Data-Driven Differential Evolution in Tire Industry Extrusion: Leveraging Surrogate Models

TL;DR

This paper introduces a surrogate-based, data-driven optimization method using machine learning and metaheuristics to improve tire extrusion processes, significantly reducing waste and setup time without explicit mathematical models.

Contribution

It develops a novel surrogate-assisted differential evolution approach tailored for complex industrial processes lacking explicit objective functions.

Findings

Achieved 65% reduction in initialization and setup time.

Significantly minimized material waste.

Outperformed traditional configurations in process optimization.

Abstract

The optimization of industrial processes remains a critical challenge, particularly when no mathematical formulation of objective functions or constraints is available. This study addresses this issue by proposing a surrogate-based, data-driven methodology for optimizing complex real-world manufacturing systems using only historical process data. Machine learning models are employed to approximate system behavior and construct surrogate models, which are integrated into a tailored metaheuristic approach: Data-Driven Differential Evolution with Multi-Level Penalty Functions and Surrogate Models, an adapted version of Differential Evolution suited to the characteristics of the studied process. The methodology is applied to an extrusion process in the tire manufacturing industry, with the goal of optimizing initialization parameters to reduce waste and production time. Results show that the surrogate-based optimization approach outperforms historical best configurations, achieving a 65\% reduction in initialization and setup time, while also significantly minimizing material waste. These findings highlight the potential of combining data-driven modeling and metaheuristic optimization for industrial processes where explicit formulations are unavailable.