Tool Wear and Failure Prediction in Machining Difficult-to-Machine Materials: A Review of Monitoring, Modeling, and Intelligent Manufacturing Technologies

Difficult-to-machine materials, including titanium alloys, nickel-based superalloys, hardened and high-strength steels, stainless steels, and fiber-reinforced composites, are widely used in advanced manufacturing but impose severe thermo-mechanical–chemical loads on cutting tools, resulting in progressive wear and degradation, localized damage, and, in severe cases, catastrophic failure. This review summarizes recent advances in tool condition monitoring and prognostics following the framework of material characteristics–tool deterioration mechanisms–condition sensing–predictive modeling–manufacturing decision making. The relationships between material properties and tool condition deterioration are first discussed, distinguishing progressive wear mechanisms, such as abrasion, adhesion, diffusion, and oxidation, from thermally induced degradation and localized damage phenomena such as cracking, coating delamination, and edge chipping. Direct tool measurement and indirect process-response monitoring based on force and torque, vibration and acoustic signals, thermal signals, and machine-tool electrical signals are then reviewed, while machining-quality characteristics are treated separately as machining-outcome-based condition indicators. Multisource information fusion is further discussed for integrating complementary tool condition information from these different sources. Tool condition assessment and prognosis are further examined in terms of wear estimation, tool-condition state identification, remaining useful life prediction, and catastrophic-failure risk prediction. Physics-based, data-driven, and hybrid models are compared with respect to accuracy, interpretability, uncertainty, and generalization. Finally, industrial deployment through online inference, edge-cloud collaboration, CNC integration, and digital-twin-supported closed-loop tool health management is discussed, together with key challenges in limited-data learning, cross-condition generalization, multimodal fusion, uncertainty quantification, standardized evaluation, and sustainable manufacturing.