Quality prediction in a smart factory: a real case study

BSTRACT The Industry 4.0 concept refers to new production patterns that include new technologies, manufacturing elements, and workforce organizations. It creates highly efficient production systems that change production processes, reduce production costs and improve product quality. Quality 4.0 is an evolution of Industry 4.0, which is a modification of traditional quality control charts. In this paper, our motivation is to improve manufacturing processes as we monitor product’s quality by improving the percentage of correctly manufactured products thereby achieving efficiency. A four-layer decision-making architecture is proposed where different models and techniques are applied and a comparative study is achieved on real industrial case study: 1) data exploration layer, 2) feature engineering layer, 3) modeling layer, in which three categories of time series forecasting algorithms are experimented: statistical model (ARIMA), machine learning models (Random forest and XGBOOST) and deep learning models (Stacked LSTM and Transformer-based model), and finally 4) interpretation layer. The transformer-based model scored the best. With the classification model’s interpretation, we deducted the recommended values to monitor the product’s quality in order to reach relatively zero defects.