyclostationarity analysis of instantaneous angular speeds for monitoring chatter in high speed milling. In IECON 2012—38th annual conference on IEEE industrial electronics society, Montreal, Qc, Canada, 25–28 Oct. 2012 2012 (pp. 3868–3873).

  • Lee, S., Kwak, M., Tsui, K.-L., & Kim, S. B. (2019). Process monitoring using variational autoencoder for high-dimensional nonlinear processes. Engineering Applications of Artificial Intelligence, 83, 13–27.

    Article  Google Scholar 

  • Liang, S. Y., Hecker, R. L., & Landers, R. G. (2004). Machining process monitoring and control: The state-of-the-art. Journal of Manufacturing Science and Engineering, 126(2), 297–310.

    Article  Google Scholar 

  • Liang, X., Liu, Z., & Wang, B. (2019). State-of-the-art of surface integrity induced by tool wear effects in machining process of titanium and nickel alloys: A review. Measurement, 132, 150–181.

    Article  Google Scholar 

  • Liu, E., An, W., Xu, Z., & Zhang, H. (2020). Experimental study of cutting-parameter and tool life reliability optimization in inconel 625 machining based on wear map approach. Journal of Manufacturing Processes, 53, 34–42.

    Article  Google Scholar 

  • Maaten, L. V. D., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9(11), 2579–2605.

    Google Scholar 

  • MacQueen, J. Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, 1967 (Vol. 1, pp. 281–297, Vol. 14). Oakland, CA, USA

  • Mancisidor, R. A., Kampffmeyer, M., Aas, K., & Jenssen, R. (2021). Learning latent representations of bank customers with the variational autoencoder. Expert Systems with Applications, 164, 1140.

    Article  Google Scholar 

  • Martínez-Arellano, G., Terrazas, G., & Ratchev, S. (2019). Tool wear classification using time series imaging and deep learning. The International Journal of Advanced Manufacturing Technology, 104(9), 3647–3662.

    Article  Google Scholar 

  • Ouafi, A. E., & Barka, N. (2014). An ANN based multi-sensor integration approach for in-process monitoring of product quality in turning operations. Journal of Automation and Control Engineering, 2(3), 289–293.

    Article  Google Scholar 

  • Pang, J., Zhang, N., Xiao, Q., Qi, F., & Xue, X. (2021). A new intelligent and data-driven product quality control system of industrial valve manufacturing process in CPS. Computer Communications, 175, 25–34.

    Article  Google Scholar 

  • Papananias, M., McLeay, T. E., Mahfouf, M., & Kadirkamanathan, V. (2019). An intelligent metrology informatics system based on neural networks for multistage manufacturing processes. Procedia CIRP, 82, 444–449.

    Article  Google Scholar 

  • Park, H.-S., & Tran, N.-H. (2014). Development of a smart machining system using self-optimizing control. The International Journal of Advanced Manufacturing Technology, 74(9), 1365–1380.

    Article  Google Scholar 

  • Proteau, A., Tahan, A., & Thomas, M. (2019a). Specific cutting energy: A physical measurement for representing tool wear. The International Journal of Advanced Manufacturing Technology, 103(1), 1–10.

    Google Scholar 

  • Proteau, A., Tahan, A. S., & Thomas, M. (2019b). Toward the quality prognostic of an aircraft engine workpiece in Inconel Alloy 625: Case study and proposed system architecture. In Surveillance, vishno and AVE conferences, Lyon, France, 8 July 2019b (pp. 1–15).

  • Proteau, A., Zemouri, R., Tahan, A., & Thomas, M. (2020). Dimension reduction and 2D-visualization for early change of state detection in a machining process with a variational autoencoder approach. The International Journal of Advanced Manufacturing Technology, 111(11), 3597–3611.

    Article  Google Scholar 

  • Rauch, M., Laguionie, R., Hascoet, J.-Y., & Suh, S.-H. (2012). An advanced STEP-NC controller for intelligent machining processes. Robotics and Computer-Integrated Manufacturing, 28(3), 375–384.

    Article  Google Scholar 

  • Saleem, M. Q., & Mumtaz, S. (2020). Face milling of Inconel 625 via wiper inserts: Evaluation of tool life and workpiece surface integrity. Journal of Manufacturing Processes, 56, 322–336.

    Article  Google Scholar 

  • San Martin, G., López Droguett, E., Meruane, V., & das Chagas Moura, M. (2019). Deep variational auto-encoders: A promising tool for dimensionality reduction and ball bearing elements fault diagnosis. Structural Health Monitoring, 18(4), 1092–1128.

    Article  Google Scholar 

  • Serin, G., Sener, B., Ozbayoglu, A. M., & Unver, H. O. (2020). Review of tool condition monitoring in machining and opportunities for deep learning. The International Journal of Advanced Manufacturing Technology, 109(3), 953–974.

    Article  Google Scholar 

  • Shahid, N., & Ghosh, A. (2019). TrajecNets: Online failure evolution analysis in 2D space. International Journal of Prognostics and Health Management, 10(Special Issue on Deep Learning and Emerging Analytics), 17.

    Google Scholar 

  • Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1), 1929–1958.

