Enhancing adaptive failure risk prognosis for cutting tools in heterogeneous working environments: A comprehensive modeling framework

Predicting the failure risk of cutting tools is an essential component for optimizing manufacturing processes. Present prognostic techniques often struggle to account for dynamic working conditions and can yield inaccurate model parameters due to generalized parameter estimations applied to data sourced from diverse working conditions. Recognizing the inherent robustness of homogeneous data in representing system status compared to heterogeneous data, this paper introduces a novel framework for adaptively forecasting the failure likelihood of cutting tools by real-time recognition of working condition types in a time-varied binary-condition environment. This is achieved by amalgamating a diverse set of prior failure models, each derived from homogeneous data subsets. Specifically, we construct a recognition method for working conditions based on similarity evaluation of real-time sensor data in amplitude and time domains, and formulate adaptive posterior prognostic models based on degradation trends, failure rates, and cumulative distribution functions. By embracing this approach, we effectively incorporate the inherent variability in cutting-tool failure risks stemming from the heterogeneity of working conditions into the prognostic assessment. To validate the efficacy of our methods, we utilize a degradation dataset from the NASA prognostics data repository, focusing on milling inserts in a machining center. Through a comprehensive comparison with existing models, we demonstrate the superiority of the proposed failure-rate-based posterior prognosis model. Additionally, we delve into the nuances of the three adaptive prognosis models through an in-depth sensitivity analysis.