Hybrid learning-based digital twin for manufacturing process: modeling framework and implementation

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019) · Journal of Computational Physics · doi:10.1016/j.jcp.2018.10.045 · Cited by 20,384 · see citing works

Physics-informed machine learning

Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., & Yang, L. (2021) · Nature Reviews Physics · doi:10.1038/s42254-021-00314-5 · Cited by 8,718 · see citing works

Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems

Grieves, M., & Vickers, J. (2017) · Transdisciplinary Perspectives on Complex Systems · doi:10.1007/978-3-319-38756-7_4 · Cited by 3,727 · see citing works

Geometric error measurement and compensation of machines—An update

Schwenke, H., Knapp, W., Haitjema, H., et al. (2008) · CIRP Annals · doi:10.1016/j.cirp.2008.09.008 · Cited by 1,009 · see citing works

Thermal issues in machine tools

Mayr, J., JEDRZEJEWSKI, J., Uhlmann, E., et al. (2012) · CIRP Annals · doi:10.1016/j.cirp.2012.05.008 · Cited by 907 · see citing works

Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems

von Rueden, L., Mayer, S., Beckh, K., Georgiev, B., Giesselbach, S., Heese, R., Kirsch, B., Walczak, M., Pfrommer, J., Pick, A., et al. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3079836 · Cited by 878 · see citing works

Machine tool feed drives

Altıntaş, Y., Verl, A. W., Brecher, C., et al. (2011) · CIRP Annals · doi:10.1016/j.cirp.2011.05.010 · Cited by 654 · see citing works

Machine tool spindle units

Abele, E., Altıntaş, Y., & Brecher, C. (2010) · CIRP Annals · doi:10.1016/j.cirp.2010.05.002 · Cited by 582 · see citing works

Transdisciplinary Perspectives on Complex Systems

Alves, A., Flumerfelt, S., & Kahlen, F.-J. (2016) · Springer eBooks · doi:10.1007/978-3-319-38756-7 · Cited by 545 · see citing works

Smart manufacturing process and system automation – A critical review of the standards and envisioned scenarios

Lu, Y., Xu, X. W., & Wang, L. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.06.010 · Cited by 531 · see citing works

A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin

Luo, W., Hu, T., Ye, Y., Zhang, C., & Wei, Y. (2020) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2020.101974 · Cited by 525 · see citing works

Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks

Zhu, Q., Liu, Z., & Yan, J. (2021) · Computational Mechanics · doi:10.1007/s00466-020-01952-9 · Cited by 515 · see citing works

Virtual Machine Tool

Altıntaş, Y., Brecher, C., Weck, M., et al. (2005) · CIRP Annals · doi:10.1016/s0007-8506(07)60022-5 · Cited by 478 · see citing works

A survey on AI-driven digital twins in Industry 4.0: smart manufacturing and advanced robotics

Huang, Z., Shen, Y., Li, J., Fey, M., & Brecher, C. (2021) · Sensors · doi:10.3390/s21196340 · Cited by 418 · see citing works

The Definitive ANTLR 4 Reference

Parr, T. (2013) · USF Scholarship Repository (University of San Francisco) · Cited by 381 · see citing works

Virtual process systems for part machining operations

Altıntaş, Y., Kersting, P., Biermann, D., et al. (2014) · CIRP Annals · doi:10.1016/j.cirp.2014.05.007 · Cited by 364 · see citing works

A framework for data-driven digital twins of smart manufacturing systems

Friederich, J., Francis, D. P., Lazarova‐Molnar, S., & Mohamed, N. (2021) · Computers in Industry · doi:10.1016/j.compind.2021.103586 · Cited by 348 · see citing works

Digital twin-driven supervised machine learning for the development of artificial intelligence applications in manufacturing

Alexopoulos, K., Nikolakis, N., & Chryssolouris, G. (2020) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2020.1747642 · Cited by 315 · see citing works

Evolution and future of manufacturing systems

ElMaraghy, H. A., Monostori, L., Schuh, G., & ElMaraghy, W. H. (2021) · CIRP Annals · doi:10.1016/j.cirp.2021.05.008 · Cited by 311 · see citing works

A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing

Wu, D., Liu, S., Zhang, L., Terpenny, J. P., Gao, R. X., Kurfess, T. R., & Guzzo, J. A. (2017) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2017.02.011 · Cited by 289 · see citing works

Machine Tool 4.0 for the new era of manufacturing

Xu, X. W. (2017) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-017-0300-7 · Cited by 210 · see citing works

