1. Relationship between Hardness and Deformation during Cold Rolling Process of Complex Profiles
- Author
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Dawei Zhang, Linghao Hu, Bingkun Liu, and Shengdun Zhao
- Subjects
Complex profile ,Cold rolling ,Multi passes ,Equivalent strain ,Vickers hardness ,Ocean engineering ,TC1501-1800 ,Mechanical engineering and machinery ,TJ1-1570 - Abstract
Abstract The hardening on surface of complex profiles such as thread and spline manufactured by cold rolling can effectively improve the mechanical properties and surface quality of rolled parts. The distribution of hardness in superficial layer is closely related to the deformation by rolling. To establish the suitable correlation model for describing the relationship between strain and hardness during cold rolling forming process of complex profiles is helpful to the optimization of rolling parameters and improvement of rolling process. In this study, a physical analog experiment reflecting the uneven deformation during complex-profile rolling process has been extracted and designed, and then the large date set (more than 400 data points) of training samples reflecting the local deformation characteristics of complex-profile rolling have been obtained. Several types of polynomials and power functions were adopted in regression analysis, and the regression correlation models of 45# steel were evaluated by the single-pass and multi-pass physical analog experiments and the complex-profile rolling experiment. The results indicated that the predicting accuracy of polynomial regression model is better in the strain range (i.e., $$\varepsilon < 1.2$$ ε < 1.2 ) of training samples, and the correlation relationship between strain and hardness out strain range (i.e., $$\varepsilon > 1.2$$ ε > 1.2 ) of training samples can be well described by power regression model; so the correlation relationship between strain and hardness during complex-profile rolling process of 45# steel can be characterized by a segmented function such as third-order polynomial in the range $$\varepsilon < 1.2$$ ε < 1.2 and power function with a fitting constant in the range $$\varepsilon > 1.2$$ ε > 1.2 ; and the predicting error of the regression model by segmented function is less than 10%.
- Published
- 2023
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