    Google Scholar 

  • Tahan, S.-A., & Levesque, S. Exploiting the process capability of profile tolerance according GD&T ASME-Y14.5M. In 2009 international conference on computers & industrial engineering, 6–9 July 2009 (pp. 1285–1290).

  • Takaya, Y. (2013). In-process and on-machine measurement of machining accuracy for process and product quality management: A review. International Journal of Automation Technology, 8(1), 4–19.

    Article  Google Scholar 

  • Thomas, M. (2011). Fiabilité, maintenance prédictive et vibration des machines. Presses de l’Université du Québec.

    Google Scholar 

  • Voisin, A., Laloix, T., Iung, B., & Romagne, E. (2018). Predictive maintenance and part quality control from joint product-process-machine requirements: Application to a machine tool. Procedia Manufacturing, 16, 147–154.

    Article  Google Scholar 

  • Wang, S., Xiang, J., Zhong, Y., & Zhou, Y. (2017). Convolutional neural network-based hidden Markov models for rolling element bearing fault identification. Knowledge-Based Systems, 144, 65–76.

    Article  Google Scholar 

  • Wuest, T., Irgens, C., & Thoben, K.-D. (2014). An approach to monitoring quality in manufacturing using supervised machine learning on product state data. Journal of Intelligent Manufacturing, 25(5), 1167–1180.

    Article  Google Scholar 

  • Xu, F., Yang, F., Fei, Z., Huang, Z., & Tsui, K.-L. (2021). Life prediction of lithium-ion batteries based on stacked denoising autoencoders. Reliability Engineering & System Safety, 208, 107396.

    Article  Google Scholar 

  • Yin, Q., Liu, Z., Wang, B., Song, Q., & Cai, Y. (2020). Recent progress of machinability and surface integrity for mechanical machining Inconel 718: A review. The International Journal of Advanced Manufacturing Technology, 109, 1–31.

    Article  Google Scholar 

  • Ying, X. (2019). An overview of overfitting and its solutions. Journal of Physics: Conference Series, 1168(2), 022022.

    Google Scholar 

  • Yu, W., Kim, I. Y., & Mechefske, C. (2021). Analysis of different RNN autoencoder variants for time series classification and machine prognostics. Mechanical Systems and Signal Processing, 149, 107322.

    Article  Google Scholar 

  • Yu, S., & Príncipe, J. C. (2019). Understanding autoencoders with information theoretic concepts. Neural Networks, 117, 104–123.

    Article  Google Scholar 

  • Yu, W., Kim, I. I. Y., & Mechefske, C. (2019). Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme. Mechanical Systems and Signal Processing, 129, 764–780.

    Article  Google Scholar 

  • Zemouri, R., Lévesque, M., Amyot, N., Hudon, C., Kokoko, O., & Tahan, S. A. (2020). Deep convolutional variational autoencoder as a 2D-visualization tool for partial discharge source classification in hydrogenerators. IEEE Access, 8, 5438–5454.

    Article  Google Scholar 

  • Zhang, Y., Zhang, Y., He, K., Li, D., Xu, X., & Gong, Y. (2021). Intelligent feature recognition for STEP-NC-compliantmanufacturing based on artificial bee colony algorithm and back propagationneural network. Journal of Manufacturing Systems. https://doi.org/10.1016/j.jmsy.2021.01.018.

    Article  Google Scholar 

  • Zhang, Y., Zhu, K., Duan, X., & Li, S. (2021b). Tool wear estimation and life prognostics in milling: Model extension and generalization. Mechanical Systems and Signal Processing, 155, 107617.

    Article  Google Scholar 

  • Zhao, G., Cao, X., Xiao, W., Liu, Q., & Jun, M.B.-G. (2020). STEP-NC feature-oriented high-efficient CNC machining simulation. The International Journal of Advanced Manufacturing Technology, 106(5), 2363–2375.

    Article  Google Scholar 

  • Zhou, Y., & Xue, W. (2018). Review of tool condition monitoring methods in milling processes. The International Journal of Advanced Manufacturing Technology, 96(5), 2509–2523.

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank APN Inc. and their employees for the knowledge and the data shared in this project. We would also like to acknowledge the financial support of the Fonds de Recherche du Québec—Nature et Technologies to this project through Grant #257668. In addition, this research was enabled in part by the support provided by Calcul Québec (www.calculquebec.ca) and Compute Canada (www.computecanada.ca).

Funding

The research leading to these results received funding from the Fonds de Recherche du Québec—Nature et Technologies under Grant Agreement No 257668.

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Authors and Affiliations

  1. Department of Mechanical Engineering, École de Technologie Supérieure, Montreal, QC, H3C 1K3, Canada

    Antoine Proteau, Antoine Tahan, Ryad Zemouri & Marc Thomas

  2. CEDRIC Laboratory of the Conservatoire National des Arts et Métiers (CNAM), HESAM Université, 750141, Paris Cedex 03, France

    Ryad Zemouri

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  1. Antoine Proteau
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Correspondence to Antoine Proteau.

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Proteau, A., Tahan, A., Zemouri, R. et al. Predicting the quality of a machined workpiece with a variational autoencoder approach. J Intell Manuf 34, 719–737 (2023). https://doi.org/10.1007/s10845-021-01822-y

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