A Self-Referencing Framework for Milling Tool Wear Diagnosis Under Tool-to-Tool Variability Using Physics-Informed Order-Tracked Features

Tool wear degrades machining quality and, if unchecked, can progress to breakage, causing workpiece defects, downtime, and spindle damage; automatic tool condition monitoring is therefore essential. In real production, new and reground tools are used interchangeably and differ slightly in geometry and material, so the “normal” baseline shifts from one tool and mounting to the next. This makes both global-baseline diagnostics and deep-learning methods that require large labeled fault datasets difficult to apply. This study proposes a lightweight, self-referencing framework—whose per-tool baseline is built from acceptable-state data alone—for diagnosing milling tool wear under tool-to-tool variability. Its novelty lies not in the individual techniques—self-referencing, order tracking, and the Mahalanobis distance, which are established—but in their integration into a single framework, designed for fault-label-free operation, that rebuilds a dedicated baseline for every newly mounted tool. Whenever a tool is mounted, its own acceptable data, a short initial segment of machining taken as healthy immediately after mounting, form the baseline; kinematics-based order-tracked features from a single spindle-bearing accelerometer are used to compute the Mahalanobis distance from the acceptable state, which serves as a continuous health index. A warning limit set statistically from the acceptable data alone, together with a defect limit set as a pragmatic engineering multiple of it, separates the acceptable, warning, and defect grades. On four end mills of identical specification, the primary full-baseline analysis yielded a warning-detection AUC of 0.986 and a defect-detection AUC of 0.936; with a persistence rule, defect-grade wear was detected in all four tools with no false alarms in the acceptable state. Because the baseline is built from only a short acceptable segment and the computation is inexpensive, the framework is, in principle, suited to shop floors with frequent tool changes and to on-machine or edge deployment (not yet benchmarked on an edge device); the present validation, however, is limited to four tools and a single workpiece material under fixed cutting conditions with accelerated wear, so broader verification remains necessary.