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DOE OSTI · 2462877

Data Visualization and Analytics for Optimal Process Parameter Selection in Turning

Karandikar, Jaydeep·Chae, Junghoon·Gomez, Michael·Goettler, Jamie
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Abstract

The objective of this project is to research physics-guided machine learning methods to recommend optimal tools and machining process parameters for turning applications using the MSC test database. For a given turning application, the MSC metalworking specialist needs to make decisions on tools and the associated process parameters for the MSC customer. For a given material, there are many alternatives for tools and a wide range of process parameters to consider. MSC has built a database of tools and parameters for different applications from the historical turning tests completed at various customer sites. The research project aims to use machine learning methods to predict optimal tools and process parameters for the MSC metalworking specialists using the MSC test database. This enables continuous learning of optimal tool and process parameters for different applications as new information is collected from testing. Through MSC, this information can be shared with machining shops across the US leading to improved productivity and efficiency.

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Karandikar, Jaydeep, Chae, Junghoon, Gomez, Michael, Goettler, Jamie. 2024-05-01. Data Visualization and Analytics for Optimal Process Parameter Selection in Turning. https://doi.org/10.2172/2462877

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