Application of machine learning algorithm in the sheet metal industry: an exploratory case study

This study solved a practical problem in a case in the sheet metal industry using machine learning and deep learning algorithms. The problem in the case company was related to detecting the minimum gaps between components, which were produced after the punching operation of a metal sheet. Due to the narrow gaps between the components, an automated sheer machine could not grip the rest of the sheet skeleton properly after the punching operation. This resulted in some of the scraped sheet on the worktable being left behind, which needed a human operator to intervene. This caused an extra trigger to the production line that resulted in a break in production. To solve this critical problem, the relevant images of the components and the gaps between them were analyzed using machine learning and deep learning techniques. The outcome of this study contributed to eliminating the production bottleneck by optimizing the gaps between the punched components. This optimization process facilitated the easy and safe movement of the gripper machine and contributed to minimizing the sheet waste.