Hostname: page-component-848d4c4894-tn8tq Total loading time: 0 Render date: 2024-06-21T19:41:19.013Z Has data issue: false hasContentIssue false
  • English
  • Français

APPLICATION OF UNSUPERVISED LEARNING AND IMAGE PROCESSING INTO CLASSIFICATION OF DESIGNS TO BE FABRICATED WITH ADDITIVE OR TRADITIONAL MANUFACTURING

Published online by Cambridge University Press:  19 June 2023

Baris Ördek  and
Yuri Borgianni
Baris Ördek*
Affiliation:
Free University of Bozen-Bolzano
Yuri Borgianni
Affiliation:
Free University of Bozen-Bolzano
*
Ördek, Baris, Free University of Bozen-Bolzano, Italy, boerdek@unibz.it

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Manufacturing process (MP) selection systems require a large amount of labelled data, typically not provided as design outputs. This issue is made more severe with the continuous development of Additive Manufacturing systems, which can be increasingly used to substitute traditional manufacturing technologies. The objective of this paper is to investigate the application of image processing for classifying MPs in an unsupervised approach. To this scope, k-means and hierarchical clustering algorithms are applied to an unlabelled image dataset. The input dataset is constructed from freely accessible web databases and consists of twenty randomly selected CAD models and corresponding images of machine elements: 35% additively manufactured parts and 65% manufactured with traditional manufacturing technologies. The input images are pre-processed to have the same colour and size. The k-means and hierarchical clustering algorithms reported 65% and 60% accuracy, respectively. The algorithms show comparable performance, however, the k-means algorithm failed to predict the correct subdivisions. The research shows promising potential for MP classification and image processing applications.

Keywords

Computer Aided Design (CAD)Machine learningAdditive ManufacturingImage ProcessingUnsupervised Learning
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 613 - 622
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

