Analysis of forecasting capabilities of ground surfaces valuation using artificial neural networks

Abstract

Industry worldwide has been marked by intense competition in recent years, placing companies under ever increasing pressure to improve the efficiency of their product processes. In addition to efficiency, precision is an extremely important factor, allowing companies to maintain standards and procedures aligned with international standards. One of the finishing processes most widely utilized for the manufacturing of mechanical precision components is grinding, and one of the principal criteria for evaluating the final quality of a product is its surface, which is influenced mainly by thermal and mechanical factors. Thus, the objective of this work was to investigate the intrinsic relationship between the surface quality of ground workpieces and the behavior of the corresponding acoustic emission and grinding power signals in the surface grinding processes, using artificial neural networks. The surface quality of workpieces was analyzed based on parameters of surface grinding burn, surface roughness and microhardness. The use of artifice-al neural networks in the characterization of the surface quality ground workpieces was found to yield good results, constituting an interesting proposal for the implementation of intelligent systems in industrial environments.