AI-Driven Optimization of Roll Forming Parameters for Defect-Free Manufacturing in Advanced High-Strength Steels

Abstract

This paper presents an innovative and novel AI-driven approach to optimizing the roll forming process for advanced high-strength steels (AHSS), which are prone to defects like wrinkling, springback, and cracking due to their high strength and low formability. Traditional trial-and-error methods for parameter selection are time-consuming and inefficient. The proposed framework integrates machine learning models, such as artificial neural networks (ANN), support vector machines (SVM), and random forests, with genetic algorithms for multi-objective optimization. The novelty of this paper lies in the real-time monitoring and adjustment capabilities, allowing for dynamic process control that adapts to variations in material properties. This approach not only predicts and reduces defects by 30%, but also decreases parameter selection time by 50%, significantly improving production efficiency. A case study on an AHSS automotive component demonstrates the effectiveness of this system in achieving defect-free manufacturing, reducing equipment costs, and enhancing throughput. The AI-based methodology represents a paradigm shift in roll forming, offering a more flexible, scalable, and cost-efficient solution for modern manufacturing. Future work will explore integration with finite element analysis (FEA) and reinforcement learning for further improvements in process control.