The modelling and operations for the digital twin in the context of manufacturing

J Bao, D Guo, J Li, J Zhang - Enterprise Information Systems, 2019 - Taylor & Francis
The lack of effective methods to develop the product, process and operation models based
on virtual and physical convergence leads to the poor performance on intelligence, real-time …

Deformable convolutional networks for efficient mixed-type wafer defect pattern recognition

J Wang, C Xu, Z Yang, J Zhang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Defect pattern recognition (DPR) of wafer maps is critical for determining the root cause of
production defects, which can provide insights for the yield improvement in wafer foundries …

A genetic programming hyper-heuristic approach for the multi-skill resource constrained project scheduling problem

J Lin, L Zhu, K Gao - Expert Systems with Applications, 2020 - Elsevier
Multi-skill resource-constrained project scheduling problem (MS-RCPSP) is one of the most
investigated problems in operations research and management science. In this paper, a …

A collaborative architecture of the industrial internet platform for manufacturing systems

J Wang, C Xu, J Zhang, J Bao, R Zhong - Robotics and Computer …, 2020 - Elsevier
One of the most significant advances in the development of intelligent manufacturing is
represented by the industrial Internet, which is combining the physical and cyber …

AdaBalGAN: An improved generative adversarial network with imbalanced learning for wafer defective pattern recognition

J Wang, Z Yang, J Zhang, Q Zhang… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Identification of the defective patterns of the wafer maps can provide insights for the quality
control in the semiconductor wafer fabrication systems (SWFSs). In real SWFSs, the …

A proactive material handling method for CPS enabled shop-floor

W Wang, Y Zhang, RY Zhong - Robotics and computer-integrated …, 2020 - Elsevier
Cyber physical system (CPS) enables companies to keep high traceability and controllability
in manufacturing for better quality and improved productivity. However, several challenges …

Knowledge augmented broad learning system for computer vision based mixed-type defect detection in semiconductor manufacturing

J Wang, P Gao, J Zhang, C Lu, B Shen - Robotics and Computer-Integrated …, 2023 - Elsevier
Defect detection is a critical measurement process for intelligent manufacturing systems to
provide insights for product quality improvement. For complex products such as integrated …

A survey on machine and deep learning in semiconductor industry: methods, opportunities, and challenges

AC Huang, SH Meng, TJ Huang - Cluster Computing, 2023 - Springer
The technology of big data analysis and artificial intelligence deep learning has been
actively cross-combined with various fields to increase the effect of its original low single …

Deep reinforcement learning for solving resource constrained project scheduling problems with resource disruptions

H Cai, Y Bian, L Liu - Robotics and Computer-Integrated Manufacturing, 2024 - Elsevier
The resource-constrained project scheduling problem (RCPSP) is encountered in many
fields, including manufacturing, supply chain, and construction. Nowadays, with the rapidly …

[PDF][PDF] A new ensemble residual convolutional neural network for remaining useful life estimation

L Wen, Y Dong, L Gao - Math. Biosci. Eng, 2019 - aimspress.com
Remaining useful life (RUL) estimation is one of the most important component in prognostic
health management (PHM) system in modern industry. It defined as the length from the …