This paper focuses on the relationship between key operation parameters and machine learning defects to design an Operation Parameters Recommender System (OPRS) in the textile industry. This paper integrates historic manufacturing process data from the perspective of data science, such as machine operation parameters from warping, sizing, beaming, weaving process, and management experience data, such as textile inspection results from the quality control section. Then, the regression models are applied to predict the textile operation parameters. This research also uses the classification models to predict the quality of the textile. Based on the ten-fold cross-validation testing, experimental results show that our model can achieve 90.8% accuracy on the quality level prediction. The best regression model for predicting weaving operation parameters can reduce the mean square error (MSE) 0.01%. By combining the above two models, the proposed OPRS can provide a completed analysis data of operation parameters. It provides good performance when comparing with previous stochastic methods. As the proposed OPRS can support technicians setting operation parameters more precisely, even for a new type of yarn, it can help to fix the tech skills gap in the textile manufacturing process.

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C.-Y. Lee, J.-Y. Lin, R.-I. Chang, “Improve quality and efficiency of textile process using data-driven machine learning in industry 4.0,” International Journal of Technology and Engineering Studies, vol. 4, no. 2, pp. 64-76, 2018.