Risk and Reliability Assessment of Metal Lathe Machining Operation with DBN-FFTA Hybrid Approach

Abstract

A metal lathe is a high-performance tool used in the field of metalworking to remove excess material and shape metal parts. However, engaging in metal turning operations carries risks that can lead to serious accidents and physical harm. It is crucial to ensure that these systems are functioning correctly, as any malfunction or flaw can lead to dangerous situations. To maintain safety in industrial environments, it is important to assess the risks and reliability of the equipment. A study was conducted using a method called fuzzy fault tree analysis (FFTA), combined with fuzzy logic, to determine the probability of basic events. Bayesian networks (BNs) were utilized to update probabilities and overcome limitations of the fault tree (FT). A Dynamic Bayesian Network (DBN) was employed to estimate the reliability of a metal lathe in a specific scenario. The FT identified 57 root events and estimated the probability of workpiece FLY-OUTS as 0.03174329 using the FT method and 0.031505849 using the BN method. Based on the predictions of the DBN, system reliability decreased by 19.89% after 24 months. The FT diagram comprehensively captured all the factors associated with FLY-OUTS, highlighting that improper closing of the part on the tool was a significant contributing factor. The study concludes by proposing safety measures for turning operations based on the identified critical events.