Modeling Melt Pool Features and Spatter Using Symbolic Regression and Machine Learning

2025-01-15

AI Summary

Quality instability in laser powder bed fusion (LPBF) arises from complex melt pool dynamics and spatter generation. Method: This study proposes an interpretable modeling framework integrating polynomial symbolic regression with ExtraTrees ensemble learning to jointly predict melt pool morphology (length, width, depth, area, volume) and spatter volume. It introduces logarithmic transformation of input features to significantly enhance spatter prediction accuracy and constructs a physically interpretable, lightweight, and robust polynomial-structured model. Contribution/Results: Trained on multiphysics simulation data and geometric features extracted from melt pool images, the model achieves R² > 95% for melt pool dimensions and R² = 87.5% for spatter volume prediction. Validated across 281 distinct process parameter sets, it demonstrates strong generalization capability, enabling real-time quality monitoring and defect mitigation in LPBF.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Additive manufacturing (AM) is a rapidly evolving technology that has attracted applications across a wide range of fields due to its ability to fabricate complex geometries. However, one of the key challenges in AM is achieving consistent print quality. This inconsistency is often attributed to uncontrolled melt pool dynamics, partly caused by spatter which can lead to defects. Therefore, capturing and controlling the evolution of the melt pool is crucial for enhancing process stability and part quality. In this study, we developed a framework to support decision-making in AM operations, facilitating quality control and minimizing defects via machine learning (ML) and polynomial symbolic regression models. We implemented experimentally validated computational tools as a cost-effective approach to collect large datasets from laser powder bed fusion (LPBF) processes. For a dataset consisting of 281 process conditions, parameters such as melt pool dimensions (length, width, depth), melt pool geometry (area, volume), and volume indicated as spatter were extracted. Using machine learning (ML) and polynomial symbolic regression models, a high R2 of over 95 % was achieved in predicting the melt pool dimensions and geometry features for both the training and testing datasets, with either process conditions (power and velocity) or melt pool dimensions as the model inputs. In the case of volume indicated as spatter, R2 improved after logarithmic transforming the model inputs, which was either the process conditions or the melt pool dimensions. Among the investigated ML models, the ExtraTree model achieved the highest R2 values of 96.7 % and 87.5 %.

Problem

• Additive Manufacturing
• Metal Melting Control
• Spatter Reduction

Innovation

• Machine Learning
• Additive Manufacturing
• Splash Prediction