Digital twin-based process reuse and evaluation approach for smart process planning

Digital twin-driven product design, manufacturing and service with big data

Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-017-0233-1 · Cited by 2,920

Digital twin and big data towards smart manufacturing and Industry 4.0: 360 degree comparison

Qi, Q., & Tao, F. (2018) · IEEE Access · doi:10.1109/access.2018.2793265 · Cited by 1,716

Digital twin-based smart production management and control framework for the complex product assembly shop-floor

Zhuang, C., Liu, J., & Xiong, H. (2018) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-018-1617-6 · Cited by 700

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

Söderberg, R., Wärmefjord, K., Carlson, J. S., & Lindkvist, L. (2017) · CIRP Annals · doi:10.1016/j.cirp.2017.04.038 · Cited by 543

A Digital Twin-Based Approach for Designing and Multi-Objective Optimization of Hollow Glass Production Line

Zhang, H., Liu, Q., Chen, X., Zhang, D., & Leng, J. (2017) · IEEE Access · doi:10.1109/access.2017.2766453 · Cited by 471

Engineers' CAx education—it's not only CAD

Dankwort, C. W., Weidlich, R., Guenther, B., & Blaurock, J. (2004) · Computer-Aided Design · doi:10.1016/j.cad.2004.02.011 · Cited by 117

A new heuristic for integrated process planning and scheduling in reconfigurable manufacturing systems

Bensmaïne, A., Dahane, M., & Benyoucef, L. (2014) · International Journal of Production Research · doi:10.1080/00207543.2013.878056 · Cited by 99

Paradigm shift: unified and associative feature-based concurrent and collaborative engineering

Ma, Y., Chen, G., & Thimm, G. (2008) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-008-0128-y · Cited by 95

Capturing and reusing knowledge in engineering change management: A case of automobile development

Lee, H. J., Ahn, H. J., Kim, J. W., & Park, S. J. (2006) · Information Systems Frontiers · doi:10.1007/s10796-006-9009-0 · Cited by 89

Integrated manufacturing features and Design-for-manufacture guidelines for reducing product cost under CAD/CAM environment

Hoque, A., Halder, P. K., Parvez, M., & Szecsi, T. (2013) · Computers & Industrial Engineering · doi:10.1016/j.cie.2013.08.016 · Cited by 74

Automatic machining feature recognition from STEP files

Malleswari, V. N., & Pragvamsa, P. G. (2023) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2022.2162590 · Cited by 6

Digital Twin with a Perspective from Manufacturing Industry

Wang, H., Chen, S., Sami, M. S. U. I., Rahman, F., & Tehranipoor, M. M. (2020) · Springer eBooks · doi:10.1007/978-3-030-64448-2_2 · Cited by 6

Deep fusion of fragmented process knowledge for process route planning

Zhang, Y., Wang, H., Huang, R., Wang, Z. X., Zhang, H., Liang, J., Zhang, S., & Xu, C. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103887 · Cited by 4

Framing the Digital Business Process Twin: From a Holistic Maturity Model to a Specific and Substantial Use Case in the Automotive Industry

Rabe, M., & Kilic, E. (2024) · Lecture notes in business information processing · doi:10.1007/978-3-031-50974-2_27 · Cited by 4

Intelligent process instruction generation method: Combining large-scale pre-trained language model and multi-reasoning

Wu, T., Guo, Y. H., Huang, S., Fang, W., Long, Z., & Qian, W. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.114038 · Cited by 4

Supply Network 5.0 Life Cycle

Nicoletti, B. (2023) · Springer eBooks · doi:10.1007/978-3-031-22032-6_7 · Cited by 3

Digital twin-based tig welding quality prediction using electrode tip angle degradation influencing Industry 5.0 in manufacturing sector

Thangavel, S., Maheswari, C., & Priyanka, E. B. (2024) · Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering · doi:10.1177/09544089241253939 · Cited by 3

Towards a smart connector for dynamic interoperability in agile enterprises

Belkadi, F., Vieille, J., & Tanous, B. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.10.058 · Cited by 3

AML-Based Multi-Dimensional Co-Evolution Approach Supported by Blockchain: Architecture Design and Case Study on Intelligent Production Lines for Industry 4.0

Ding, K., Gerhard, D., & Fan, L. (2025) · Information · doi:10.3390/info16030243 · Cited by 3

Information Modelling Method of As-built Process Data

Dai, S. L., Zhao, G. L., Yu, Y., & Bao, Q. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1824/1/012013 · Cited by 3

An approach for mining typical machining process plans integrating representation learning and spectral clustering

Xu, X., Huang, Z., Wan, Y., Shao, P., Qiao, L., Chen, C., Anwer, N., & Qie, Y. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-17334-5 · Cited by 2

Definitions and principles of digital twins

Fang, Z., Lu, Q., Xie, X., Parlikad, A. K., Schooling, J. M., & Pitt, M. (2022) · doi:10.1680/dtbe.65802.005 · Cited by 2

Digital Twin and Extended Reality: Strategic Approach and Practical Implementation

Rocca, R. L., Santacruz, R. F. B., Sassanelli, C., Rosa, P., Fumagalli, L., & Negri, E. (2023) · Springer handbooks · doi:10.1007/978-3-030-67822-7_34 · Cited by 2

In-process identification of milling parameters based on digital twin driven intelligent algorithmPreprint

