增材修复质量预测模型及工艺优化研究进展

郭伟; 谭孟佳; 薛祯; 王国举; 黄希

doi:10.16490/j.cnki.issn.1001-3660.2026.04.008

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表面技术 ›› 2026, Vol. 55 ›› Issue (4) : 87-101. DOI: 10.16490/j.cnki.issn.1001-3660.2026.04.008

激光表面改性技术

增材修复质量预测模型及工艺优化研究进展

郭伟¹, 谭孟佳², 薛祯¹, 王国举¹, 黄希^1,*

作者信息 +

Research Progress on Quality Prediction Models and Process Optimization for Additive Repair

GUO Wei¹, TAN Mengjia², XUE Zhen¹, WANG Guoju¹, HUANG Xi^1,*

Author information +

文章历史 +

摘要

增材修复工艺参数与修复质量之间存在复杂的映射关系,工艺参数直接影响修复质量,优化工艺参数是调控修复质量的有效方法;建立增材修复质量预测模型旨在揭示工艺参数与修复质量之间的关系,对实现工艺参数优化具有重要意义。基于此,在预测模型方面,对回归分析模型和机器学习模型在增材修复领域的研究现状进行综述,重点阐述机器学习中神经网络、随机森林和支持向量机三种典型的预测模型,并对比分析各模型的优缺点;在工艺优化方面,综述田口法、响应曲面法和机器学习算法在增材修复领域的研究情况,分类阐述响应曲面法中鲍克斯-本肯设计和中心复合设计两种方法,重点分析机器学习算法中遗传算法和粒子群优化算法在增材修复工艺优化中的研究进展。最后对预测模型和优化方法在增材领域的应用现状进行总结,并对其未来发展方向做出展望。

Abstract

A complex mapping relationship exists between process parameters and repair quality in additive repair processes. Process parameters directly affect repair quality, and optimizing these parameters is an effective method for regulating repair outcomes. Establishing predictive models of additive repair quality is crucial for elucidating the relationship between process parameters and repair quality, playing a significant role in achieving process parameter optimization. In additive repair quality prediction, establishing mapping models between process parameters and quality assessment metrics offers a significant advantage over traditional large-scale experimental trials: it substantially reduces sample requirements and lowers experimental costs. Given this context, regarding predictive models, this review summarizes the research status of regression analysis models and machine learning models in the field of additive repair quality prediction. It focuses on elaborating three typical machine learning predictive models: neural networks, random forests, and support vector machines, while also conducting a comparative analysis of their respective advantages and disadvantages. Specifically, multivariate regression models are relatively simple to construct and execute but exhibit limited predictive capability for complex, nonlinear additive repair problems. In recent years, machine learning models have seen broader application within the additive repair domain. The suitability of different machine learning models for additive repair must be selected based on the complexity of the process and data characteristics. Neural network models can be used to learn complex nonlinear relationships. Through deep nonlinear mapping, they can effectively analyze intricate associations, such as those between melt pool dynamics and deposition layer defects (e.g., pores, cracks) in laser cladding. Random forests, requiring no feature scaling or complex preprocessing, can simultaneously handle multidimensional features extracted from images, making them suitable for image detection and classification tasks in wire arc additive repair. Support vector machines maintain stable defect classification performance even under small sample size conditions. Concerning process optimization, this review summarizes research on the Taguchi method, response surface methodology (RSM), and machine learning algorithms within the additive repair field. It categorically elaborates on two RSM design methods: Box-Behnken Design (BBD) and Central Composite Design (CCD). Furthermore, it analyzes the research progress of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) algorithms, as machine learning techniques, in optimizing additive repair processes. The Taguchi method effectively reduces the number of experiments required and rapidly identifies key parameters. However, its global optimization capability is limited for complex additive repair processes involving factors like multi-material interaction or dynamic thermal stress. Response surface methodology can characterize the relationship between process parameters and repair quality and analyze interactions between parameters. Nevertheless, it is less effective in handling multi-objective optimization problems inherent to additive repair. In contrast, machine learning algorithms are well-suited for multi-variable, multi-objective optimization scenarios. They offer advantages such as requiring fewer samples and achieving high precision, making them particularly applicable to additive repair situations characterized by multiple variable constraints and complex coupling effects. Finally, this review summarizes the current application status of predictive models and optimization methods within the additive manufacturing field and provides an outlook on their future research directions.

导出引用

郭伟, 谭孟佳, 薛祯, 王国举, 黄希. 增材修复质量预测模型及工艺优化研究进展[J]. 表面技术. 2026, 55(4): 87-101

GUO Wei, TAN Mengjia, XUE Zhen, WANG Guoju, HUANG Xi. Research Progress on Quality Prediction Models and Process Optimization for Additive Repair[J]. Surface Technology. 2026, 55(4): 87-101

中图分类号： TG44 TP18

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