Digital Twin Technology Reduces Waste and Enhances Efficiency in Automotive Manufacturing

• Analytics & Modeling - Machine Learning
• Sensors - Accelerometers

• Automotive
• Metals

• Product Research & Development
• Quality Assurance

• Digital Twin
• Manufacturing Process Simulation

• Testing & Certification

Patrone and Mongiello, a leading tier-one automotive supplier based in Italy, was seeking a solution to enhance the monitoring and control of its sheet metal forming process. The company aimed to improve product quality and reduce production waste. The solution needed to account for sheet metal properties such as stress, strain, and elasticity, and cover equipment operating conditions such as pad force and die friction. The challenge was to find a solution that could accurately simulate the company's existing sheet metal forming process, including machine press and sheet-metal behavior, system variables, and operating conditions.

Patrone and Mongiello is a leading tier-one automotive supplier based in Tito, Potenza, Italy. Founded in 1985, the company has seen consistent growth in its turnover and has invested in premier industrial technologies to support its automotive, agricultural, and general mechanical, cold-metal-forming manufacturing business. The company is committed to improving product quality and reducing production waste, and it sought a solution that could enhance the monitoring and control of its sheet metal forming process.

Patrone and Mongiello selected Altair’s digital twin solution, implemented by Advanced Engineering (AE) Solutions, to meet their challenge. The solution involved creating a comprehensive digital twin that simulated the company’s existing sheet metal forming process. This included the machine press, sheet-metal behavior, system variables, and operating conditions. The digital twin used both simulated data and real data from accelerometers and AutoGrid sensors on the physical machine press. Altair produced the simulated data through finite element analysis (FEA) with Altair® Inspire™ Form. They also ran design-of-experiment (DoE) studies with Altair® HyperStudy® to reveal the effect of the sheet-metal properties and equipment-operation settings on the forming process performance. The team used Altair® romAI™ to create efficient reduced-order models (ROMs) that support AI and enable very fast simulation runtime. The ROMs revealed parameter variation effects on the forming process’s end-product quality. The team then used romAI to train machine learning models with simulated data. All data models were deployed through Altair® Panopticon™, a cloud-based dashboard where forming press operators could visually monitor actual sensor data against expected behavior for key performance indicators (KPIs) and deploy corrective actions throughout the sheet metal forming process.

• The implementation of Altair’s digital twin solution provided Patrone and Mongiello with the ability to monitor quality and make corrections during all stages of its sheet metal forming process. This was achieved by accounting for varying sheet metal material properties and equipment operating conditions. The solution enabled efficient parameter variation that significantly reduced simulation time and allowed teams to monitor the process in a real-time environment. The company was able to immediately leverage the new process to supply axle attachment brackets to a leading automotive manufacturer. The ability to monitor and correct the process in real-time has significantly improved the company's operational efficiency and product quality.

• Reduced production waste by more than 15%
• Reduced simulation time from hours to seconds
• Enabled real-time monitoring of the process