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. However, process optimization in manufacturing scenarios has never been an easy task, due to the complexity, non-convexity, and non-linearity of dependences among process parameters and physical constraints typical for strict production procedures. Precise and high-performative digital replicas of physical systems are required to simulate different scenarios. Physical models are computationally demanding for real-time applications and are usually hard to develop. In that light, this paper brings a novel solution based on multi-objective evolutionary optimization coupled with process surrogate data-driven models, in charge of predicting the relevant process responses. 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. The process of tire tread production has served as the playground for methodology design and implementation. Validated in this real-world scenario, the solution produced a rise from 81.83% to 90.91% in the tread quality. Thanks to its generic and modular nature, the methodology is applicable to various industrial cases, with the potential to enhance their efficiency and ensure high-quality output.