Welcome to Fego!

Our #MISULAB software for predicting residual stresses in machining continues to be deployed among major industrial stakeholders. A key factor for the precision of the calculations lies in the quality of the software's input data, particularly the workmaterial behavior model. The scientific literature presents various material behavior models for simulating the machining of standard materials (such as 316L stainless steel, Ti-6Al-4V titanium, etc.). However, these data are often calibrated for loading conditions (plastic strain , strain rates, temperatures) that are not suitable for machining, where materials are subjected to plastic strain greater than 5, strain rates up to 10,000 s⁻¹, and simultaneously to temperatures reaching several hundred degrees. Additionally, the metallurgical and mechanical state of materials used in production may differ from laboratory-grade materials (due to work hardening, heat treatment, etc.). Consequently, based on the data from the literature, it is often essential to perform an inverse identification of the material model parameters using experimental data obtained from machining (cutting forces, chips, etc.) of industrial parts with the corresponding metallurgical state relevant to the industrial context.

To address this scientific and industrial challenge, we are pleased to welcome Oghenefegor Ugbine, a master's intern under the #Erasmus Mundus Joint Master meta 4.0 program at #Centrale Lyon ENISE. We are collaborating with Renaud FERRIER from the École des Mines de Saint-Étienne.