Regression Approach to a Novel Lateral Flatness Leveling System for Smart Manufacturing

• Main Authors: Sung-Yu Tsai, Jen-Yuan Chang
• Format: Article
• Language: English
• Published: MDPI AG 2021-07-01
• Series: Applied Sciences
• Subjects: coil leveling; roller leveling; sheet metal coil; regression; lateral leveling
• Online Access: https://www.mdpi.com/2076-3417/11/14/6645

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.