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<p>Process capability indices are widely used in manufacturing quality control, but capability approval is often implemented by directly thresholding finite-sample estimates, which can produce unstable and poorly calibrated decisions near the approval boundary. This paper develops a hybrid statistical–learning framework for capability-based decision support in manufacturing. The proposed UC-Cap approach combines a statistically grounded capability baseline with a residual learning component that uses process, distributional, specification-related, and measurement-related features to refine capability-decision risk estimates under non-ideal manufacturing conditions. The statistical baseline preserves the interpretability of classical capability analysis, while the learning component provides data-driven correction for systematic deviations arising from non-normality, measurement effects, and finite-sample variability. A nested Monte Carlo evaluation is introduced to assess probabilistic calibration under controlled synthetic settings, and an empirical manufacturing study is used to examine decision behavior under realistic capability data. Results show that deterministic thresholding can be unstable near the capability boundary and that anchored UC-Cap improves calibration over the statistical baseline under synthetic reference-risk settings. In the manufacturing study, an observation-disjoint, dimension-grouped evaluation favors unconstrained logistic baselines on predictive metrics, whereas UC-Cap provides a statistically anchored and interpretable risk decomposition. The framework further supports cost-sensitive thresholds and integration into existing capability-analysis workflows.</p>

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A hybrid statistical learning framework for capability-based decision support in manufacturing

  • 期刊论文

  • 发表日期：

    2026年7月31日

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  • Fei Jiang,
  • Lei Yang

摘要

Process capability indices are widely used in manufacturing quality control, but capability approval is often implemented by directly thresholding finite-sample estimates, which can produce unstable and poorly calibrated decisions near the approval boundary. This paper develops a hybrid statistical–learning framework for capability-based decision support in manufacturing. The proposed UC-Cap approach combines a statistically grounded capability baseline with a residual learning component that uses process, distributional, specification-related, and measurement-related features to refine capability-decision risk estimates under non-ideal manufacturing conditions. The statistical baseline preserves the interpretability of classical capability analysis, while the learning component provides data-driven correction for systematic deviations arising from non-normality, measurement effects, and finite-sample variability. A nested Monte Carlo evaluation is introduced to assess probabilistic calibration under controlled synthetic settings, and an empirical manufacturing study is used to examine decision behavior under realistic capability data. Results show that deterministic thresholding can be unstable near the capability boundary and that anchored UC-Cap improves calibration over the statistical baseline under synthetic reference-risk settings. In the manufacturing study, an observation-disjoint, dimension-grouped evaluation favors unconstrained logistic baselines on predictive metrics, whereas UC-Cap provides a statistically anchored and interpretable risk decomposition. The framework further supports cost-sensitive thresholds and integration into existing capability-analysis workflows.