IMTS 2024 Conference: AI-assisted Feedback to Sheet Metal Stamping Processes for Automotive Applications

Monday September 9 10:00 AM CST
IMTS02 Room W192-B

About the presentation
Sheet metal stamping processes are widely used in various industries due to their cost-effectiveness, rapid cycle times, mass production capabilities, and relatively high precision. The quality and efficiency of these processes hinge on several critical factors, including material properties, blank dimension and uniformity, die geometry, and processing parameters such as binder force, lubricants, and spacer utilization. To enhance production efficiency, process simulation tools are commonly employed.

A typical optimization procedure for manufacturing requires an iterative process involving parameter setting, execution of computational simulations, and modifying the parameters. The entire process demands substantial computational time, making it impractical for real-time feedback towards rapid corrective actions required for in-line control for running production processes.

To overcome this challenge, artificial intelligence (AI) can be leveraged to determine optimal manufacturing parameters within a single manufacturing cycle time. This research proposes an in-line optimization framework incorporating a trained AI model to predict kidney-shaped die forming. Preliminary results indicate that the AI framework can accurately predict draw-in values based on a given parameter set, a process referred to as forward prediction. Furthermore, the AI framework can also predict the optimal parameter set that leads to the desired draw-in values, referred to as backward prediction.

This research is performed in collaborations among ORNL, AutoForm, and USCAR (US Council for Automotive Research). The members of USCAR are Ford, GM, and Stellantis.

Meet your presenter
Kidambi Kannan is with AutoForm Engineering USA, Inc. as technical specialist. Following a Ph.D. in Materials Science and Engineering, University of Maryland, College Park, Kidambi joined EASi Engineering as a project engineer, and eventually served as project manager for sheet metal forming projects. He joined AutoForm in 2002 as technical manager, and has been intimately involved in the rapid expansion of AutoForm over the past 20 years in the global sheet metal industry.