激光增材制造表面完整性理论与控制技术的研究现状

徐一飞, 刘福朝, 孙耀宁

doi:10.16577/j.issn.1001-1560.2025.0073

材料保护 >

2025 , Vol. 58 >Issue 5: 1 - 11

DOI: https://doi.org/10.16577/j.issn.1001-1560.2025.0073

激光表面改性技术专栏

激光增材制造表面完整性理论与控制技术的研究现状

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新疆大学智能制造现代产业学院

孙耀宁(1977－)，教授，博士生导师，主要从事表面工程、智能制造等，电话:15026000615，E-mail:synxju2024＠163.com

收稿日期: 2025-01-21

修回日期: 2025-02-10

录用日期: 2025-02-15

网络出版日期: 2025-06-10

基金资助

国家自然科学基金(52461022)资助

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Research Status of Laser Additive Manufacturing Surface Integrity Theory and Control Technology

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(School of Intelligent Manufacturing Modern Industry， Xinjiang University， Urumqi 830049， China)

SUN Yaoning (1977－)，Professor，Ph.D.Supervisor，Research Focus:Surface Engineering，Intelligent Manufacturing， etc， Tel.: 15026000615， E-mail: synxju2024＠163.com

Received date: 2025-01-21

Revised date: 2025-02-10

Accepted date: 2025-02-15

Online published: 2025-06-10

Supported by

National Natural Science Foundation of China (52461022)

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摘要

激光增材制造技术虽然具有极高的设计灵活性和制造多功能性，但在应用中其制造的一致性、重复性和质量的可靠性、可预测性难以保证，这就意味着开展表面完整性的理论与控制技术研究，实现以服役性能的精准保证为目标的加工过程定量控制，将成为推动激光增材制造过程精准调控的关键。因此，通过实验和仿真来研究工艺-结构-性能-服役(PSPP)关系是进一步提高增材制造能力的行之有效的策略。本文首先简要概述了激光增材制造技术的分类，并对过程控制进行了描述；然后，讨论了表面微观结构、内部缺陷、表面粗糙度和残余应力如何影响激光增材制造构件的力学和服役性能；并探讨了引入机器学习对PSPP 的非线性过程的影响；最后，讨论了激光增材制造表面完整性控制技术未来的发展趋势。

关键词： 激光增材制造; 表面完整性; 机器学习; 服役性能

本文引用格式

徐一飞, 刘福朝, 孙耀宁 . 激光增材制造表面完整性理论与控制技术的研究现状[J]. 材料保护, 2025 , 58(5) : 1 -11 . DOI: 10.16577/j.issn.1001-1560.2025.0073

Abstract

Although laser additive manufacturing offers exceptional design flexibility and manufacturing versatility， it is difficult to ensure consistency， repeatability， reliability and predicability in practical applications， which means that conducting research on the theory and control technology of surface integrity and achieving quantitative control over the manufacturing process with the aim of accurately guaranteeing service performance has become pivotal for promoting the precise regulation of the laser additive manufacturing process.Consequently， investigating the process-structure-property-performance (PSPP) relationship through experiments and simulations is an effective strategy to further enhance additive manufacturing capabilities.This paper first provided a brief overview of laser additive manufacturing classifications and described its process control.Subsequently， the influences of surface microstructure， internal defects， surface roughness and residual stress on the mechanical and service performance of laser additively manufactured components were discussed.Furthermore，the impact of introducing machine learning on the nonlinear processes of PSPP was explored.Finally， the future development trends in surface integrity control technologies for laser additive manufacturing were discussed.

Key words： laser additive manufacturing; surface integrity; machine learning; service performance

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