Industry 4.0 in Automotive Manufacturing: A Digital Twin Approach

This study introduces a Digital Twin framework for real-time monitoring and decision-making in automotive strut tower manufacturing via automated high-pressure die casting. As a key enabler of Industry 4.0, the Digital Twin concept integrates physical and digital domains, allowing bi-directional communication for process optimization. The proposed model collects real-time data from sensors in the manufacturing cell and utilizes FlexSim simulation software alongside machine learning algorithms, specifically Artificial Neural Networks (ANN) and Logistic Regression, to classify product outcomes. The ANN achieved high accuracy, supporting rapid, data-driven decision-making. Additionally, FlexSim’s Emulation tool and Open Platform Communications United Architecture (OPC UA) protocol enable seamless communication between the physical and digital systems, ensuring consistent and reliable performance. Results demonstrate the effectiveness of the Digital Twin in enhancing manufacturing efficiency and predictive accuracy, highlighting its potential for broader applications across various manufacturing stages and industries.