This three-year, $349,999 National Science Foundation project grant supports research at Case Western Reserve University to develop specific energy-based prognosis models for machining surface integrity through integration of process physics and machine learning. Funded under the NSF Directorate for Engineering's Engineering Grants program (CFDA 47.041), the research aims to foster innovation in engineering by improving prediction of machined surface conditions to enable more efficient manufacturing processes. The university researchers will integrate machining process models with machine learning to develop prognostic tools for real-time optimization and quality control across a range of machining applications. Outcomes from this collaboration seek to advance American manufacturing competitiveness and economic strength through engineering innovation.
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