Bayesian Belief Network Modeling of Accident Occurrence in Metal Lathe Machining Operations

Abstract

Accidents occurrence in metal lathe machining operations in industrial workshops often cost organizations billions of dollars while injured workers and families are faced with financial and emotional burdens. Studies revealed that the fly-out accident is the most probable accident that occurs during metal lathe machining operations. The uncertainty surrounding its occurrence is rarely reported. This study, therefore, modeled the uncertainty surrounding the occurrence of a fly-out accident during metal lathe machining operations and its corresponding consequences using the Bayesian belief network (BBN). Fly-out accident causal factors were identified representing the parent nodes with two states each. Two child-node scenarios were modeled on Bayesian belief influence diagrams, namely the fly-out accident with two states (yes and no) and the consequences of the fly-out accident with three states (fatal, serious and minor). Seven causal factors of the fly-out accident were identified (chuck-related fault, tool-post failure, workpiece holding fault, coolant fault, wrong operating speed, safety-related guards fault and wrong feed rate). Bayesian causal inference of fly-out accident was 0.708 and the fatal fly-out accident was 0.263. Bayesian diagnostic inference showed that chuck association fault and improper feed rate were significant causal factors influencing the occurrence of a fly-out accident, fatal fly-out accident and serious fly-out accident, while the occurrence of a minor fly-out accident was affected by coolant fault during machining operations. The study identified areas of safety concerns that may be used for the development of Machine Workshop Safety Management Systems toward sustainable, safe, and effective machine workshop operations.