9
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Predicting melt track geometry and part density in laser powder bed fusion of metals using machine learning

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Laser powder bed fusion of metals (PBF-LB/M) is a process widely used in additive manufacturing (AM). It is highly sensitive to its process parameters directly determining the quality of the components. Hence, optimal parameters are needed to ensure the highest part quality. However, current approaches such as experimental investigation and the numerical simulation of the process are time-consuming and costly, requiring more efficient ways for parameter optimization. In this work, the use of machine learning (ML) for parameter search is investigated based on the influence of laser power and speed on simulated melt pool dimensions and experimentally determined part density. In total, four machine learning algorithms are considered. The models are trained to predict the melt pool size and part density based on the process parameters. The accuracy is evaluated based on the deviation of the prediction from the actual value. The models are implemented in python using the scikit-learn library. The results show that ML models provide generalized predictions with small errors for both the melt pool dimensions and the part density, demonstrating the potential of ML in AM. The main limitation is data collection, which is still done experimentally or simulatively. However, the results show that ML provides an opportunity for more efficient parameter optimization in PBF-LB/M.

          Related collections

          Most cited references16

          • Record: found
          • Abstract: not found
          • Article: not found

          Machine Learning in Additive Manufacturing: A Review

            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            In-process sensing in selective laser melting (SLM) additive manufacturing

              Bookmark
              • Record: found
              • Abstract: not found
              • Book: not found

              Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems

                Bookmark

                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Progress in Additive Manufacturing
                Prog Addit Manuf
                Springer Science and Business Media LLC
                2363-9512
                2363-9520
                February 2023
                January 09 2023
                February 2023
                : 8
                : 1
                : 47-54
                Article
                10.1007/s40964-022-00387-3
                3aa40a25-f017-4948-8101-b0e05b07f7b4
                © 2023

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

                History

                Comments

                Comment on this article