Toward a Digital Twin for real-time geometry assurance in individualized production

By R. Söderberg, Kristina Wärmefjord, J. Carlson, L. Lindkvist. 2017

518 citations recorded by the source corpus.

Subfields: Automotive Engineering, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/966fda9ea3589eda623b6d159d2671839d6b1946

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Samad M. E. Sepasgozar
2. Andrew Yeh-Ching Nee
3. Fei Tao

In Industrial and Manufacturing Engineering

1. Qinglin Qi
2. Hervé Panetto
3. Mario Lezoche
4. Michele Dassisti
5. Concetta Semeraro
6. Fei Tao
7. Andrew Yeh-Ching Nee
8. Samad M. E. Sepasgozar

Citations in the corpus

• Digital Twin in Industry: State-of-the-Art (2019, unknown): “discussed the DT application in real-time geometry assurance during the preproduction and production phases, based on a case study of the sheet metal assembly station [42].”
• Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison (2019, background): “As for a DT, simulation and seamless data transfer from one phase to the next throughout its life-cycle are central [27].”
• The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities (2021, background): “Söderberg et al. [20] focused on the application of optimization while defining a digital twin.”
• Defining a Digital Twin-based Cyber-Physical Production System for autonomous manufacturing in smart shop floors (2019, background): “Söderberg et al. (2017) also discussed Digital Twin-based real-time geometry assurance for individualised product production.”
• Digital twin-driven product design framework (2019, background): “Recently, Soderberg et al. studied digital twin-based real-time geometry assurance in individualised production (Söderberg et al. 2017).”
• Digital twin paradigm: A systematic literature review (2021, methodology): “16 (Söderberg et al., 2017) “a digital copy of a product or a production system, going across the design, pre-production, and production phases and performing real-time optimization.””
• Digital Twin for rotating machinery fault diagnosis in smart manufacturing (2018, methodology): “In Söderberg et al. (2017), Digital Twin is employed for the guarantee of product geometry assurance in the individualised production.”
• A Digital Twin-Based Approach for Designing and Multi-Objective Optimization of Hollow Glass Production Line (2017, background): “The initial design, resulting in late process organizations and operations, usually constitute a significant part of the system performance [15].”