Digital Twin and Data-Driven Quality Prediction of Complex Die-Casting Manufacturing
Dong Liu, Yu Du, Wenjie Chai, ChangQi Lu, Ming Cong
Abstract
Digital twin and data-driven technologies provide an idea for realizing complex product quality prediction. Aiming at the issues of real-time visual monitoring, operating status analysis, and quality prediction in complex die-casting intelligent manufacturing, a digital twin and data-driven quality prediction architecture is proposed. The virtual-real interaction digital twin of die-casting manufacturing cells is established. The collaborative working mode of physical cells, virtual cells, and real-time monitoring is constructed to predict product quality. The learning method of die-casting parameter data and appearance defect data is proposed to realize the real-time quality prediction in die-casting process and the appearance defect quality prediction after processing, respectively. The data preprocessing and XGBoost-based learning method is proposed for real-time quality prediction of die-casting process. A single-shot refinement neural network for aluminum casting tiny defects detection (Refine-ACTDD) based on deep learning is proposed to solve the high-precision defect detection problems of small appearance defects of complex castings and large interference of complex background. Taking the complex aluminum die-casting as an example, the applications of quality prediction are verified. The method provides a new technical approach for high-precision quality prediction of complex die-casting manufacturing.