Abstract
Sheet metal coils are widely used in the steel, automotive, and electronics industries. Many of these coils are processed through metal stamping or laser cutting to form different types of shapes. Sheet metal coil leveling is an essential procedure before any metal forming process. In practice, this leveling procedure is now executed by operators and primarily relies on their experience, resulting in many trials and errors before settling on the correct machine parameters. In smart manufacturing, it is required to digitize the machine’s parameters to achieve such a leveling process. Although smart manufacturing has been adopted in the manufacturing industry in recent years, it has not been implemented in steel leveling. In this paper, a novel leveling method for flatness leveling is proposed and validated with data collected by flatness sensors for measuring each roll adjustment position, which is later processed through the multi-regression method. The regression results and experienced machine operator results are compared. From this research, not only can the experience of the machine operators be digitized, but the results also indicate the feasibility of the proposed method to offer more efficient and accurate machine settings for metal leveling operations.
Highlights
The metal forming industry is considered the main part of mass production in the automotive, electronics, telecommunication, and construction industries
Different machine learning methods such as random forest regression and decision tree regression can be applied to this application and compared with the MLR method used in this research for further accuracy improvement
A parametric study on lateral leveling with the adjustment of backup sets in the model industry leveling machine was achieved with the MLR machine learning method
Summary
The metal forming industry is considered the main part of mass production in the automotive, electronics, telecommunication, and construction industries. The leveling process depends on the coil’s thickness, width, tensile strength, yield strength, outer diameter, inner diameter, and the line speed of the production line It is often through experienced machine operators with many years. Zhong et al [17] identified that the machine learning method can be used for reliability analyses and decision support systems through the use of regression analysis to predict the interval generation framework. Liang et al [24] proposed a novel multi-objective particle swarm optimization (MOPSO) algorithm employing the Gaussian process regression (GPR)-based machine learning (ML) method for multi-variable, multi-level optimization problems with multiple constraints. Multiple regression algorithms are employed to predict the output values based on input features from the data set input into the leveling system. It is found that greater improvement can be reached with the proposed method with regression analysis when compared with trial and error based on the experienced operator
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