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Data-driven Approach to Explore the Contribution of Process Parameters for Laser Powder Bed Fusion of a Ti-6Al-4V Alloy KCI 등재
- 언어ENG
- URLhttps://db.koreascholar.com/Article/Detail/436369
pp.137-145
In order to predict the process window of laser powder bed fusion (LPBF) for printing metallic components, the calculation of volumetric energy density (VED) has been widely calculated for controlling process parameters. However, because it is assumed that the process parameters contribute equally to heat input, the VED still has limitation for predicting the process window of LPBF-processed materials. In this study, an explainable machine learning (xML) approach was adopted to predict and understand the contribution of each process parameter to defect evolution in Ti alloys in the LPBF process. Various ML models were trained, and the Shapley additive explanation method was adopted to quantify the importance of each process parameter. This study can offer effective guidelines for fine-tuning process parameters to fabricate high-quality products using LPBF.
2. Experimental Procedure
2.1. Sample fabrication and characterization
2.2. Machine learning model training procedure
3. Results and Discussion
3.1. Defect evolution in the samples
3.2. ML model analysis
4. Conclusion
Acknowledgments
References
