Machine Learning Techniques to address classification issues in Reverse Engineering

This paper aims to provide a road map for future works related to reverse engineering field of expertise. Reverse Engineering, in a mechanical context, relates to any process working in a bottom-up fashion, namely that it goes from a lower level concept or product (closer to the final product) to a higher level one (closer to the ideation step). Nowadays, the manufacturing industry is facing unprecedented increase in data exchange and data warehousing. This comes with new issues that our work will not explore, such as "how to store these data in an efficient manner?", "what should be stored?" and so on. Nonetheless, this trend also creates new opportunities if we manage to integrate these data into the expertise workflows. In this paper we will cover the possibilities offered by machine learning to succeed in this challenge. We will also present a first and major step in our road map in order to achieve our research goals. We plan to design a metric to quantify how well and how precise can we perform some specific reverse engineering tasks such as detection, segmentation and classification of mechanical parts in imagery data. We aspire to open this metric; and make it freely and widely available to researchers and industry in order to compare the effectiveness, robustness and preciseness of the existing and future approaches.