Reliability analysis of cutting tools using transformed inverse Gaussian process-based wear modelling considering parameter dependence

Reliability analysis is crucial for ensuring the performability of the desired function. The cutting tool performs the machining operation at varied conditions to manufacture diverse products. During operation, the tool degrades stochastically in the form of wear. To avoid the unfavourable consequences occurring from severe tool wear, appropriate formulation of the tool reliability, considering threshold degradation level as the failure criterion, is crucial. However, the degradation of the tool during machining is impacted by the current state of the tool wear and operating conditions. Considering these, the present study proposes a state-dependent transformed inverse Gaussian (TIG) process incorporating the effects of operating conditions to develop the tool wear model. In order to evaluate the proposed method, tool wear experiments are conducted at different operating conditions following the Taguchi orthogonal array experimental design. The experimental data are utilised to estimate the parameters of the developed model using the Bayesian approach. Following the parameter estimation, tool reliability is evaluated under varying operating conditions. The comparison of the estimated median time to failure of the tools with the failure time observed in the validation experiments ensures the effectiveness of the proposed model. The proposed approach has the potential to estimate the reliability of the industrial products subjected to state-dependent degradation under varied operating conditions.