@ARTICLE{10.21494/ISTE.OP.2019.0428, 

TITLE={A machine learning methodology for decision support in a Computer Aided Manufacturing context}, 
  
AUTHOR={Emeric Ostermeyer, Christophe Danjou, Alexandre Durupt, Julien Le Duigou, }, 
	
JOURNAL={Industrial and Systems Engineering}, 
	
VOLUME={2}, 

NUMBER={Numéro Spécial Lean et industrie du futur }, 
	
YEAR={2019}, 

URL={http://openscience.fr/A-machine-learning-methodology-for-decision-support-in-a-Computer-Aided}, 
	
DOI={10.21494/ISTE.OP.2019.0428}, 
	
ISSN={2632-5993}, 
	
ABSTRACT={The evolution of data mining techniques, as well as the increase in storage and computing capacity, in all areas, is generating interest of the data produced. In this way, manufacturing is no exception. Given the amount of data created when writing the various programs to be played on CNC machines, the application of data mining techniques to capitalize industrialization knowledge is considered. This paper concerns the structuring of an Industrialization Knowledge Base system, able to provide a programmer decision support, based on a corpus of documents relating to the machining of parts produced in the past, and thus assisting him in the production of a new part. The system uses data mining techniques to extract this information and deliver it to the programmer.}}