Abstract
The present era of Industry 4.0 has taken a lead in the manufacturing industry to achieve predictive maintenance of machine tool systems to ensure increased productivity and improved quality of machined parts. However, the implementation of predictive maintenance involves a huge capital investment for the installation of sensors and computational algorithms. Therefore, only the most critical subsystems of the machine tool are considered for maintenance purpose. Criticality analysis of a machine tool is performed to identify the most critical components and their potential failure modes. Failure modes, effects, and criticality analysis (FMECA) is the most popular tool for criticality analysis of mechanical systems. The present study illustrates a systematic methodology to perform FMECA of computer numerical control (CNC) lathe machine tool for implementing predictive maintenance. Industrial field failure data and expert elicitation are used to determine the risk associated with each component and subsystems of CNC lathe to estimate the risk priority number (RPN), which quantify the risk factor. Spindle unit is identified as the most critical subsystem with RPN equal to 781. The subsystems identified with higher RPNs are considered for predictive maintenance, and those with lower RPNs are considered for preventive or reactive maintenance.
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