Digital Twin Quality Engineering

Aims & Scope

This session aims to establish a premier interdisciplinary platform for advancing the theory, methodology, and industrial application of Digital Twin technology in the realm of equipment quality engineering. It seeks to bridge the gap between cutting-edge digital technologies and the entire lifecycle quality management of complex equipment—from design and manufacturing to operation and maintenance. The forum is dedicated to exploring how data-driven virtual replicas can transform traditional quality assurance into a predictive, proactive, and pervasive intelligent system, thereby enhancing reliability, safety, and cost-effectiveness.
                                                        

The session will focus on the following points:

• Quality Digital twin Data Governance

• Quality Digital Thread

• Digital twin Design for Quality & Virtual Verification

• Digital twin Manufacturing Process & Metrology Integration

• Digital twin driven Predictive Maintenance & Operational Health Management

• Closed-Loop Quality & Lifecycle Digital twin Data Fusion

• Digital twin-Augmented Quality Intelligence & Decision Support

• Standards, Interoperability & Implementation Challenges

 

------

Your valuable insights are welcome! 

We cordially invite interested researchers to contact us for details and presentation applications at: secretariat@idea-global.net

Session Chairs

Presentations

  • Professor
    Université Paris-Saclay
    Title: To be confirmed
    Abstract To be confirmed
  • Professor & HoD
    National Institute of Technical Teachers' Training and Research Kolkata, India
    Title: Updating digital twin in supervisory control and data acquisition for sustainable manufacturing
    Abstract Quality engineering in modern manufacturing is no longer limited to post-process inspection; it increasingly relies on intelligent, predictive, and adaptive systems that ensure quality in real time. Within this context, digital twins have emerged as a powerful enabler of Digital Twin Quality Engineering, offering a live, data-driven mirror of physical processes. This work demonstrates how a digital twin integrated with a supervisory control and data acquisition system can actively monitor and control laser beam micro-machining to achieve superior quality outcomes. By fusing mathematical models, artificial neural networks, and real-time vibration signals, the digital twin continuously evaluates surface roughness and specific machining energy as key quality metrics and dynamically adjusts process parameters. This closed-loop quality engineering framework not only reduces defects and rework but also enhances energy efficiency and sustainability, illustrating how digital twins can shift quality assurance from reactive correction to proactive, self-optimizing control in advanced manufacturing systems.
  • Professor
    The Hong Kong Polytechnic University
    Title: To be confirmed
    Abstract To be confirmed
  • Professor
    Nanjing University of Aeronautics and Astronautics
    Title: To be confirmed
    Abstract To be confirmed
  • Associate Professor
    University of Science and Technology Beijing
    Title: To be confirmed
    Abstract To be confirmed