Technology Category
- Analytics & Modeling - Machine Learning
- Sensors - Accelerometers
Applicable Industries
- Automotive
- Metals
Applicable Functions
- Product Research & Development
- Quality Assurance
Use Cases
- Digital Twin
- Manufacturing Process Simulation
Services
- Testing & Certification
About The Customer
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.
The Challenge
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.
The Solution
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.
Operational Impact
Quantitative Benefit
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