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February 17, 2026The International Journal of Advanced Manufacturing Technology0 citations

Cold-start quality prediction in multi-machine manufacturing systems via weak-consistency data fusion with human-in-the-loop learning

CWChien-Chih WangMing Chi University of Technology HSHong-Gu ShenMing Chi University of Technology CCChun-Hua ChienMing Chi University of Technology

Key Points

This research aims to enhance cold-start quality prediction in multi-machine manufacturing systems facing data scarcity and variability.
Developed a weak-consistency data fusion framework
Implemented human-in-the-loop learning for interpretable predictions
Utilized interquartile range filtering and machine screening
Validated with real production data from a laminate manufacturing plant
Achieved an R² of 85.7% and MAPE of 2.14%
Expanded initial training dataset by 7.6 times
Documented an 87% prediction achievement rate within ±5s quality tolerance
Demonstrated a 17% points improvement over cold-start baseline (p < 0.01)

Abstract

Cold-start quality prediction remains a critical challenge in multi-machine manufacturing systems, where newly commissioned equipment suffers from severe data scarcity and pronounced non-independent and identically distributed (non-i.i.d.) heterogeneity across nominally similar machines. In such environments, naïve data pooling or black-box transfer learning often leads to negative transfers and low practitioner trust, limiting deployment in real production settings. To address this challenge, this study proposes a weak-consistency data fusion framework with human-in-the-loop learning for interpretable cold-start quality prediction in heterogeneous manufacturing systems. Rather than enforcing full distributional alignment, the proposed framework enables safe cross-machine data augmentation by combining variability-based machine screening, interquartile range (IQR) overlap filtering, categorical process validation, and structured expert intervention. A physics-informed hierarchical stacking ensemble was further integrated with a three-cycle human-in-the-loop refinement process guided by SHAP and permutation importance, ensuring transparency and operational relevance. The framework was validated using real production data from a copper-clad laminate manufacturing plant with seven parallel impregnation lines. Under a severe cold-start scenario with only 384 initial samples from a newly commissioned machine, the proposed approach safely augments the training dataset by 7.6 times using data screened from a compatible legacy machine. Experimental results demonstrate that the proposed method achieves an R² of 85.7%, an MAPE of 2.14%, and an 87% prediction achievement rate within the plant’s ± 5 s quality tolerance, representing a statistically significant improvement of 17% points over the cold-start baseline ( p < 0.01). The results confirm that weak-consistency data fusion, combined with structured human oversight, provides a practical and interpretable solution for cold-start quality prediction in multi-machine manufacturing systems, supporting reliable deployment in high-mix, low-volume production environments.

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