A Deep-Reinforcement-Learning-Based Digital Twin for Manufacturing Process Optimization

A Deep-Reinforcement-Learning-Based Digital Twin for Manufacturing Process Optimization

24 January 2024 | Abdelmoula Khoudi, Tawfik Masrou, Ibtissam El Hassani, Choumicha El Mazguali
This paper presents a framework for implementing a full-duplex digital twin system for autonomous process control in manufacturing, with plastic injection molding as a practical use case. The approach combines supervised learning and deep reinforcement learning models to automate the updating of the virtual system representation and enable intelligent decision-making for operational metrics optimization. The method aims to improve product quality while reducing costs and minimizing human involvement. The study demonstrates how the digital twin technology can enhance productivity, effectiveness, and adaptability in industrial processes, advancing its application in the manufacturing sector. 1. **Introduction** - **Background and Motivation**: Explains the concept of smart manufacturing and the role of digital twins in enhancing production systems. - **Research Contribution**: Highlights the innovative aspects of the proposed framework, including a data-driven approach, specific-purpose digital twins, and integrated AI and machine learning for decision-making. 2. **Literature Review on Digital Twin Technologies** - Reviews existing research on digital twins, focusing on their applications in various industries and the challenges in implementing them. 3. **Digital Twin Design Methodology** - **General Description**: Describes the methodology for designing an AI-based digital twin, emphasizing the use of real-time data and advanced analytics. - **Data Collection and Pre-Processing**: Details the process of collecting and preprocessing data for the digital twin. - **Data-Driven Replica of the Physical Model**: Explains how the digital twin models the physical system using real-time data. - **Deep Reinforcement Learning for Automated Decision-Making**: Discusses the use of deep reinforcement learning algorithms to optimize manufacturing processes. 4. **Digital Twin Implementation Methodology** - **Theoretical Background: Deep Reinforcement Learning**: Provides an overview of deep reinforcement learning algorithms, including value-based, policy gradient, and actor-critic methods. - **Industrial Use Case: Plastic Injection Molding**: Presents a real-world application of the digital twin in plastic injection molding, detailing the process parameters and their impact on product quality. 5. **Conclusion** - Summarizes the main contributions of the research and discusses future improvements and potential applications of the proposed framework.This paper presents a framework for implementing a full-duplex digital twin system for autonomous process control in manufacturing, with plastic injection molding as a practical use case. The approach combines supervised learning and deep reinforcement learning models to automate the updating of the virtual system representation and enable intelligent decision-making for operational metrics optimization. The method aims to improve product quality while reducing costs and minimizing human involvement. The study demonstrates how the digital twin technology can enhance productivity, effectiveness, and adaptability in industrial processes, advancing its application in the manufacturing sector. 1. **Introduction** - **Background and Motivation**: Explains the concept of smart manufacturing and the role of digital twins in enhancing production systems. - **Research Contribution**: Highlights the innovative aspects of the proposed framework, including a data-driven approach, specific-purpose digital twins, and integrated AI and machine learning for decision-making. 2. **Literature Review on Digital Twin Technologies** - Reviews existing research on digital twins, focusing on their applications in various industries and the challenges in implementing them. 3. **Digital Twin Design Methodology** - **General Description**: Describes the methodology for designing an AI-based digital twin, emphasizing the use of real-time data and advanced analytics. - **Data Collection and Pre-Processing**: Details the process of collecting and preprocessing data for the digital twin. - **Data-Driven Replica of the Physical Model**: Explains how the digital twin models the physical system using real-time data. - **Deep Reinforcement Learning for Automated Decision-Making**: Discusses the use of deep reinforcement learning algorithms to optimize manufacturing processes. 4. **Digital Twin Implementation Methodology** - **Theoretical Background: Deep Reinforcement Learning**: Provides an overview of deep reinforcement learning algorithms, including value-based, policy gradient, and actor-critic methods. - **Industrial Use Case: Plastic Injection Molding**: Presents a real-world application of the digital twin in plastic injection molding, detailing the process parameters and their impact on product quality. 5. **Conclusion** - Summarizes the main contributions of the research and discusses future improvements and potential applications of the proposed framework.
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