OPTIMIZING PAINTING OPERATION IN AN AUTOMOBILE PAINTSHOP USING ML APPROACH
DOI:
https://doi.org/10.11113/jm.v49.658Keywords:
Defects, First Time Through, Paintshop, Random forest regression, Reinforcement learningAbstract
Optimizing paint operations is crucial to ensure product quality, consistency, and customer satisfaction in the automobile paintshop. The detailed analyses and understanding of the paint process proffers a thorough optimization approach to increase productivity, lower the operational costs, while maintaining a competitive advantage. The study aims to improve the painting operation in an automobile paintshop through key painting parameters, implementing quantitative approach techniques, using both random forest regression and reinforcement ML model and evaluating the effectiveness of optimized model for enhancing the painting processes. The painting defects and the causes were analyzed using cause and effect diagram and the corresponding parameters associated with the defects were used for data modelling to optimize the process. Random forest regression model predicts the good products, number of scraps and number of reworks with an accuracy of 70.6% while the Q-learning model has a prediction accuracy of 94.6%. The model was validated by adjusting the system parameters to the predicted model data which indicates a significant improvement in the FTT, number of scraps and reworks. The significance of the study is its potential to drive operational and process improvements, to increase the organization’s productivity by reducing paint defects
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