Typical applications of Artificial Intelligence Surrogate Models in Laser Metal Additive Manufacturing

DENG Aolin; FAN Enxiang

doi:10.7512/j.issn.1001-2303.2026.04.04

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Typical applications of Artificial Intelligence Surrogate Models in Laser Metal Additive Manufacturing
Vol. 56, Issue 4, Pages: 31-42(2026)
Received：20 December 2025，

Revised：2026-02-03，

Published：20 April 2026
DOI： 10.7512/j.issn.1001-2303.2026.04.04
稿件说明：

移动端阅览

邓奥林，樊恩想.人工智能代理模型在激光金属增材中的典型应用［J］.电焊机，2026，56（4）：31-42. DOI： 10.7512/j.issn.1001-2303.2026.04.04.

DENG Aolin, FAN Enxiang.Typical applications of Artificial Intelligence Surrogate Models in Laser Metal Additive Manufacturing[J].Electric Welding Machine, 2026, 56(4): 31-42. DOI： 10.7512/j.issn.1001-2303.2026.04.04.

摘要

增材制造技术以其高度的几何自由度和定制化能力，在航空航天、汽车、生物医疗及高端装备等领域展现出巨大的应用潜力。然而，对于金属增材制造而言，如何高效地设计出性能优异且满足特定功能需求的结构，并准确预测其在复杂工况下的力学行为和服役性能，仍是制约其进一步迈向大规模产业化的关键瓶颈。本文对近年来基于数据驱动和物理机理约束的人工智能代理模型在激光金属增材中的应用研究进行了简要综述，主要包括其在结构设计优化、宏观性能快速预测、工艺参数-微观组织-宏观性能关联分析等方面的典型研究应用，并针对目前激光金属增材与人工智能相结合领域的痛点及应对策略给出了一些建议，旨在为相关领域的研究人员和工程技术人员提供有价值的参考和启示，推动金属增材制造向更智能、更高效、更可靠的方向发展。

Abstract

Additive manufacturing (AM) has demonstrated substantial application potential in aerospace

automotive

biomedical

and high-end equipment industries

owing to its exceptional geometric freedom and customization capabilities. However

for metal additive manufacturing

the efficient design of high-performance structures that satisfy specific functional requirements

along with the accurate prediction of their mechanical behavior and service performance under complex working conditions

remains a critical bottleneck hindering its widespread industrial adoption. This paper presents a concise review of recent research on artificial intelligence surrogate models

constrained by both data-driven approaches and physical mechanisms

in laser-based metal additive manufacturing. The review primarily covers typical applications in structural design optimization

rapid prediction of macroscopic properties

and correlation analysis among process parameters

microstructure

and macroscopic properties. Furthermore

several recommendations are provided regarding the current challenges and corresponding strategies in the integration of laser metal additive manufacturing with artificial intelligence. The objective is to offer valuable insights and references for researchers and engineers in related fields

thereby promoting the development of metal additive manufacturing toward greater intelligence

efficiency

and reliability.

关键词

Keywords

references

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