Unveiling processing–property relationships in laser powder bed fusion: The synergy of machine learning and high-throughput experiments
Achieving desired mechanical properties in additive manufacturing requires many experiments and a well-defined design framework becomes crucial in reducing trials and conserving resources. Here, we propose a methodology embracing the synergy between high-throughput (HT) experimentation and hierarchi...
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Elsevier
2025-04-01
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| Series: | Materials & Design |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S026412752500125X |
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| author | Mahsa Amiri Zahra Zanjani Foumani Penghui Cao Lorenzo Valdevit Ramin Bostanabad |
| author_facet | Mahsa Amiri Zahra Zanjani Foumani Penghui Cao Lorenzo Valdevit Ramin Bostanabad |
| author_sort | Mahsa Amiri |
| collection | DOAJ |
| description | Achieving desired mechanical properties in additive manufacturing requires many experiments and a well-defined design framework becomes crucial in reducing trials and conserving resources. Here, we propose a methodology embracing the synergy between high-throughput (HT) experimentation and hierarchical machine learning (ML) to unveil the complex relationships between a large set of process parameters in Laser Powder Bed Fusion (LPBF) and selected mechanical properties (tensile strength and ductility). The HT method envisions the fabrication of small samples for rapid automated hardness and porosity characterization, and a smaller set of tensile specimens for more labor-intensive direct measurement of yield strength and ductility. The ML approach is based on a sequential application of Gaussian processes (GPs) where the correlations between process parameters and hardness/porosity are first learnt and subsequently adopted by the GPs that relate strength and ductility to process parameters. Finally, an optimization scheme is devised that leverages these GPs to identify the process parameters that maximize combinations of strength and ductility. By founding the learning on larger “easy-to-collect” and smaller “labor-intensive” data, we reduce the reliance on expensive characterization and enable exploration of a large processing space. Our approach is material-agnostic and herein we demonstrate its application on 17-4PH stainless steel. |
| format | Article |
| id | doaj-art-56e432a4b3db4d09a0f935807a6724c3 |
| institution | Kabale University |
| issn | 0264-1275 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj-art-56e432a4b3db4d09a0f935807a6724c32025-08-20T03:44:12ZengElsevierMaterials & Design0264-12752025-04-0125211370510.1016/j.matdes.2025.113705Unveiling processing–property relationships in laser powder bed fusion: The synergy of machine learning and high-throughput experimentsMahsa Amiri0Zahra Zanjani Foumani1Penghui Cao2Lorenzo Valdevit3Ramin Bostanabad4Materials and Manufacturing Technology Program, University of California, Irvine, CA 92697, USADepartment of Mechanical and Aerospace Engineering, University of California, Irvine, CA 92697, USAMaterials and Manufacturing Technology Program, University of California, Irvine, CA 92697, USA; Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA 92697, USA; Department of Materials Science and Engineering, University of California, Irvine, CA 92697, USAMaterials and Manufacturing Technology Program, University of California, Irvine, CA 92697, USA; Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA 92697, USA; Department of Materials Science and Engineering, University of California, Irvine, CA 92697, USA; Corresponding author at: Department of Materials Science and Engineering, University of California, Irvine, CA 92697, USA.Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA 92697, USA; Department of Civil and Environmental Engineering, University of California, Irvine, CA 92697, USA; Corresponding author at: Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA 92697, USA.Achieving desired mechanical properties in additive manufacturing requires many experiments and a well-defined design framework becomes crucial in reducing trials and conserving resources. Here, we propose a methodology embracing the synergy between high-throughput (HT) experimentation and hierarchical machine learning (ML) to unveil the complex relationships between a large set of process parameters in Laser Powder Bed Fusion (LPBF) and selected mechanical properties (tensile strength and ductility). The HT method envisions the fabrication of small samples for rapid automated hardness and porosity characterization, and a smaller set of tensile specimens for more labor-intensive direct measurement of yield strength and ductility. The ML approach is based on a sequential application of Gaussian processes (GPs) where the correlations between process parameters and hardness/porosity are first learnt and subsequently adopted by the GPs that relate strength and ductility to process parameters. Finally, an optimization scheme is devised that leverages these GPs to identify the process parameters that maximize combinations of strength and ductility. By founding the learning on larger “easy-to-collect” and smaller “labor-intensive” data, we reduce the reliance on expensive characterization and enable exploration of a large processing space. Our approach is material-agnostic and herein we demonstrate its application on 17-4PH stainless steel.http://www.sciencedirect.com/science/article/pii/S026412752500125XLaser powder bed fusionHigh-throughput experimentsGaussian processUncertainty quantification17-4PH stainless steelMechanical properties |
| spellingShingle | Mahsa Amiri Zahra Zanjani Foumani Penghui Cao Lorenzo Valdevit Ramin Bostanabad Unveiling processing–property relationships in laser powder bed fusion: The synergy of machine learning and high-throughput experiments Materials & Design Laser powder bed fusion High-throughput experiments Gaussian process Uncertainty quantification 17-4PH stainless steel Mechanical properties |
| title | Unveiling processing–property relationships in laser powder bed fusion: The synergy of machine learning and high-throughput experiments |
| title_full | Unveiling processing–property relationships in laser powder bed fusion: The synergy of machine learning and high-throughput experiments |
| title_fullStr | Unveiling processing–property relationships in laser powder bed fusion: The synergy of machine learning and high-throughput experiments |
| title_full_unstemmed | Unveiling processing–property relationships in laser powder bed fusion: The synergy of machine learning and high-throughput experiments |
| title_short | Unveiling processing–property relationships in laser powder bed fusion: The synergy of machine learning and high-throughput experiments |
| title_sort | unveiling processing property relationships in laser powder bed fusion the synergy of machine learning and high throughput experiments |
| topic | Laser powder bed fusion High-throughput experiments Gaussian process Uncertainty quantification 17-4PH stainless steel Mechanical properties |
| url | http://www.sciencedirect.com/science/article/pii/S026412752500125X |
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