초록

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Abstract ▼ AI-Helper

Defects in the coating process of plastic automotive components are caused by various causes and phenomena. The correlation between defect occurrence rate and meteorological and environmental conditions such as temperature, humidity, and fine dust was analyzed. The defect rate data categorized by ty...

Defects in the coating process of plastic automotive components are caused by various causes and phenomena. The correlation between defect occurrence rate and meteorological and environmental conditions such as temperature, humidity, and fine dust was analyzed. The defect rate data categorized by type and cause was collected for a year from a automotive parts coating company. This data and its correlation with environmental condition was acquired and experimented by machine learning techniques to predict the defect rate at a certain environmental condition. Correspondingly, the model predicted 98% from fine dust and 90% from curtaining (runs, sags) and hence proved its reliability.