References

Algunaid, K.M.A. and Liu, J. (2022), “Decision support system to select a 3D printing process/machine and material from a large-scale options pool”, International Journal of Advanced Manufacturing Technology, Springer Science and Business Media Deutschland GmbH, Vol. 121 No. 11–12, pp. 7643–7659, https://doi.org/10.1007/S00170-022-09362-2.CrossRefGoogle Scholar
Ashenden, S.K., Bartosik, A., Agapow, P.M. and Semenova, E. (2021), “Introduction to artificial intelligence and machine learning”, The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry, Elsevier, pp. 15–26, https://doi.org/10.1016/B978-0-12-820045-2.00003-9.CrossRefGoogle Scholar
Bhattacharya, S., Reddy Maddikunta, P.K., Pham, Q.V., Gadekallu, T.R., Krishnan, S.S.R., Chowdhary, C.L., Alazab, M., et al. (2021), “Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey”, Sustainable Cities and Society, Elsevier, Vol. 65, p. 102589, https://doi.org/10.1016/J.SCS.2020.102589.Google Scholar
Bhattacharyya, S. (2011), “A brief survey of color image preprocessing and segmentation techniques”, Journal of Pattern Recognition Research, Journal of Pattern Recognition Research, Vol. 1 No. 1, pp. 120–129.CrossRefGoogle Scholar
Choi, J. il, Lee, Y., Kim, Y.Y. and Lee, B.Y. (2017), “Image-processing technique to detect carbonation regions of concrete sprayed with a phenolphthalein solution”, Construction and Building Materials, Elsevier, Vol. 154, pp. 451–461, https://doi.org/10.1016/J.CONBUILDMAT.2017.07.205.Google Scholar
Dogan, A. and Birant, D. (2021), “Machine learning and data mining in manufacturing”, Expert Systems with Applications, Pergamon, Vol. 166, p. 114060, https://doi.org/10.1016/J.ESWA.2020.114060.Google Scholar
Dohale, V., Gunasekaran, A., Akarte, M. and Verma, P. (2021), “An integrated Delphi-MCDM-Bayesian Network framework for production system selection”, International Journal of Production Economics, Vol. 242, https://doi.org/10.1016/j.ijpe.2021.108296.CrossRefGoogle Scholar
García-Moreno, A.-I., Alvarado-Orozco, J.-M., Ibarra-Medina, J. and Martínez-Franco, E. (2020), “Image-based porosity classification in Al-alloys by laser metal deposition using random forests”, International Journal of Advanced Manufacturing Technology, Vol. 110 No. 9–10, pp. 2827–2845, https://doi.org/10.1007/s00170-020-05887-6.CrossRefGoogle Scholar
Garza-Ulloa, J. (2018), “Application of mathematical models in biomechatronics: artificial intelligence and time-frequency analysis”, Applied Biomechatronics Using Mathematical Models, Academic Press, pp. 373–524, https://doi.org/10.1016/B978-0-12-812594-6.00006-8.CrossRefGoogle Scholar
Gerlach, T. and Eggink, D.H.D. (2021), “Generative Adversarial Networks for spot weld design”, IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, Vol. 2021-September, Institute of Electrical and Electronics Engineers Inc., https://doi.org/10.1109/ETFA45728.2021.9613282.CrossRefGoogle Scholar
Grasso, M., Laguzza, V., Semeraro, Q. and Colosimo, B.M. (2017), “In-Process Monitoring of Selective Laser Melting: Spatial Detection of Defects Via Image Data Analysis”, Journal of Manufacturing Science and Engineering, American Society of Mechanical Engineers (ASME), Vol. 139 No. 5, https://doi.org/10.1115/1.4034715/377587.CrossRefGoogle Scholar
Hariri, R.H., Fredericks, E.M. and Bowers, K.M. (2019), “Uncertainty in big data analytics: survey, opportunities, and challenges”, Journal of Big Data, SpringerOpen, Vol. 6 No. 1, pp. 1–16, https://dx.doi.org/10.1186/S40537-019-0206-3.Google Scholar
Hernández-Castellano, P., Martínez-Rivero, M.D., Marrero-Alemán, M.D. and Suárez-García, L. (2019), “Manufacturing Process Selection Integrated in the Design Process: University and Industry”, Procedia Manufacturing, Vol. 41, Elsevier, pp. 1079–1086, https://doi.org/10.1016/J.PROMFG.2019.10.036.Google Scholar
Hoefer, M.J. and Frank, M.C. (2018), “Automated manufacturing process selection during conceptual design”, Journal of Mechanical Design, Transactions of the ASME, American Society of Mechanical Engineers (ASME), Vol. 140 No. 3, https://doi.org/10.1115/1.4038686.CrossRefGoogle Scholar
Jayanti, S., Kalyanaraman, Y. and Ramani, K. (2009), “Shape-based clustering for 3D CAD objects: A comparative study of effectiveness”, Computer-Aided Design, Elsevier, Vol. 41 No. 12, pp. 999–1007, https://doi.org/10.1016/J.CAD.2009.07.003.Google Scholar
Ji, X., Vedaldi, A. and Henriques, J. (2018), “Invariant Information Clustering for Unsupervised Image Classification and Segmentation”, Proceedings of the IEEE International Conference on Computer Vision, Institute of Electrical and Electronics Engineers Inc., Vol. 2019-October, pp. 9864–9873, https://doi.org/10.48550/arxiv.1807.06653.Google Scholar