Zheng, C. M., Zhang, L., Kang, Y.-H., Zhan, Y., & Xu, Y. (2022) · Research Square · doi:10.21203/rs.3.rs-1560913/v1 · Cited by 2

Closed-Loop Digital Twin for the Product LifecyclePreprint

Durão, L. F. C. S., Zancul, E. D. S., & Schützer, K. (2021) · Research Square · doi:10.21203/rs.3.rs-186777/v1 · Cited by 2

Preliminary Manufacturing Cell Design in Digital Factory

Vosniakos, G.-C., Gogouvitis, X. V., & Panos, E. (2023) · Lecture notes in mechanical engineering · doi:10.1007/978-3-031-17629-6_48 · Cited by 1

Intelligent Retrieval and Reuse in Product Manufacturing: A Comprehensive Analysis of Current Practices and Future Directions

Wu, Z., Liang, C., Wang, J., & Chen, Z. (2025) · Journal of Intelligent & Fuzzy Systems · doi:10.1177/18758967251367065 · Cited by 1

The metaverse: a product-service innovation canvas for co-creating and capturing value

Boakye, D., & Sarpong, D. (2025) · Journal of Business Strategy · doi:10.1108/jbs-08-2025-0194 · Cited by 1

Digital upgrade of a bandsaw machine through an innovative guidance system based on the digital shadow concept

Scalzo, F. (2023) · Materials research proceedings · doi:10.21741/9781644902714-34 · Cited by 1

Digital twin driven process design evaluation

Liu, X., Liu, J., Zhou, H., & Ni, Z. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-818918-4.00012-9 · Cited by 1

Analysis of the quasi-stability of kinematic parameters for manipulators system during the docking process using the Digital Twin approach

Jędrzejowski, M., Błachowicz, T., Krafczyk, W., Pyka, W., Tokarczyk, O., Chudý, M., Bzymek, A., Sokół, Z., & Ehrmann, A. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1950/1/012019 · Cited by 1

Graph-enabled digital twins for intelligent product lifecycle management: a multi-dimensional approach to design, manufacturing, and supply chain transformation

Lim, K. Y. H. (2023) · doi:10.32657/10356/176020 · Cited by 2

Application of digital twins in specific manufacturing processes

Olender-Skóra, M. (2021) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1182/1/012058

Research on Assembly Process Simulation of Construction Machinery Arm Based on Digital Twin

Zhang, Y., Wang, G., Zhang, D., & Zhang, Q. (2021) · International Electronic Conference on Sensors and Applications (ECSA) · doi:10.3390/ecsa-8-11331

Application of a Digital Twin for Manufacturing Process Simulation

Olender-Skóra, M., & Banaś, W. (2022) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2198/1/012029

Modelo de referência do gêmeo digital no ciclo de vida do produto.

Facchini, D. S. (2023) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.3.2023.tde-05022024-102747

Digital twin for smart factory: Usage of digital twin to reduce manufacturing defects

Sundaram, M., Varala, V., & Kumar, N., V (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-24132-1.00006-5

A Process Decision-Making Method for Planar Machining of Box-Type Components

Shi, Z., Huang, M., Tang, Z., Hu, Z.-Q., & Hu, W. (2025) · Applied Sciences · doi:10.3390/app15074029

Implementation of residual stress models in a technological material removal simulation

Denkena, B., Heide, K. M., Pillkahn, P., & Schlenker, F. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-026-18579-4

Theoretical Foundations of Mechanical Design for Improving Machine Productivity during the Cutting Process

Alsigar, M., Alhafadhi, M., Lafta, A., Mayoof, O., & Al-Murshidi, H. (2026) · Advances in science and technology · doi:10.4028/p-3ohet2

Motor Health State Prediction Based on Temperature Signals Driven by Digital Twins

Ma, J., Yang, W., Sitahong, A., & Huang, Y. (2026) · doi:10.1109/icbdse70225.2026.11635731

A novel milling operation strategy prediction method based on boundary representation learning

Niu, S., Ning, F., Cai, M., Tong, X., & Shi, Y. (2026) · CIRP journal of manufacturing science and technology · doi:10.1016/j.cirpj.2026.08.011

Dynamic Evaluation Method of Machining Process Planning Based on Digital Twin

Liu, J., Zhou, H., Liu, X., Tian, G., Wu, M., Cao, L., & Wang, W. (2019) · IEEE Access · doi:10.1109/access.2019.2893309 · Cited by 167

Digital twin driven process design evaluation

Liu, X., Liu, J., Zhou, H., & Ni, Z. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-818918-4.00012-9 · Cited by 1

Dynamic design method of digital twin process model driven by knowledge-evolution machining features

Liu, J., Zhao, P., Jing, X., Cao, X., Sheng, S., Zhou, H., Liu, X., & Feng, F. (2021) · International Journal of Production Research · doi:10.1080/00207543.2021.1887531 · Cited by 50

Defining a feature-level digital twin process model by extracting machining features from MBD models for intelligent process planning

Li, J., Zhou, G., Zhang, C., Jun-sheng, H., Chang, F., & Matta, A. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02406-2 · Cited by 16

The development of a digital twin for machining processes for the application in aerospace industry

Hänel, A., Schnellhardt, T., Wenkler, E., Nestler, A., Brosius, A., Corinth, C., Fay, A., & Ihlenfeldt, S. (2020) · Procedia CIRP · doi:10.1016/j.procir.2020.04.017 · Cited by 91