• M. Vincent CHEUTET, Professor, INSA Lyon - DISP
  
  
• M. Laurent TAPIE, Assistant professor, University Paris 13 - URB2I
  
  
• M. William DERIGENT, Assistant professor, University of Lorraine - CRAN
  
  
• M. Benoit FURET, Professor, University of Nantes - LS2N
  
  
• Mrs Marija JANKOVIC, Professor, CentraleSupelec - LGI
  
  
• M. Bernard HAUTBERGUE, R&D, NCSIMUL, Hexagon Manufacturing Intelligence

In response to competition and new industrial challenges, companies are forced to be more and more efficient, productive and competitive. Managing industrial know-how and the data flow of the manufacturing digital chain must be explored in order to shorten the industrialization time while ensuring better quality.

In this context, this thesis focuses on digital chain data exploration for the capture of good practices in NC machining using a feature-based approach. Several issues related to machining feature characterization and digital chain data exploitation for machining process knowledge reuse have been identified.

To address these issues, a new characterization of multi-level complex machining feature has been proposed. The proposed approach is characterized by a hierarchical structuring of digital chain data and a mapping between the geometrical and machining data. A statistical analysis is then carried out to analyse and exploit this data. Curvature-based segmentation and statistical clustering of machining data were combined to define new machining regions based technological segmentation approach. These regions were then used to characterize the machining feature and thus ensure the reuse of machining data through a feature based and a region based approach exploiting similarity measures a similarity measure. Finally, the developed approach was applied on an industrial case in aeronautics.