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

GUO Wei; TAN Mengjia; XUE Zhen; WANG Guoju; HUANG Xi

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

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

Laser Surface Modification Technology

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

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

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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.

Key words

additive repair / quality prediction / process optimization / machine learning algorithm

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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

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