Deep reinforcement learning in smart manufacturing: A review and prospects
To facilitate the personalized smart manufacturing paradigm with cognitive automation
capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …
capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …
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 …
actively cross-combined with various fields to increase the effect of its original low single …
General purpose digital twin framework using digital shadow and distributed system concepts
Digital twin (DT) is an emerging concept in the Industry 4.0 era. It integrates intelligence into
industrial processes. The broadness of DT's concept allows for multiple definitions and …
industrial processes. The broadness of DT's concept allows for multiple definitions and …
[HTML][HTML] Data-driven simulation-based decision support system for resource allocation in industry 4.0 and smart manufacturing
Data-driven simulation (DDS) is fundamental to analytical and decision-support
technologies in Industry 4.0 and smart manufacturing. This study investigates the potential of …
technologies in Industry 4.0 and smart manufacturing. This study investigates the potential of …
Designing an adaptive and deep learning based control framework for modular production systems
In today's rapidly changing production landscape with increasingly complex manufacturing
processes and shortening product life cycles, a company's competitiveness depends on its …
processes and shortening product life cycles, a company's competitiveness depends on its …
Production-level artificial intelligence applications in semiconductor supply chains
This is a panel paper that discusses the use of Artificial Intelligence (AI) technologies to
address production and supply chain level problems in semiconductor manufacturing. We …
address production and supply chain level problems in semiconductor manufacturing. We …
A context-aware real-time human-robot collaborating reinforcement learning-based disassembly planning model under uncertainty
A Amirnia, S Keivanpour - International Journal of Production …, 2024 - Taylor & Francis
Herein, we present a real-time multi-agent deep reinforcement learning model as a
disassembly planning framework for human–robot collaboration. This disassembly plan …
disassembly planning framework for human–robot collaboration. This disassembly plan …
[HTML][HTML] schlably: A Python framework for deep reinforcement learning based scheduling experiments
Research on deep reinforcement learning (DRL) based production scheduling (PS) has
gained a lot of attention in recent years, primarily due to the high demand for optimizing …
gained a lot of attention in recent years, primarily due to the high demand for optimizing …
Digital twin-based reinforcement learning framework: application to autonomous mobile robot dispatching
This paper proposes a new framework for embedding an Intelligent Digital Twin (DT) in a
production system with the objective of achieving more efficient real-time production …
production system with the objective of achieving more efficient real-time production …
Traffic Flow Speed Prediction in Overhead Transport Systems for Semiconductor Fabrication Using Dense-UNet
To improve semiconductor productivity, efficient operation of the overhead hoist transport
(OHT) system, which is an automatic wafer transfer device in a semiconductor fabrication …
(OHT) system, which is an automatic wafer transfer device in a semiconductor fabrication …