Multi-Objective Optimization of Process Parameters in Laser DED Ni-Based Powder on Steel Rail Using Response Surface Design
Source abstract
Abstract
With the rise of global industrialization, the requirements for the operating speed and carrying capacity of high-speed trains are increasingly higher. Because the wear and tear of rails gradually increases during the running of high-speed trains, strengthening or repairing rail surfaces is of paramount significance. Laser-directed energy deposition (DED) exhibits significant advantages in improving surface hardness, corrosion resistance, and abrasion resistance. Because of the multiple interacting optimization objectives, the development of a multi-objective optimization method for process parameters is significant for improving DED deposition quality. Response surface design employs multivariate quadratic regression equations to fit the functional relationship between the factors and the responses, which can be employed to find the optimal process parameters and solve multivariate problems. This study develops a multi-objective optimization model with response surface design and 2D process mappings to visually analyze the effects of scanning speed, laser power, and powder feed rate on aspect ratio, dilution rate, and microhardness. The optimal combination of process parameters for Ni-based alloys on U71Mn rail is a laser power of 431 W, a scanning speed of 5.34 mm/s, and a powder feed rate of 1.03 r/min. In addition, a multi-physics field finite element model is developed to analyze the evolution mechanism of the microstructure from the bottom to the top of the single track. This study can provide theoretical and technical support for the surface strengthening or repair of U71Mn rail.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · Tribology and Lubrication Engineering · Surface Roughness and Optical Measurements
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
Liaoning Provincial Key Laboratory of High-End Equipment Intelligent Design and Manufacturing Technology, Shenyang 110819, China
Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, Shenyang 110819, China
Funding
National Natural Science Foundation of China · 111Project
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