Our client is a large rubber company, one of the top ten tire companies in the world. It owns more than 38,000 stores and is exported to 160 countries and regions.
The challenge is the high rubber defective rate.
Our solution is the production line process is optimized, and the AI algorithm recommends the best production parameters to help customers improve the yield rate.
The energy consumption and defective rate of enterprises in the process of rubber mixing (the core part of rubber production) are greatly affected by raw materials and the production environment, resulting in large fluctuations in overall production efficiency, difficult production cost control, and high energy consumption and defective rate;
The intensive training process relies heavily on manual experience and is difficult to pass on.
We construct an intelligent analysis model between the glue discharge time (including temperature, pressure, power, etc.) and the rubber inspection results;
The system then will recommend the optimal process parameters for key control such as the duration and temperature of the ban burying in the real-time production line.
Improvement in rubber refining qualification rate
Annual profit increased