Sourish GHOSH will defend his thesis on Monday, March 23, 2026, at the Arts et Métiers campus Arts et Métiers . His work at the MSMP laboratory focuses on "Artificial intelligence-assisted surface metrology for machining: an integrated approach to evaluating surface quality in machines."
Title
"Artificial intelligence-assisted surface metrology for machining: an integrated approach to surface quality assessment in machines"
Jury
• Ms. Caroline RICHARD, Professor, University of Tours – Rapporteur
• Mr. Abdelali OUDRISS, Professor, Mines Paris PSL - Rapporteur
• Mr. Arnaud GOTLIEB, Professor, Simula Research Laboratory – Examiner
• Mr. Mohamed EL MANSORI, University Professor, ENSAM – Examiner
• Mr. Ricardo KNOBLAUCH, Associate Professor, ENSAM – Examiner
Thesis summary
Surface quality is a determining factor in the performance and reliability of machined components. As production systems evolve towards "Zero Defect" strategies and in-process control, it becomes essential to link machining behavior, surface formation, and measurement reliability within a coherent framework. This doctoral work proposes an integrated methodology combining cutting mechanics, signal analysis, and artificial intelligence to predict and regulate surface quality during machining. Interpretable AI models are developed to establish the link between signals, machining parameters, and surface roughness, thus forming the basis for a proactive quality control framework. At the same time, multiscale analysis techniques are introduced to characterize texture evolution beyond conventional roughness indicators, while AI-based approaches are explored to improve the robustness of non-contact optical measurements. The objective of this thesis is to contribute to the development of intelligent and reliable manufacturing systems by enabling earlier detection of surface degradation and supporting data-driven decision-making in machining processes.
