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Challenges and opportunities in machine learning for metal additive manufacturing: data scarcity and interpretability
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DOI：10.1016/j.mattod.2026.103371.png)
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Abstract
En 中文
Metal additive manufacturing (MAM) offers significant advantages in near-net shape production of complex parts. However, the intricate relationship between process parameters, microstructure, and mechanical properties makes quality control challenging. Machine learning (ML) offers a powerful framework to address this issue. Data scarcity remains the primary bottleneck that restricts the application of ML in MAM. We have conducted a comprehensive exploration of the issues and solutions related to data scarcity in MAM and discussed various techniques for data generating, sampling, and fusion. These range from high-throughput experiments and simulations to advanced methods such as active learning and multi-fidelity fusion technology. In addition, we address the interpretability challenge in MAM by highlighting the trade-off between data, accuracy, and physical consistency. By classifying methods of domain knowledge integration, we provide a systematic guide to their advantages and constraints in enhancing model reliability. Finally, a roadmap for ML in MAM is proposed, highlighting the synergy between knowledge graph-driven RAG agents, high-fidelity digital twins, and embodied intelligence. This path aims to achieve autonomous manufacturing through self-improving closed-loop systems, accelerating the transition to fully intelligent, data-driven production.
Keywords:
machine learning
metal additive manufacturing
data scarcity
interpretability
digital twins
Journal
M
IF:
22
Papers:
279
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0
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Cited Papers