A Machine Learning Framework for Melt-Pool Geometry Prediction and Process Parameter Optimization in the Laser Powder-Bed Fusion Process

M. Shafiqur Rahman, Naw Safrin Sattar, Radif Uddin Ahmed, Jonathan Ciaccio, Uttam K. Chakravarty

Research output: Contribution to journalArticlepeer-review

Abstract

This study presents a cost-effective and high-precision machine learning (ML) method for predicting the melt-pool geometry and optimizing the process parameters in the laser powder-bed fusion (LPBF) process with Ti-6Al-4V alloy. Unlike many ML models, the presented method incorporates five key features, including three process parameters (laser power, scanning speed, and spot size) and two material parameters (layer thickness and powder porosity). The target variables are the melt-pool width and depth that collectively define the melt-pool geometry and give insight into the melt-pool dynamics in LPBF. The dataset integrates information from an extensive literature survey, computational fluid dynamics (CFD) modeling, and laser melting experiments. Multiple ML regression methods are assessed to determine the best model to predict the melt-pool geometry. Tenfold cross-validation is applied to evaluate the model performance using five evaluation metrics. Several data pre-processing, augmentation, and feature engineering techniques are performed to improve the accuracy of the models. Results show that the "Extra Trees regression"and "Gaussian process regression"models yield the least errors for predicting melt-pool width and depth, respectively. The ML modeling results are compared with the experimental and CFD modeling results to validate the proposed ML models. The most influential parameter affecting the melt-pool geometry is also determined by the sensitivity analysis. The processing parameters are optimized using an iterative grid search method employing the trained ML models. The presented ML framework offers computational speed and simplicity, which can be implemented in other additive manufacturing techniques to comprehend the critical traits.

Original languageEnglish
Article number041006
JournalJournal of Engineering Materials and Technology
Volume146
Issue number4
DOIs
StatePublished - Oct 1 2024

Funding

This project is mainly funded by the US National Science Foundation under Grant No. OIA-1946231, and the Louisiana Board of Regents through the Louisiana Materials Design Alliance (LAMDA). Partial funding has come from the support of the Louisiana Board of Regents through the Board of Regents Support Fund, Contract No. LEQSF (2023-26)-RD-A-19, Program: R&D, Research Competitiveness Subprogram (RCS).

Keywords

  • Ti-6Al-4V
  • laser powder-bed fusion
  • machine learning
  • melt-pool width
  • meltpool depth
  • optimization
  • sensitivity

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