Technologic parameter optimization of gas quenching process using response surface method

• Published in: Computational materials science Vol. 38; no. 4; pp. 561 - 570
• Main Authors: Huiping, Li, Guoqun, Zhao, Shanting, Niu, Yiguo, Luan
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.02.2007; Elsevier Science
• Subjects: Cold working, work hardening; annealing, quenching, tempering, recovery, and recrystallization; textures; Cross-disciplinary physics: materials science; rheology; Exact sciences and technology; Gas quenching; Materials science; Optimization; Physics; Response surface method; Technological parameter; Treatment of materials and its effects on microstructure and properties; Technological parameter; Gas quenching; Response surface method; Optimization; Finite element method; Time dependence; Surface hardness; Lagrange multiplier; Regression model; Mathematical models; Heat transfer; Quenching
• ISSN: 0927-0256; 1879-0801
• DOI: 10.1016/j.commatsci.2006.03.014

A step function model with time is presented in the paper, and an axisymmetric component is regarded as the study objective in this model. The heat transfer coefficient during the gas quenching process is described as a function of time in this model, and five design variables are selected to do the design of Box–Behnken experiment with five factors and three levels. The levels of design variables that attain from the result of Box–Behnken experiment design are regard as the technical parameters of gas quenching to simulate the gas quenching process using the FEM software developed in the paper. Some mathematical models of response surface are gained by the mixed regression method and response surface method. These mathematical models show the dependencies of distortion, surface average equivalent residual stress, standard deviation of equivalent residual stress, average surface hardness and standard deviation of surface hardness with respect to the design variables. The optimization model is presented with the distortion as the optimization objective, and the model is optimized with an upper limit, a lower limit and the constraint function by the non-linear method and the Lagrange multiplier method.