University of Belgrade
Assembly Line
Manufacturing process optimization for real-time quality control in multi-regime conditions: Tire tread production use case
The high-stake nature of most manufacturing processes empowers the importance of real-time quality control and assurance. In the event of a failure in production, a decision-making process can be time-consuming for the human and prevent timely actions. The agility can be boosted with a decision-support system based on artificial intelligence. Particularly, multi-objective process optimization can be employed to select the optimal control settings in real-time, and thus enhance relevant key performance indicators, concurrently.
Based on process and quality parameters being streamed from the production plant in real-time, the optimizer can act in timely critical and quality-threatening situations and generate immediate corrective actions. The multi-regime operation of the plant and design space dimensionality can impact the convergence rate and add to execution time. Therefore, production regimes recognition and greedy search of suffix tree-based models of the process have been engaged, aiding in a better-focused and faster space search at an early phase of the algorithm run.
Beyond simply reviewing the outputs, the user can leave feedback, which is utilized by the optimizer’s reinforcement learning mechanisms. Validated in this real-world scenario, the solution produced a rise from 81.83% to 90.91% in the tread quality.