{"title":"Relationship between Hardness and Deformation during Cold Rolling Process of Complex Profiles","authors":"Dawei Zhang, Linghao Hu, Bingkun Liu, Shengdun Zhao","doi":"10.1186/s10033-023-00950-1","DOIUrl":null,"url":null,"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., $$\\varepsilon < 1.2$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>ε</mml:mi> <mml:mo><</mml:mo> <mml:mn>1.2</mml:mn> </mml:mrow> </mml:math> ) of training samples, and the correlation relationship between strain and hardness out strain range (i.e., $$\\varepsilon > 1.2$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>ε</mml:mi> <mml:mo>></mml:mo> <mml:mn>1.2</mml:mn> </mml:mrow> </mml:math> ) 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 $$\\varepsilon < 1.2$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>ε</mml:mi> <mml:mo><</mml:mo> <mml:mn>1.2</mml:mn> </mml:mrow> </mml:math> and power function with a fitting constant in the range $$\\varepsilon > 1.2$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>ε</mml:mi> <mml:mo>></mml:mo> <mml:mn>1.2</mml:mn> </mml:mrow> </mml:math> ; and the predicting error of the regression model by segmented function is less than 10%.","PeriodicalId":10115,"journal":{"name":"Chinese Journal of Mechanical Engineering","volume":"69 1","pages":"0"},"PeriodicalIF":4.5000,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chinese Journal of Mechanical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s10033-023-00950-1","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0
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%.
期刊介绍:
Chinese Journal of Mechanical Engineering (CJME) was launched in 1988. It is a peer-reviewed journal under the govern of China Association for Science and Technology (CAST) and sponsored by Chinese Mechanical Engineering Society (CMES).
The publishing scopes of CJME follow with:
Mechanism and Robotics, including but not limited to
-- Innovative Mechanism Design
-- Mechanical Transmission
-- Robot Structure Design and Control
-- Applications for Robotics (e.g., Industrial Robot, Medical Robot, Service Robot…)
-- Tri-Co Robotics
Intelligent Manufacturing Technology, including but not limited to
-- Innovative Industrial Design
-- Intelligent Machining Process
-- Artificial Intelligence
-- Micro- and Nano-manufacturing
-- Material Increasing Manufacturing
-- Intelligent Monitoring Technology
-- Machine Fault Diagnostics and Prognostics
Advanced Transportation Equipment, including but not limited to
-- New Energy Vehicle Technology
-- Unmanned Vehicle
-- Advanced Rail Transportation
-- Intelligent Transport System
Ocean Engineering Equipment, including but not limited to
--Equipment for Deep-sea Exploration
-- Autonomous Underwater Vehicle
Smart Material, including but not limited to
--Special Metal Functional Materials
--Advanced Composite Materials
--Material Forming Technology.