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DATA ANALYSIS AS THE BASIS FOR IMPROVED DESIGN FOR ADDITIVE MANUFACTURING (DFAM)

Published online by Cambridge University Press:  27 July 2021

Dominika Hamulczuk  and
Ola Isaksson
Dominika Hamulczuk*
Affiliation:
Chalmers University of Technology
Ola Isaksson
Affiliation:
Chalmers University of Technology
*
Hamulczuk, Dominika, Chalmers University of Technology, Mechanics and Maritime Sciences, Sweden, [email protected]

Abstract

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Additive Manufacturing (AM) has a large potential to revolutionize the manufacturing industry, yet the printing parameters and part design have a profound impact on the robustness of the printing process as well as the resulting quality of the manufactured components. To control the printing process, a substantial number of parameters is measured while printing and used primarily to control and adjust the printing process in-situ. The question raised in this paper is how to benefit from these data being gathered to gain insight into the print process stability. The case study performed included the analysis of data gathered during printing 22 components. The analysis was performed with a widely used Random Forest Classifier. The study revealed that the data did contain some detectable patterns that can be used further in assessing the quality of the printed component, however, they were distinct enough so that in case the test and train sets were comprised of separate components the predictions’ result was very poor. The study gives a good understanding of what is necessary to do a meaningful analytics study of manufacturing data from a design perspective.

Keywords

Design for Additive Manufacturing (DfAM)Machine learningAdditive Manufacturing
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 811 - 820
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), 2021. Published by Cambridge University Press

References

Aminzadeh, M. and Kurfess, T. (2019), “Online quality inspection using bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images”, Journal of Intelligent Manufacturing, Vol. 30, pp. 1–19, 10.1007/s10845-018-1412-0.10.1007/s10845-018-1412-0CrossRefGoogle Scholar
Attaran, M. (2017), “The rise of 3-d printing: The advantages of additive manufacturing over traditional manufacturing”, Business Horizons, Vol. 60 No. 5, pp. 677–688, https://doi.org/10.1016/j.bushor.2017.05.011.CrossRefGoogle Scholar
Diegel, O., Nordin, A. and Motte, D. (2019a), Design for Metal AM, Springer Singapore, Singapore, pp. 121–155, 10.1007/978-981-13-8281-99.Google Scholar
Diegel, O., Nordin, A. and Motte, D. (2019b), DfAM Strategic Design Considerations, Springer Singapore, Singapore, pp. 41–70, 10.1007/978-981-13-8281-93.Google Scholar
Dongsen, Y., Hong, G.S., Zhang, Y., Zhu, K. and Fuh, J. (2018), “Defect detection in selective laser melting technology by acoustic signals with deep belief networks”, The International Journal of Advanced Manufacturing Technology, Vol. 96, 10.1007/s00170-018-1728-0.Google Scholar
Eckert, C., Isaksson, O., Eckert, C., Coeckelbergh, M. and Hagström, M.H. (2020), “Data Fairy in Engineering Land: The Magic of Data Analysis as a Sociotechnical Process in Engineering Companies”, Journal of Mechanical Design, Vol. 142 No. 12, 10.1115/1.4047813.121402.10.1115/1.4047813CrossRefGoogle Scholar
EPMA (2019), Introduction to Additive Manufacturing Technology: a guide for Designers and Engineers. 3rd Edition. URL: https://www.epma.com/epma-free-publications/product/introduction-to-additivemanufacturing-brochureGoogle Scholar
Grasso, M., Gallina, F. and Colosimo, B. (2018), “Data fusion methods for statistical process monitoring and quality characterization in metal additive manufacturing”, Procedia CIRP, Vol. 75, pp. 103–107, 10.1016/j.procir.2018.04.045.10.1016/j.procir.2018.04.045CrossRefGoogle Scholar
LaurensvanLieshout, C. (2009), “Selective laser sintering process.”, . URL: https://upload.wikimedia.org/wikipedia/commons/e/ed/Selectivelasersinteringprinciple:pngGoogle Scholar
Liaw, A., Wiener, M. et al. (2002), “Classification and regression by random forest”, R news, Vol. 2 No. 3, pp. 18–22.Google Scholar
Materialise (), “Materialise magics”, https://www.materialise.com/en/software/magics.Google Scholar
Montavon, G., Samek, W. and Müller, K.R. (2018), “Methods for interpreting and understanding deep neural networks”, Digital Signal Processing, Vol. 73, pp. 1 – 15, https://doi.org/10.1016/j.dsp.2017.10.011.CrossRefGoogle Scholar
Moran, T., Warner, D. and Phan, N. (2020), “Scan-by-scan part-scale thermal modelling for defect prediction in metal additive manufacturing”, Additive Manufacturing, p. 101667, https://doi.org/10.1016/j.addma.2020.101667.CrossRefGoogle Scholar
Petrich, J., Gobert, C., Phoha, S., Nassar, A. and Reutzel, E. (2020), “Machine learning for defect detection for pbfam using high resolution layerwise imaging coupled with post-build ct scans”, pp. 1363–1381. 28th Annual International Solid Freeform Fabrication Symposium - An Additive Manufacturing Conference, SFF 2017 ; Conference date: 07-08-2017 Through 09-08-2017.Google Scholar
Razvi, S.S., Feng, S., Narayanan, A., Lee, Y.T. and Witherell, P. (2019), “A review of machine learning applications in additive manufacturing”, 10.1115/DETC2019-98415.Google Scholar
Rebala, G., Ravi, A. and Churiwala, S. (2019), Classification, Springer International Publishing, Cham, pp. 57–66, 10.1007/978-3-030-15729-65.Google Scholar
Runkler, T.A. (2016), Introduction, Springer Fachmedien Wiesbaden, Wiesbaden, pp. 1–3, 10.1007/978-3-658-14075-51.Google Scholar
Shevchik, S., Kenel, C., Leinenbach, C. and Wasmer, K. (2018), “Acoustic emission for in situ quality monitoring in additive manufacturing using spectral convolutional neural networks”, Additive Manufacturing, Vol. 21, pp. 598–604, 10.1016/j.addma.2017.11.012.10.1016/j.addma.2017.11.012CrossRefGoogle Scholar
Uhlmann, E., Pastl, R., Laghmouchi, H. and Bergmann, A. (2017), “Intelligent pattern recognition of a slm machine process and sensor data”, Procedia CIRP, Vol. 62, pp. 464–469, 10.1016/j.procir.2016.06.060.10.1016/j.procir.2016.06.060CrossRefGoogle Scholar
Yao, X., Moon, S. and Bi, G. (2017), “A hybrid machine learning approach for additive manufacturing design feature recommendation”, Rapid Prototyping Journal, Vol. 23, pp. 00–00, 10.1108/RPJ-03-2016-0041.10.1108/RPJ-03-2016-0041CrossRefGoogle Scholar
Yap, C.Y., Chua, C., Dong, Z., Liu, Z., Zhang, D., Loh, L. and Sing, S.L. (2015), “Review of selective laser melting: Materials and applications”, Applied Physics Reviews, Vol. 2, p. 041101, 10.1063/1.4935926.10.1063/1.4935926CrossRefGoogle Scholar
Zhehan, C., Xiaohua, Z., Ketai, H., Xianhui, Z. and Haiyue, W. (2018), “Image processing methods based on key temperature features for state analysis and process monitoring of selective laser melting (slm)”, pp. 110–115, 10.1145/3206185.3206210.10.1145/3206185.3206210CrossRefGoogle Scholar