Artificial intelligence (AI) is increasingly used for tool-wear prediction in computer numerical control (CNC) machining, yet a high average accuracy can conceal systematic failure in the safety-critical high-wear region, where under-prediction makes a worn tool appear healthier than it is. This paper presents a trustworthy multimodal framework that audits, mitigates, and ultimately characterises this bias on the public MATWI dataset. A fusion model combining 512 image features with 84 force, acceleration, and acoustic features attains a strong test mean absolute error (MAE) of $30.9\mathsf{\mu }\mathrm{m}$, outperforming image-only ($51.4\mathsf{\mu }\mathrm{m}$) and sensor-only ($68.3\mathsf{\mu }\mathrm{m}$) baselines. A signed, stratified bias audit nevertheless reveals a pronounced directional error: the model predicts the healthy region to within $21.3\mathsf{\mu }\mathrm{m}$ but under-predicts the worn region by approximately $85\mathsf{\mu }\mathrm{m}$ (worn-region MAE $85.5\mathsf{\mu }\mathrm{m}$), with the error deepening monotonically as wear increases. A three-stage mitigation pipeline—data-level resampling, cost-sensitive and relevance-based training, and post-hoc recalibration—reduces the worn-region error by $18\%$ for the combined pipeline and by up to $22\%$ for the best single-stage configuration, but cannot eliminate it, and only at a measurable cost to the well-sampled region. The residual bias persists across six regressor families sharing the same fused representation, with a 69–$127\mathsf{\mu }\mathrm{m}$ residual gap, and survives rigorous hyperparameter search and bias-aware loss functions behind an $\approx 85\mathsf{\mu }\mathrm{m}$ error floor. At every maintenance threshold evaluated the model produces a $100\%$ false-safe rate.Under the feature representation and sampling of this dataset, the bias is therefore not addressable by model family, mitigation strategy, or optimisation, which identifies data adequacy rather than model design as the binding constraint. The conclusion is scoped accordingly: alternative feature representations, generative augmentation, and domain adaptation were not evaluated and remain open. The contribution is a reusable protocol for distinguishing model-reducible from data-limited bias in safety-critical regression.

Artificial intelligence (AI) is increasingly used for tool-wear prediction in computer numerical control (CNC) machining, yet a high average accuracy can conceal systematic failure in the safety-critical high-wear region, where under-prediction makes a worn tool appear healthier than it is. This paper presents a trustworthy multimodal framework that audits, mitigates, and ultimately characterises this bias on the public MATWI dataset. A fusion model combining 512 image features with 84 force, acceleration, and acoustic features attains a strong test mean absolute error (MAE) of 30.9 µm, outperforming image-only (51.4 µm) and sensor-only (68.3 µm) baselines. A signed, stratified bias audit nevertheless reveals a pronounced directional error: the model predicts the healthy region to within 21.3 µm but under-predicts the worn region by approximately 85 µm (worn-region MAE 85.5 µm), with the error deepening monotonically as wear increases. A three-stage mitigation pipeline (data-level resampling, cost-sensitive and relevance-based training, and post-hoc recalibration) reduces the worn-region error by up to 18% but cannot eliminate it, and only at a measurable cost to the well-sampled region. The residual bias is shown to be both model-independent, persisting across six regressor families with a 69–127 µm residual gap, and optimisation-independent, surviving rigorous hyperparameter search and bias-aware loss functions behind an ≈ 85 µm error floor. These results establish the bias as data-intrinsic: it arises from the sparse sampling of the high-wear regime rather than from model or training choices, and the effective remedy lies in balanced, unbiased data collection rather than further algorithmic correction. The contribution is a reusable protocol for distinguishing model-reducible from data-intrinsic bias in safety-critical regression.