Optimization of Silicone Rubber Production Process Based on Machine Learning — A Case Study of Shenzhen Xiongyu Rubber Hardware Products Co., Ltd.

Abstract

With the advancement of Industry 3.0, the manufacturing sector is increasingly demanding intelligent and efficient production processes. Silicone rubber, an essential industrial material, plays a vital role in various sectors such as electronics, automotive, and medical industries. Optimizing its production process is crucial for enhancing product quality, reducing production costs, and strengthening corporate competitiveness. This study takes Shenzhen Xiongyu Rubber Hardware Products Co., Ltd. as a case to explore how machine learning technology can be utilized to monitor and optimize the production process of silicone rubber. By collecting and analyzing a large amount of production data and combining it with machine learning algorithms, a production process optimization model has been developed and tested in actual production. The results indicate that the optimization scheme based on machine learning can significantly improve production efficiency and product quality, offering new ideas and methods for the technological upgrading and management innovation of silicone rubber production enterprises. Finally, suggestions for further optimization and future research directions are proposed.