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Optimization of Local Processing Conditions in Complex Part Geometries Through Novel Scan Strategy in Laser Powder Bed Fusion Process

  • Applications of Machine Learning in Materials Development and Manufacturing
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Abstract

Additive manufacturing (AM) involves construction of 3D parts by sequentially adding material to a component and has undergone advancements in the range of materials used and the complexity of parts being printed. Laser powder bed fusion (LPBF) AM, which is the focal point of this work, has sparked interest in the materials and manufacturing community. LPBF has the ability to print complex designs, which may reduce production costs depending on materials, machine, time to print the part, and desired part quality. These complex designs introduce complex processing spaces, resulting in local processing heterogeneities, which may limit the application of LPBF. Hence, there is a need to develop methods to efficiently search for process parameter sets that reduce local processing heterogeneities. We present an optimization methodology implemented to demonstrate the advantages of locally tailored process parameters to produce a more homogeneous component. The optimization is applied to two geometries, using an optimized single parameter set for the entire geometry and locally optimized scan parameters developed based on vector level analysis. Lastly, we show how different optimized scan parameter sets can be related to the different subregions in the part in a generalized way to be applied to numerous geometries without retraining.

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References

  1. J.-Y. Lee, J. An, and C.K. Chua, Appl. Mater. Today 7, 120 (2017).

    Article  Google Scholar 

  2. S. Srinivasan, B. Swick, and M.A. Groeber, JOM 72, 4393 (2020).

    Article  Google Scholar 

  3. J. Jiang and Y. Ma, Micromachines 11, 633 (2020).

    Article  Google Scholar 

  4. L. Englert, S. Czink, S. Dietrich, and V. Schulze, J. Mater. Process. Technol. 299, 117331 (2022).

    Article  Google Scholar 

  5. M.J. Matthews, G. Guss, S.A. Khairallah, A.M. Rubenchik, P.J. Depond, and W.E. King, Acta Mater. 114, 33 (2016).

    Article  Google Scholar 

  6. E.J. Schwalbach, S.P. Donegan, M.G. Chapman, K.J. Chaput, and M.A. Groeber, Addit. Manuf. 25, 485 (2019).

    Google Scholar 

  7. B. Stump, A. Plotkowski, and J. Coleman, Model. Simul. Mater. Sci. Eng. 29, 035001 (2021).

    Article  Google Scholar 

  8. J. Lin, E. Keogh, S. Lonardi, B. Chiu, in DMKD ’03 Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (2003).

  9. E. Duong, L. Masseling, C. Knaak, P. Dionne, and M. Megahed, Addit. Manuf. Lett. 3, 100047 (2022).

    Article  Google Scholar 

  10. R.L. Iman, Encyclopedia of Quantitative Risk Analysis and Assessment (John Wiley & Sons, Ltd, 2008).

    Google Scholar 

  11. H. Yeung, B. Lane, and J. Fox, Addit. Manuf. 30, 100844 (2019).

    Google Scholar 

  12. C.L. Druzgalski, A. Ashby, G. Guss, W.E. King, T.T. Roehling, and M.J. Matthews, Addit. Manuf. 34, 101169 (2020).

    Google Scholar 

  13. T. Szandała, in Bio-Inspired Neurocomputing. ed. by A.K. Bhoi, P.K. Mallick, C.-M. Liu, and V.E. Balas (Springer, Singapore, 2021), pp. 203–224.

    Chapter  Google Scholar 

  14. A.J. Smola and B. Schölkopf, Stat. Comput. 14, 199 (2004).

    Article  MathSciNet  Google Scholar 

  15. D. Berrar, in Encyclopedia of Bioinformatics and Computational Biology. ed. by S. Ranganathan, M. Gribskov, K. Nakai, and C. Schönbach (Academic Press, Oxford, 2019), pp. 542–545.

    Chapter  Google Scholar 

  16. K. Weiss, T.M. Khoshgoftaar, and D. Wang, J. Big Data 3, 9 (2016).

    Article  Google Scholar 

  17. L. Torrey and J. Shavlik, in Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques. ed. by E.S. Olivas, J.D.M. Guerrero, M. Martinez-Sober, R. Magdalena-Benedito, and A.J.S. Lopez (IGI Global, Hershey, 2010), pp. 242–264.

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Department of Integrated Systems Engineering, The Ohio State University, Columbus, OH, 43210, USA

    Sandeep Srinivasan, Brennan Swick & Michael A. Groeber

Authors
  1. Sandeep Srinivasan
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  2. Brennan Swick
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  3. Michael A. Groeber
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Correspondence to Michael A. Groeber.

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Srinivasan, S., Swick, B. & Groeber, M.A. Optimization of Local Processing Conditions in Complex Part Geometries Through Novel Scan Strategy in Laser Powder Bed Fusion Process. JOM 76, 99–113 (2024). https://doi.org/10.1007/s11837-023-06255-x

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  • DOI: https://doi.org/10.1007/s11837-023-06255-x

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