Digital Twins in Additive Manufacturing: Bridging Virtual Simulation and Physical Production

The design, production, and testing of things have all been transformed by additive manufacturing (AM). Nonetheless, there are still many difficulties because of the intricate relationship between material characteristics, process parameters, and product performance. Trial-and-error methods, higher production costs, and lower product quality might result from these difficulties. A possible way to close the gap between AM’s virtual simulation and practical manufacturing is through digital twins, which are virtual representation of physical systems. The scope and focus of this Collection will be:

• Design and implementation of digital twin systems for AM, including data integration, simulation, and visualization.

• Development and application of simulation models for AM processes, materials, and products, including thermal, mechanical, and optical phenomena.

• Applications of machine learning and artificial intelligence in digital twins for AM, such as predictive modeling, anomaly detection, and optimization.

• Experimental validation and verification of digital twin predictions, including comparison with physical experiments and testing.

• Real-world applications of digital twins in AM, including success stories, challenges, and lessons learned.

• Data-driven approaches for digital twin development, including data acquisition, processing, and analytics.

• Integration of digital twins with cyber-physical systems, including IoT devices, sensors, and actuators.

• Human-machine interaction aspects of digital twins, including visualization, feedback, and control.

The goals and expected outcomes will be:

• Provide a comprehensive overview of the current state-of-the-art in digital twins for AM.

• Identify key challenges and research gaps in the development and application of digital twins in AM.

• Foster collaboration and knowledge sharing among researchers, industry practitioners, and stakeholders.

• Inspire new research directions and innovation in the field of digital twins for AM.

• Showcase successful applications and case studies of digital twins in AM, highlighting their benefits and impact.

Keywords:

• AI/ML

• IoT

• Data analytics

• Simulation modeling

• Process optimization

This Collection supports and amplifies research related to SDG 9.