Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 18, Issue 11, Pages 8119-8128Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2022.3168309
Keywords
Digital twin; Manufacturing; Casting; Real-time systems; Solid modeling; Data models; Production; Deep learning; defects detection; die-casting manufacturing; digital twin; quality prediction
Categories
Funding
- Take the lead Science and Technology Research Project of Liaoning Province [2021JH1/104000 79]
- Dalian Science and Technology Major Project [2020ZD13GX003]
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This paper proposes a digital twin and data-driven quality prediction architecture, which realizes real-time quality prediction and appearance defect quality prediction in die-casting manufacturing through the virtual-real interaction of digital twin and the collaborative working mode. The real-time quality prediction accuracy of die-casting process is improved through data preprocessing and XGBoost-based learning method. The detection problem of complex appearance defects in castings is solved by a deep learning based neural network.
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.
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