A thermal field FEM of titanium alloy coating on low-carbon steel by laser cladding with experimental validation

材料科学 涂层 微观结构 钛合金 有限元法 包层(金属加工) 激光器 碳钢 温度梯度 热的 复合材料 合金 冶金 光学 结构工程 热力学 腐蚀 物理 工程类 量子力学
作者
Sizhi Zuo‐Jiang,Hongying Yu,Xuzhou Jiang,Wei Gao,Dongbai Sun
出处
期刊:Surface & Coatings Technology [Elsevier]
卷期号:452: 129113-129113 被引量:13
标识
DOI:10.1016/j.surfcoat.2022.129113
摘要

Numerical simulation is an efficient method to study the laser cladding process via reconstructing the thermal field and analyzing the evolution of the microstructures. In this work, a novel 3D finite element model (FEM) of the laser cladding process is proposed for materials with significantly different thermo-physical properties, for example, a titanium alloy (TA1) coating on an iron (Q235) substrate. Multiple practical factors, like the coating geometry variation, powder preheating and laser energy attenuation, were considered in this model to ensure its high accuracy. For different laser scanning speeds, the transient temperature contours and their variations against time were simulated, and the calculation errors of the highest temperatures are <2 %. Based on the comparison of the binary phase (TiFe) diagram with the chemical composition of the coating, a new criterion, called the Tm criterion, was established for the coating solidification. Then, the solidification parameters of the moving solid-liquid interface, like the cooling rate, solidification rate and G/R (temperature gradient to solidification rate) ratio, were calculated to predict and analyze the solidification process and microstructure evolution of the coating. Finally, the simulation results were compared with the physical experiments, which proved the validity of the FEM and the rationality of the Tm criterion. Based on the numerical simulation and predicted microstructure evolution, this research provides an accurate, applicable and powerful analytical method for the laser cladding process with different materials, which can substantially improve the time and cost efficiencies of the process optimization.
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