A Cyber-Physical Machine Tools Platform using OPC UA and MTConnect

Liu, C., Vengayil, H., Lu, Y.-Q., & Xu, X. (2019) · Journal of manufacturing systems · doi:10.1016/j.jmsy.2019.04.006 · Cited by 209 · see citing works

A digital-twin visualized architecture for Flexible Manufacturing System

Fan, Y., Yang, J.-Z., Chen, J.-H., Hu, P., Wang, X., Xu, J.-C., & Zhou, B. (2021) · Journal of manufacturing systems · doi:10.1016/j.jmsy.2021.05.010 · Cited by 181 · see citing works

A systematic development method for cyber-physical machine tools

Liu, C., Vengayil, H., Zhong, R. Y., et al. (2018) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2018.02.001 · Cited by 162 · see citing works

Integration of virtual and on-line machining process control and monitoring

Altıntaş, Y., & Aslan, D. (2017) · CIRP Annals · doi:10.1016/j.cirp.2017.04.047 · Cited by 149 · see citing works

An automatic method for constructing machining process knowledge base from knowledge graph

Guo, L., Yan, F., Li, T., Tao, Y., & Lu, Y. (2021) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2021.102222 · Cited by 149 · see citing works

Testing the Digital Thread in Support of Model-Based Manufacturing and Inspection

Hedberg, T., Lubell, J., Fischer, L. S., Maggiano, L., & Feeney, A. B. (2016) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4032697 · Cited by 147 · see citing works

A framework for manufacturing system reconfiguration and optimisation utilising digital twins and modular artificial intelligence

Mo, F., Rehman, H. U., Monetti, F. M., Chaplin, J. C., SANDERSON, D. R., Popov, A. A., Maffei, A., & Ratchev, S. M. (2023) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2022.102524 · Cited by 146 · see citing works

A Computer Science Perspective on Digital Transformation in Production

Brauner, P., Dalibor, M., Jarke, M., Kunze, I., Koren, I., Lakemeyer, G., Liebenberg, M., Michael, J., Pennekamp, J., Quix, C., et al. (2022) · ACM Transactions on Internet of Things · doi:10.1145/3502265 · Cited by 145 · see citing works

A digital twin-driven hybrid approach for the prediction of performance degradation in transmission unit of CNC machine tool

Yang, X. M., Ran, Y., Zhang, G., Wang, H., Mu, Z., & Zhi, S. (2021) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2021.102230 · Cited by 139 · see citing works

A collaborative architecture of the industrial internet platform for manufacturing systems

Wang, J., Xu, C., Zhang, J., Bao, J., & Zhong, R. Y. (2019) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2019.101854 · Cited by 124 · see citing works

Digitalisation and servitisation of machine tools in the era of Industry 4.0: a review

Liu, C., Zheng, P., & Xu, X. W. (2021) · International Journal of Production Research · doi:10.1080/00207543.2021.1969462 · Cited by 124 · see citing works

Machine learning with domain knowledge for predictive quality monitoring in resistance spot welding

Zhou, B., Pychynski, T., Reischl, M., et al. (2022) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-021-01892-y · Cited by 113 · see citing works

On-line chatter detection in milling with hybrid machine learning and physics-based model

Rahimi, M. H., Huynh, H. N., & Altıntaş, Y. (2021) · CIRP journal of manufacturing science and technology · doi:10.1016/j.cirpj.2021.05.006 · Cited by 108 · see citing works

Enriching standards-based digital thread by fusing as-designed and as-inspected data using knowledge graphs

Kwon, S., Monnier, L. V., Barbau, R., & Bernstein, W. Z. (2020) · Advanced Engineering Informatics · doi:10.1016/j.aei.2020.101102 · Cited by 97 · see citing works

Toward Intelligent Machine Tool

Chen, J., Hu, P., Zhou, H., Yang, J., Xie, J., Jiang, Y., Gao, Z., & Zhang, C. (2019) · Engineering · doi:10.1016/j.eng.2019.07.018 · Cited by 88 · see citing works

Solid modeling of polyhedral objects by Layered Depth-Normal Images on the GPU

Wang, C. C. L., Leung, Y., & Chen, Y. (2010) · Computer-Aided Design · doi:10.1016/j.cad.2010.02.001 · Cited by 78 · see citing works

International Conference on Electrical, Computer and Energy Technologies

(2023) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet58911.2023.10389303 · Cited by 78 · see citing works

Using Graphs to Link Data Across the Product Lifecycle for Enabling Smart Manufacturing Digital Threads

Hedberg, T., Bajaj, M., & Camelio, J. A. (2019) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4044921 · Cited by 77 · see citing works

Reference architecture to integrate heterogeneous manufacturing systems for the digital thread

