An Overview of Technological Parameter Optimization in the Case of Laser Cladding
Source abstract
Abstract
This review examines the methods used to optimize the process parameters of laser cladding, including traditional optimization algorithms such as single-factor, regression analysis, response surface, and Taguchi, as well as intelligent system optimization algorithms such as neural network models, genetic algorithms, support vector machines, the new non-dominance ranking genetic algorithm II, and particle swarm algorithms. The advantages and disadvantages of various laser cladding process optimization methods are analyzed and summarized. Finally, the development trend of optimization methods in the field of laser cladding is summarized and predicted. It is believed that the result would serve as a foundation for future studies on the preparation of high-quality laser cladding coatings.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · Welding Techniques and Residual Stresses · High Entropy Alloys Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410114, China
Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province, China Jiliang University, Hangzhou 310018, China
State Key Laboratory of Tribology in Advanced Equipment, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
Key Laboratory of Vibration and Control of Aero-Propulsion Systems, Ministry of Education, Northeastern University, Shenyang 110819, China
Key Laboratory of Advanced Functional Materials, Ministry of Education, Department of Materials Science and Engineering, Beijing University of Technology, Beijing 100124, China
Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong 999077, China
Funding
National Natural Science Foundation of China · Natural Science Foundation of Hunan Province · Changsha Key Research and Development Project · Changsha Municipal Natural Science Foundation · R&D Program of Beijing Municipal Education Commission · the Open Fund of Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province · Tribology Science Fund of State Key Laboratory of Tribology in Advanced Equipment · the Open Fund of Key Laboratory of Vibration and Control of AeroPropulsion System
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