This paper presents the process required to implement a data driven Predictive Maintenance (PdM) not only in the machine decision making, but also in data acquisition and processing. A short review of the different approaches and techniques in maintenance is given. The main contribution of this paper is a solution for the predictive maintenance problem in a real machining process. Several steps are needed to reach the solution, which are carefully explained. The obtained results show that the Preventive Maintenance (PM), which was carried out in a real machining process, could be changed into a PdM approach. A decision making application was developed to provide a visual analysis of the Remaining Useful Life (RUL) of the machining tool. This work is a proof of concept of the methodology presented in one process, but replicable for most of the process for serial productions of pieces.
Article citation info: among other events that can damage both the workpiece and the machine tool [13].Tool condition monitoring can track tool wear and thereby predict the Remaining Useful Life (RUL) of the tool, a very important issue for rapid machining processes involving superalloys. Together with the detection of instability, tool monitoring provides a valuable data source for improving the efficiency of superalloy turning processes.In this paper, cutting-force signals are studied for the detection of instabilities during the hard-turning process and a tool-wear prediction model is proposed. The three superalloys tested in this study were as follows: Inconel 718, Haynes 282 and Waspalloy.The structure of this paper will be as follows. In Section 2, related work on radial turning process optimization, tool-wear prediction, and the improvement of tool life will be discussed. In Section 3, the industrial application will be explained and, in Section 4, the methodology used to detect instabilities and to predict tool wear. The results will
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.