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Toward an Extensive Data Integration to Address Reverse Engineering Issues

Abstract : Mechanical Reverse Engineering has been getting increasingly more attention from the industry. It aims rebuilding a broad Digital Mock Up (DMU) in order to redesign and/or remanufacture a product. Some of the reverse engineering challenges are to perform an efficient knowledge extraction out of the original product, and then to process the data it and consolidate them for further analysis. These data could be extracted from a vast number of different data as such as Manufacturing Data, Technical Reports, Design Data (e.g. CAD Files, technical drawings, etc.), Quality procedures, etc. Moreover, the amount of data stored by the companies’ information system keep on rapidly growing. We propose to use data science in order to cope with the previous issues. This paper aims to detail the different possibilities offered by the data science field of expertize, more precisely in terms of machine learning, text mining and computer vision, but also to give a brief overview on the future works we will research. This paper position itself as a roadmap for our further proceedings.
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Submitted on : Tuesday, February 6, 2018 - 9:58:17 AM
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Jonathan Dekhtiar, Alexandre Durupt, Matthieu Bricogne, Dimitris Kiritsis, Harvey Rowson, et al.. Toward an Extensive Data Integration to Address Reverse Engineering Issues. 13th IFIP International Conference on Product Lifecycle Management (PLM), Jul 2016, Columbia, SC, United States. pp.478-487, ⟨10.1007/978-3-319-54660-5_43⟩. ⟨hal-01699728⟩



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