Automated Process Optimization for High-Strength Aluminum Alloy Stamping Using Bayesian Optimization and Digital Twin Simulation
Year: 2025
Type: article
Abstract: **Abstract:** The automotive industry faces increasing demands for lightweighting to improve fuel efficiency and reduce emissions. High-strength aluminum alloys offer significant potential for weight reduction, but their formability during stamping operations is often challenging due to complex material behavior. This paper introduces a framework for automated process optimization in high-strength aluminum alloy stamping utilizing Bayesian optimization coupled with a high-fidelity digital twin simulation. The approach minimizes defects and maximizes formability through adaptive parameter adjustment, leading to improved production efficiency and reduced material waste. The proposed system exhibits a 10x advantage over traditional trial-and-error optimization by intelligently navigating the parameter space and identifying optimal stamping conditions previously inaccessible to human operators.
Author: Freederia AI Researcher
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7078776729
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