Jiang, J., Xiong, Y., Zhang, Z. and Rosen, D.W. (2022), “Machine learning integrated design for additive manufacturing”, Journal of Intelligent Manufacturing, Springer, Vol. 33 No. 4, pp. 1073–1086, https://doi.org/10.1007/S10845-020-01715-6.Google Scholar
Kek, V., Vinodh, S., Brajesh, P. and Muralidharan, R. (2016), “Rapid prototyping process selection using multi criteria decision making considering environmental criteria and its decision support system”, Rapid Prototyping Journal, Emerald Group Publishing Ltd., Vol. 22 No. 2, pp. 225–250, https://doi.org/10.1108/RPJ-03-2014-0040.CrossRefGoogle Scholar
Kuruvilla, J., Sukumaran, D., Sankar, A. and Joy, S.P. (2016), “A review on image processing and image segmentation”, Proceedings of 2016 International Conference on Data Mining and Advanced Computing, SAPIENCE 2016, Institute of Electrical and Electronics Engineers Inc., pp. 198–203, https://doi.org/10.1109/SAPIENCE.2016.7684170.CrossRefGoogle Scholar
Liu, W., Zhu, Z. and Ye, S. (2020), “A decision-making methodology integrated in product design for additive manufacturing process selection”, Rapid Prototyping Journal, Emerald Group Holdings Ltd., Vol. 26 No. 5, pp. 895–909, https://doi.org/10.1108/RPJ-06-2019-0174.CrossRefGoogle Scholar
Liu, Y., Jin, M., Pan, S., Zhou, C., Zheng, Y., Xia, F. and Yu, P. (2022), “Graph Self-Supervised Learning: A Survey”, IEEE Transactions on Knowledge and Data Engineering, IEEE Computer Society, https://doi.org/10.1109/TKDE.2022.3172903.Google Scholar
Manikonda, S.K.G. and Gaonkar, D.N. (2020), “Islanding detection method based on image classification technique using histogram of oriented gradient features”, IET Generation, Transmission & Distribution, The Institution of Engineering and Technology, Vol. 14 No. 14, pp. 2790–2799, https://doi.org/10.1049/IET-GTD.2019.1824.CrossRefGoogle Scholar
Maree, R., Stevens, B., Geurts, P., Guern, Y. and Mack, P. (2009), “A machine learning approach for material detection in hyperspectral images”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Institute of Electrical and Electronics Engineers (IEEE), pp. 106–111, https://doi.org/10.1109/CVPRW.2009.5204119.Google Scholar
Martínez-Arellano, G., Terrazas, G. and Ratchev, S. (2019), “Tool wear classification using time series imaging and deep learning”, International Journal of Advanced Manufacturing Technology, Springer London, Vol. 104 No. 9–12, pp. 3647–3662, https://doi.org/10.1007/S00170-019-04090-6.CrossRefGoogle Scholar
McCue, C. (2015), “Identification, Characterization, and Modeling”, Data Mining and Predictive Analysis, Butterworth-Heinemann, pp. 137–155, https://doi.org/10.1016/B978-0-12-800229-2.00007-9.CrossRefGoogle Scholar
Mukherjee, R. (2017), “Selection of Sustainable Process and Essential Indicators for Decision Making Using Machine Learning Algorithms”, Process Integration and Optimization for Sustainability, Springer, Vol. 1 No. 2, pp. 153–163, https://doi.org/10.1007/S41660-017-0011-4.CrossRefGoogle Scholar
Ördek, B., Borgianni, Y. and Coatanea, E. (2022), “Classification Framework for Machine Learning Support in Manufacturing”, in Matt Dominik, T., Vidoni, R., Rauch, Erwin and Dallasega, Patrick (Eds.), Managing and Implementing the Digital Transformation, Vol. 525, Springer, Cham, pp. 61–73, doi: https://doi.org/10.1007/978-3-031-14317-5_6.CrossRefGoogle Scholar
Paturi, U.M.R. and Cheruku, S. (2021), “Application and performance of machine learning techniques in manufacturing sector from the past two decades: A review”, Materials Today: Proceedings, Elsevier, Vol. 38, pp. 2392–2401, https://doi.org/10.1016/J.MATPR.2020.07.209.Google Scholar
Pernot, J.-P., Giannini, F. and Petton, C. (2015), “Thin part identification for CAD model classification”, edited by Gerritsen, B, H.M.. and Imre Horvath, P. Engineering Computations, Emerald Group Publishing Limited, Vol. 32 No. 1, pp. 62–85, https://doi.org/10.1108/EC-03-2013-0082.Google Scholar
Pilania, G., Wang, C., Jiang, X., Rajasekaran, S. and Ramprasad, R. (2013), “Accelerating materials property predictions using machine learning”, Scientific Reports 2013 3:1, Nature Publishing Group, Vol. 3 No. 1, pp. 1–6, https://doi.org/10.1038/srep02810.Google Scholar
Pouladzadeh, P., Shirmohammadi, S. and Al-Maghrabi, R. (2014), “Measuring calorie and nutrition from food image”, IEEE Transactions on Instrumentation and Measurement, Institute of Electrical and Electronics Engineers Inc., Vol. 63 No. 8, pp. 1947–1956, https://doi.org/10.1109/TIM.2014.2303533.CrossRefGoogle Scholar
Qin, F.W., Li, L.Y., Gao, S.M., Yang, X.L. and Chen, X. (2014), “A deep learning approach to the classification of 3D CAD models”, Journal of Zhejiang University: Science C, Zhejiang University, Vol. 15 No. 2, pp. 91–106, https://doi.org/10.1631/JZUS.C1300185.CrossRefGoogle Scholar
