PREDICTIVE QUALITY CONTROL IN HV CABLE MANUFACTURING: AN ALPHA ALGORITHM APPROACH TO HUMAN ERROR PATTERN RECOGNITION AND DEFECT FORECASTING

Emilia Maria CÂMPEAN, Călin Moldovan, Liviu Adrian CRIŞAN, Adrian Constantin POPESCU, Claudiu ABRUDAN

Abstract


This study investigates human error reduction strategies in high-voltage (HV) cable manufacturing. Manual assembly operations remain a critical source of variability and defects, with human factors accounting for 68.8% of total quality issues. A mixed-methods approach was employed, integrating ergonomic assessment, process maturity evaluation, and data mining-based predictive modeling. Key defects analyzed included incorrect cable branch dimensioning, missing components, and wrong clip orientation. Data mining predictions revealed a potential 20% decrease in dimensioning errors with improved equipment maturity, while missing components and orientation errors showed risk of increase without ergonomic intervention. The findings underscore the critical role of ergonomics and operator competency in achieving sustainable manufacturing quality and provide a replicable framework for similar high-mix, low-volume production environments.


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