Abstract
In laser cutting processes, cutting failure is one of the most common causes of faulty productions. Monitoring cutting failure events is extremely complex, as failures might be initiated by several factors, the most prominent probably being the high production speeds required by modern standards. The present work aims at creating and deploying a classifier able to assess the status of a production cutting quality in a real-time fashion. To this aim, multiple datasets were collected in different environmental conditions and with different sensors. Model inputs include photo-sensors and production parameters. At first, different algorithms were tested and rated by prediction ability. Second, the selected algorithm was deployed on a GPU embedded system and added to the current machine configuration. The final system can receive the input data from the sensors, perform the inference, and send back the results to the computer numerical control. The data management is based on a client-server architecture. The selected algorithm and hardware showed good performances despite multiple changes in the environmental conditions (domain adaptation ability) both in terms of prediction ability (accuracy) and computational times.
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References
Alippi, C., Bono, V., Piuri, V., Scotti, F.: Toward real-time quality analysis measurement of metal laser cutting. In: 2002 IEEE International Symposium on Virtual and Intelligent Measurement Systems (IEEE Cat. No. 02EX545), pp. 39–44. IEEE (2002)
Anicic, O., Jović, S., Skrijelj, H., Nedić, B.: Prediction of laser cutting heat affected zone by extreme learning machine. Opt. Lasers Eng. 88, 1–4 (2017)
Bianco, S., Cadene, R., Celona, L., Napoletano, P.: Benchmark analysis of representative deep neural network architectures. IEEE Access 6, 64270–64277 (2018)
Carlucci, F.M., Porzi, L., Caputo, B., Ricci, E., Bulo, S.R.: Autodial: automatic domain alignment layers. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 5077–5085. IEEE (2017)
Das, S.K., Das, S.P., Dey, N., Hassanien, A.-E. (eds.): Machine Learning Algorithms for Industrial Applications. SCI, vol. 907. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-50641-4
Dong, J., Cong, Y., Sun, G., Zhong, B., Xu, X.: What can be transferred: unsupervised domain adaptation for endoscopic lesions segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4023–4032 (2020)
Franceschetti, L., Pacher, M., Tanelli, M., Strada, S.C., Previtali, B., Savaresi, S.M.: Dross attachment estimation in the laser-cutting process via convolutional neural networks (CNN). In: 2020 28th Mediterranean Conference on Control and Automation (MED), pp. 850–855. IEEE (2020)
Glorot, X., Bordes, A., Bengio, Y.: Domain adaptation for large-scale sentiment classification: a deep learning approach. In: ICML (2011)
Halm, U., Arntz-Schroeder, D., Gillner, A., Schulz, W.: Towards online-prediction of quality features in laser fusion cutting using neural networks. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) IntelliSys 2020. AISC, vol. 1250, pp. 346–359. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-55180-3_26
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Jurkovic, Z., Cukor, G., Brezocnik, M., Brajkovic, T.: A comparison of machine learning methods for cutting parameters prediction in high speed turning process. J. Intell. Manuf. 29(8), 1683–1693 (2018)
Larrañaga, P., Atienza, D., Diaz-Rozo, J., Ogbechie, A., Puerto-Santana, C.E., Bielza, C.: Industrial Applications of Machine Learning. CRC Press, New York (2018)
Li, X., Zhang, W.: Deep learning-based partial domain adaptation method on intelligent machinery fault diagnostics. IEEE Trans. Ind. Electron. (2020)
Munro, J., Damen, D.: Multi-modal domain adaptation for fine-grained action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 122–132 (2020)
Roy, S., Siarohin, A., Sangineto, E., Bulo, S.R., Sebe, N., Ricci, E.: Unsupervised domain adaptation using feature-whitening and consensus loss. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9471–9480 (2019)
Santolini, G.: Deep Learning Models for Cut Interruption Detection in Laser Cutting Machines. Master’s thesis, University of Trento (Department of Industrial Engineering), Trento (2019)
Santolini, G., Rota, P., Gandolfi, D., Bosetti, P.: Cut quality estimation in industrial laser cutting machines: a machine learning approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)
Shan, Y., Lu, W.F., Chew, C.M.: Pixel and feature level based domain adaptation for object detection in autonomous driving. Neurocomputing 367, 31–38 (2019)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Tercan, H., Al Khawli, T., Eppelt, U., Büscher, C., Meisen, T., Jeschke, S.: Improving the laser cutting process design by machine learning techniques. Prod. Eng. 11(2), 195–203 (2017)
Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7167–7176 (2017)
Zhang, Y., David, P., Gong, B.: Curriculum domain adaptation for semantic segmentation of urban scenes. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2020–2030 (2017)
Zhao, S., et al.: Multi-source domain adaptation for semantic segmentation. In: Advances in Neural Information Processing Systems, pp. 7287–7300 (2019)
Acknowledgment
The authors would like to thank Adige S.p.A. for the opportunity of working on such interesting research project, for their expert advice and collaborative flair. We would like to thank ProM Facility for providing computational resources and support that have contributed to these research results.
The project presented in this paper has been funded with the contribution of the Autonomous Province of Trento, Italy, through the Regional Law 6/99. Name of the granted Project: LT4.0.
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Peghini, N., Zignoli, A., Gandolfi, D., Rota, P., Bosetti, P. (2021). Real-Time Cross-Dataset Quality Production Assessment in Industrial Laser Cutting Machines. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12664. Springer, Cham. https://doi.org/10.1007/978-3-030-68799-1_36
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