The process parameter optimization of laser cladding using a bio-inspired algorithm is a hot issue and attracts the attention of many scholars. The biggest difficulty, at present, is the lack of accurate information regarding the function relationship between objectives and process parameters. In this study, a novel process parameter optimization approach for laser cladding is proposed based on a multiobjective slime mould algorithm (MOSMA) and support vector regression (SVR). In particular, SVR is used as a bridge between target and process parameters for solving the problem of lacking accurate information regarding the function relationship. As a new metaheuristic algorithm, MOSMA is to obtain the Pareto solution sets and fronts. The Pareto solution sets are optimized process parameters, and the Pareto fronts are optimized objectives. Users can select the corresponding optimized process parameters according to their needs for the target. The performance of the proposed approach was evaluated by the TOPSIS method, based on actual laser cladding data and compared with several well known approaches. The results indicate that the optimal process parameters obtained by the proposed approach have better process performance.

This study investigates the wear performance of 20CrMnTi steel, a commonly used material for spiral bevel gears, after heat treatment, with a focus on the microstructural evolution and wear behavior in both the surface and gradient direction of the carburized layer. The results show that the microstructure composition in the gradient direction of the carburized layer gradually transitions from martensite and residual austenite to a martensite–bainite mixed structure, and eventually transforms to fully bainitic in the matrix. With the extension of carburizing time, both the effective carburized layer depth and the hardened layer depth significantly increase. Wear track morphology analysis reveals that the wear track depth gradually becomes shallower and narrower, and the wear rate increases significantly with increasing load. However, the friction coefficient shows little sensitivity to changes in carburizing time and load. Further investigations show that as the carburized layer depth increases, the carbon concentration and hardness of the samples gradually decrease, resulting in an increase in the average wear rate and a progressive worsening of wear severity. After the wear tests, different depths of plowing grooves, spalling, and fish-scale-like features were observed in the wear regions. Additionally, with the increase in load and carburized layer depth, both the width and depth of the wear tracks significantly increased. The research results provide a theoretical basis for optimizing the surface carburizing process of 20CrMnTi steel and improving its wear resistance.

The uniformity of microstructure and wear properties exist in the T15 coating for the laser cladding on 42CrMo steel. It can be improved by a post-heat treatment process. Temperature ranges from 1100 to 1240 °C were applied on the cladding layer to investigate the effect of the heat treatment on the wear resistance and hardness gradient. The post-heat treatment can efficiently improve the inhomogeneity of microstructure. The lower wear rate is obtained after the quenching process at 1100 °C, and the wear rate is increased though the tempering process. The carbides at the grain boundary are decomposed and integrated into the matrix during the high temperature quenching process. The carbides are precipitated and dispersed in the grain during the tempering process. The content of martensite and alloy carbide is significantly increased through the heat treatment process. The microhardness of the cladding layer is 910 HV after quenching and 750 HV after tempering. The wear mechanism of the cladding layer is mainly abrasive wear and fatigue wear. The crack and falling off from cladding layers are significantly reduced for the quenching–tempering process.

Laser cladding is one of the critical technologies for additive manufacturing and rapid repair. Improving cladding performance by materials and process parameters is the leading research direction, but defects and instability of quality in the cladding process are inevitable. Therefore, it is necessary to study which factors are related to quality. In this paper, a new detection method is proposed to measure the radiation intensity of the reflected laser, laser scanning displacement, and temperature of the substrate while cladding. The characteristic values corresponding to the position of the cladding spots are extracted, the cladding quality is preliminarily evaluated and graded, and the correlation between them is verified with the method of machine learning nu-SVM. The results show that the accuracy of the model trained by 300 groups of data to predict the quality grades is 78.74%, which indicates that there is a strong correlation between these process variables and the cladding quality, and this method is feasible for the quality evaluation and control of the cladding process.