Wednesday, October 16th 2024

(Milan Time) 10:00-12:00

(Beijing Time) 16:00-18:00

Tencent ID : 490-178-240

Weblink: https://meeting.tencent.com/dm/tLe4USwgWC0t

Session Aims & Scope

Digital twin is a cutting-edge technology in the digital economy and is considered a key enabling technology for intelligent manufacturing systems. The conference discusses the cutting-edge technologies of digital twin and its application models in manufacturing equipment, manufacturing processes, production lines, workshops, factories,  warehousing and so on, aiming to promote the application of digital twin technology in the manufacturing industry.

Session Chair(s)

Chair

Haisong HUANG

Professor

Guizhou University (China)

Co-Chair

Kai YANG

Associate Professor

Guizhou University (China)

Session Presentation

1.

Yan JIN

Professor

Queen’s University Belfast (UK)

Title: Advanced Manufacturing Empowered by Parallel Kinematic Machines

Abstract 

Parallel robots have been intensively researched in the last three decades, but it is not widely utilized for manufacturing yet. This talk will introduce the state of the art of using parallel robots for high value manufacturing. Our latest research on automated aircraft assembly and double-sided synchronized machining using parallel kinematic machines will be presented. Research issues and future directions will be discussed.

2.

Ismael Lopez-JUAREZ

Professor

Center for Research and Advanced Studies (CINVESTAV). Mexico

Title: End-to-End DRL for Path Planning on 3D Surfaces

Abstract 

Deep Reinforcement Learning (DRL) is a robust learning paradigm that can be used to train agents through their interaction with the environment, but due to the sampling inefficiency of these algorithms, many computational resources and training time are needed to solve complex tasks, especially those based on computer vision.

The use of simulators to train agents promises to solve the training data demands of DRL algorithms. This, coupled with the extensive development of robotics simulators in the last decade (e.g., CoppeliaSim, MuJoCo, PyBullet) makes it a promising training approach. However, there are still bottlenecks in data exchange between the simulator and Deep Learning frameworks (e.g., TensorFlow, PyTorch), which delay training time.

A training methodology is presented that allows generating in simulation an optimized policy that solves the task of tracking trajectories on 3D surfaces, using only a partial observation of the environment (from an RGBD sensor) to derive the agent’s actions (i.e., end- to-end), which also reduces the amount of time and computational resources needed for training. The objective is not to replace traditional control techniques for solving manipulation tasks. Instead, it is proposed to use a combination of DRL algorithms and traditional techniques to solve tasks with complex observation or action spaces. A reduction in training time is achieved with the proposed methodology, and the learned policy can follow three-dimensional trajectories with a small margin of error using only images from an RGBD sensor mounted on the robot’s end-effector.

3.

Sihan HUANG

Associate Professor

Beijing Institute of Technology (China)

 

Title: Toward digital validation for smart manufacturing based on digital twin

Abstract 

A digital validation framework of rapid product development for smart manufacturing based on digital twin is proposed. During product development, the new product is designed according to the new requirements in the virtual space, in which the existing digital twins of products can be referenced. Then, an ultrahigh-fidelity virtual manufacturing system is constructed for digital trial manufacturing based on the digital twin of the manufacturing system and the design scheme of the new product. An ultrahigh-fidelity digital prototype can be obtained from digital trial manufacturing for digital validation. The new product validation is executed on the digital proto- type to test its performance. The digital validation results can be used to improve the design scheme of the new product and boost the corresponding manufacturing processes. Finally, a case study is presented to show the effectiveness of the proposed framework.

4.

Alireza AHMADI

PhD candidate

Politecnico di Milano (Italy)

Title: Using Digital Twin to Optimize Workforce Allocation and Improve Operation Efficiency in Smart Manufacturing with Discrete Event Simulation

Abstract 

This study explores the use of Digital Twin technology combined with Labour Flexibility (LF) strategies and Discrete Event Simulation (DES) to enhance operational efficiency in Industry 5.0 manufacturing. By simulating various levels of worker flexibility, from static assignments to full cross-training, the impact on production performance is evaluated, particularly in terms of throughput time, worker relocation, and resource usage. Results indicate that moderate flexibility reduces idle time, while excessive flexibility can lead to diminishing returns. The Digital Twin facilitates real-time monitoring and predictive analytics, ensuring efficient workforce allocation to meet bottlenecks. Integrating this system with Manufacturing Execution Systems (MES) enhances communication between digital and physical assets. This research highlights how smart manufacturing can achieve operational excellence and human-centric adaptability through digital transformation.

5.

Yung Po TSANG

Lecturer

The Hong Kong Polytechnic University (China)

Title: Digital Twin for Sustainable and Resilient Supplier Management

Abstract 

In the era of Industry 4.0, conventional supplier management practices struggle with uncertainty and lack effective decision validation. This research introduces a decision support system that addresses these challenges by integrating stratified decision-making with multi-agent digital twin technology. The digital twin enables real-time simulation and validation of supplier development strategies, enhancing adaptability and resilience.