Helu, M., Hedberg, T., & Feeney, A. B. (2017) · CIRP journal of manufacturing science and technology · doi:10.1016/j.cirpj.2017.04.002 · Cited by 72 · see citing works

Application Research of Digital Twin-Driven Ship Intelligent Manufacturing System: Pipe Machining Production Line

Wu, Q., Mao, Y.-S., Chen, J., & Wang, C. (2021) · Journal of Marine Science and Engineering · doi:10.3390/jmse9030338 · Cited by 72 · see citing works

Edge intelligence-driven digital twin of CNC system: Architecture and deployment

Yu, H., Yu, D. I., Wang, C., Hu, Y., & Li, Y. (2022) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2022.102418 · Cited by 72 · see citing works

Estimation of the virtual workpiece quality by the use of a spindle-integrated process force measurement

Brecher, C., Eckel, H.-M., Motschke, T., Fey, M., & Epple, A. (2019) · CIRP Annals · doi:10.1016/j.cirp.2019.04.020 · Cited by 52 · see citing works

A standards-based approach for linking as-planned to as-fabricated product data

Helu, M., Joseph, A., & Thomas Hedberg, Jr. (2018) · Cirp Annals-manufacturing Technology · doi:10.1016/j.cirp.2018.04.039 · Cited by 44 · see citing works

Dynamic feature based adaptive process planning for energy-efficient NC machining

Wang, L., Wang, W., & Liu, D. (2017) · CIRP Annals · doi:10.1016/j.cirp.2017.04.015 · Cited by 41 · see citing works

The Industrial Internet of Things

Willner, A. (2018) · doi:10.1002/9781119456735.ch11 · Cited by 36 · see citing works

Tool deflection compensation by drive signal-based force reconstruction and process control

Denkena, B., Bergmann, B., & Stoppel, D. (2021) · Procedia CIRP · doi:10.1016/j.procir.2021.11.096 · Cited by 18 · see citing works

Edge computing-based virtual measuring machine for process-parallel prediction of workpiece quality in metal cutting

Huang, Z., Wiesch, M., Fey, M., & Brecher, C. (2022) · Procedia CIRP · doi:10.1016/j.procir.2022.04.059 · Cited by 14 · see citing works

Micro/Meso-scale Manufacturing

Dohda, K., Ni, J., & de Rooij, N. (2004) · Journal of Manufacturing Science and Engineering · doi:10.1115/1.1814125 · Cited by 8 · see citing works

Feeling Machine for Process Monitoring of Components with Stock Allowance

Denkena, B., Bergmann, B., & Witt, M. (2021) · Machines · doi:10.3390/machines9030053 · Cited by 8 · see citing works

Online Parameterization of a Milling Force Model using an Intelligent System Architecture and Bayesian Optimization

Schmucker, B., Trautwein, F., Hartl, R., et al. (2022) · Procedia CIRP · doi:10.1016/j.procir.2022.05.105 · Cited by 7 · see citing works

AI-Driven Digital Process Twin via Networked Digital Process Chain

Huang, Z., Xi, T., Fey, M., & Brecher, C. (2022) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc/picom/cbdcom/cy55231.2022.9928016 · Cited by 3 · see citing works

Redefining the Programmer: Human-AI Collaboration, LLMs, and Security in Modern Software Engineering

Cruz, E. D. L., Le, H., Meduri, K., Nadella, G. S., & Gonaygunta, H. (2025) · Computers, Materials & Continua · doi:10.32604/cmc.2025.068137 · Cited by 9 · see citing works

Micro Manufacturing and Digital Manufacturing——Key Areas of Mechatronics in 21st Century

Tang, R. (2001) · Mechanical & Electrical Engineering Magazine

The research progress and prospect of Digital Manufacturing Science

Zhou, Z. D., Ai, Q., Liu, Q., et al. (2009) · International Journal of Mechatronics and Manufacturing Systems · doi:10.1504/ijmms.2009.024348

Digital Factory Approach for Flexible and Efficient Manufacturing Systems in the Aerospace Industry

Caggiano, A., Caiazzo, F., & Teti, R. (2015) · Procedia CIRP · doi:10.1016/j.procir.2015.08.015 · Cited by 35 · see citing works

Research and Application of Key Technologies of Intelligent Manufacturing Based on 5G Vehicle

Tang, S., Huo, M., Zhang, Y., et al. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-9968-0_111

The Review of Manufacturing Technology for Aircraft Structural Part

Ma, F., Cao, W., Luo, Y., et al. (2016) · Procedia CIRP · doi:10.1016/j.procir.2016.10.117 · Cited by 26 · see citing works