Qin, Y., Qi, Q., Scott, P.J. and Jiang, X. (2020), “An additive manufacturing process selection approach based on fuzzy Archimedean weighted power Bonferroni aggregation operators”, Robotics and Computer-Integrated Manufacturing, Pergamon, Vol. 64, p. 101926, https://doi.org/10.1016/J.RCIM.2019.101926.Google Scholar
Salobir, J., Duhovnik, J. and Tavčar, J. (2019), “Methods and Principles of Product Design for Small-Scale Production Based on 3D Printing”, Proceedings of the Design Society: International Conference on Engineering Design, Vol. 1, Cambridge University Press, pp. 789–798, https://doi.org/10.1017/DSI.2019.83.Google Scholar
Schneberger, J.H., Kaspar, J. and Vielhaber, M. (2019), “Integrated and Customer-Oriented Material and Process Selection by Sensory Multi-Criteria Decision-Making”, Proceedings of the Design Society: International Conference on Engineering Design, Vol. 1, Cambridge University Press, pp. 1175–1184, https://doi.org/10.1017/DSI.2019.123.CrossRefGoogle Scholar
Shahapure, K.R. and Nicholas, C. (2020), “Cluster quality analysis using silhouette score”, Proceedings - 2020 IEEE 7th International Conference on Data Science and Advanced Analytics, DSAA 2020, Institute of Electrical and Electronics Engineers Inc., pp. 747–748, https://doi.org/10.1109/DSAA49011.2020.00096.CrossRefGoogle Scholar
Tavcar, J. and Nordin, A. (2021), “MULTI-CRITERIA ASSESSMENT AND PROCESS SELECTION MODEL FOR ADDITIVE MANUFACTURING IN THE CONCEPTUAL PHASE OF DESIGN”, Proceedings of the Design Society, Vol. 1, Cambridge University Press, pp. 2197–2206, https://doi.org/10.1017/PDS.2021.481.CrossRefGoogle Scholar
Venkataraman, K., Vijaya Ramnath, B., Sarvesh, R. and Rohit Prasanna, C. (2015), “Selection of Manufacturing Method Using Artificial Neural Network”, Applied Mechanics and Materials, Trans Tech Publications Ltd, Vol. 766–767, pp. 1201–1206, https://doi.org/10.4028/WWW.SCIENTIFIC.NET/AMM.766-767.1201.CrossRefGoogle Scholar
Wang, B., Wang, P., Song, J., Lam, Y.C., Song, H., Wang, Y. and Liu, S. (2022), “A hybrid machine learning approach to determine the optimal processing window in femtosecond laser-induced periodic nanostructures”, Journal of Materials Processing Technology, Elsevier, Vol. 308, p. 117716, https://doi.org/10.1016/J.JMATPROTEC.2022.117716.CrossRefGoogle Scholar
Wang, Y., Zhong, R.Y. and Xu, X. (2018), “A decision support system for additive manufacturing process selection using a hybrid multiple criteria decision-making method”, Rapid Prototyping Journal, Emerald Group Holdings Ltd., Vol. 24 No. 9, pp. 1544–1553, https://doi.org/10.1108/RPJ-01-2018-0002.CrossRefGoogle Scholar
Wang, Z. and Rosen, D. (2022), “Manufacturing process classification based on heat kernel signature and convolutional neural networks”, Journal of Intelligent Manufacturing, Springer, pp. 1–23, https://doi.org/10.1007/S10845-022-02009-9.Google Scholar
Yao, X., Moon, S.K. and Bi, G. (2017), “A hybrid machine learning approach for additive manufacturing design feature recommendation”, Rapid Prototyping Journal, Emerald Group Publishing Ltd., Vol. 23 No. 6, pp. 983–997, https://doi.org/10.1108/RPJ-03-2016-0041.CrossRefGoogle Scholar
Yu, S.X. and Shi, J. (2003), “Multiclass spectral clustering”, Proceedings of the IEEE International Conference on Computer Vision, Vol. 1, Institute of Electrical and Electronics Engineers Inc., pp. 313–319, https://doi.org/10.1109/ICCV.2003.1238361.Google Scholar
Zaman, U.K. uz, Rivette, M., Siadat, A. and Mousavi, S.M. (2018), “Integrated product-process design: Material and manufacturing process selection for additive manufacturing using multi-criteria decision making”, Robotics and Computer-Integrated Manufacturing, Pergamon, Vol. 51, pp. 169–180, https://doi.org/10.1016/J.RCIM.2017.12.005.CrossRefGoogle Scholar
Zelnik-manor, L. and Perona, P. (2004), “Self-Tuning Spectral Clustering”, in Saul, L., Weiss, Y. and Bottou, L. (Eds.), Advances in Neural Information Processing Systems, Vol. 17, MIT Press.Google Scholar
Zhang, Y., Shen, S., Li, H. and Hu, Y. (2022), “Review of in situ and real-time monitoring of metal additive manufacturing based on image processing”, The International Journal of Advanced Manufacturing Technology, Springer Science and Business Media LLC, Vol. 123 No. 1, pp. 1–20, https://doi.org/10.1007/S00170-022-10178-3.CrossRefGoogle Scholar
Zhang, Y., Xu, Y. and Bernard, A. (2014), “A new decision support method for the selection of RP process: knowledge value measuring”, International Journal of Computer Integrated Manufacturing, Taylor & Francis, Vol. 27 No. 8, pp. 747–758, https://doi.org/10.1080/0951192X.2013.834474.CrossRefGoogle Scholar
Zhou, W., Gao, S., Zhang, L. and Lou, X. (2020), “Histogram of Oriented Gradients Feature Extraction from Raw Bayer Pattern Images”, IEEE Transactions on Circuits and Systems II: Express Briefs, Institute of Electrical and Electronics Engineers Inc., Vol. 67 No. 5, pp. 946–950, https://doi.org/10.1109/TCSII.2020.2980557.Google Scholar