Applying Deep Learning for Automated Quality Control and Defect Detection in Multi-stage Plastic Extrusion Process

Abstract

In the plastics industry, particularly in multistage extrusion processes, maintaining a consistent product quality is paramount. The extrusion process often involves converting granular raw material into a plastic film by heating and stretching it across multiple layers. Two significant aspects of the output product quality are product parameters such as film thickness and stretch, and the presence or absence of defects. Currently, product parameters are efficiently monitored using sensors, but defect identification largely relies on the manual visual inspection by the operator, which may not always occur in real time. This manual approach is prone to errors and can result in delayed defect detection. This study proposes to explore the application of deep learning to automate defect detection in the multi-stage plastic extrusion process. By training deep learning models on a rich dataset of process parameters of the output product, it is possible to enable realtime, automatic identification of defects. This can lead to a substantial improvement in the efficiency and accuracy of the quality control process. Various deep learning architectures will be employed and evaluated for their effectiveness in this task. Furthermore, this study also aims to investigate the correlation between various factors, including equipment performance and quality of incoming raw materials, and the occurrence of defects. Advanced deep learning techniques like Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks will be used to analyze the time-series data from the extrusion process. The findings from this analysis could provide valuable insights into the root causes of defects and guide efforts to minimize their occurrence. In conclusion, this research seeks to leverage the potential of deep learning to enhance the quality control process in the multi-stage plastic extrusion industry, with a focus on automated, real-time defect detection and root cause analysis.