{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T17:35:05Z","timestamp":1777743305731,"version":"3.51.4"},"reference-count":84,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ICA"],"published-print":{"date-parts":[[2021,8,27]]},"abstract":"<jats:p>Manufacturing digitalisation is a critical part of the transition towards Industry 4.0. Digital twin plays a significant role as the instrument that enables digital access to precise real-time information about physical objects and supports the optimisation of the related processes through conversion of the big data associated with them into actionable information. A number of frameworks and conceptual models has been proposed in the research literature that addresses the requirements and benefits of digital twins, yet their applications are explored to a lesser extent. A time-domain machining vibration model based on a generative adversarial network (GAN) is proposed as a digital twin component in this paper. The developed conditional StyleGAN architecture enables (1) the extraction of knowledge from existing models and (2) a data-driven simulation applicable for production process optimisation. A novel solution to the challenges in GAN analysis is then developed, where the comparison of maps of generative accuracy and sensitivity reveals patterns of similarity between these metrics. The sensitivity analysis is also extended to the mid-layer network level, identifying the sources of abnormal generative behaviour. This provides a sensitivity-based simulation uncertainty estimate, which is important for validation of the optimal process conditions derived from the proposed model.<\/jats:p>","DOI":"10.3233\/ica-210662","type":"journal-article","created":{"date-parts":[[2021,7,27]],"date-time":"2021-07-27T13:10:26Z","timestamp":1627391426000},"page":"399-415","source":"Crossref","is-referenced-by-count":19,"title":["Conditional StyleGAN modelling and analysis for a machining digital twin"],"prefix":"10.1177","volume":"28","author":[{"given":"Evgeny","family":"Zotov","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ashutosh","family":"Tiwari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Visakan","family":"Kadirkamanathan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/ICA-210662_ref1","unstructured":"Henning K, Wolfgang W, Johannes H. Recommendations for implementing the strategic initiative INDUSTRIE 4.0; Technical Report April, 2013."},{"key":"10.3233\/ICA-210662_ref2","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1145\/1951365.1951432","article-title":"Big data and cloud computing","author":"Agrawal","year":"2011","journal-title":"Proceedings of the 14th International Conference on Extending Database Technology\u00a0\u2013 EDBT\/ICDT \u201911. New York, USA: ACM Press"},{"issue":"5","key":"10.3233\/ICA-210662_ref3","doi-asserted-by":"publisher","first-page":"1050","DOI":"10.1007\/s10033-017-0164-7","article-title":"Challenges and requirements for the application of industry 4.0: A special insight with the usage of cyber-physical system","volume":"30","author":"Mueller","year":"2017","journal-title":"Chinese Journal of Mechanical Engineering (English Edition)"},{"issue":"5","key":"10.3233\/ICA-210662_ref4","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.1080\/00207543.2016.1201604","article-title":"An event-driven manufacturing information system architecture for Industry 4.0","volume":"55","author":"Theorin","year":"2017","journal-title":"International Journal of Production Research"},{"key":"10.3233\/ICA-210662_ref5","doi-asserted-by":"publisher","first-page":"670","DOI":"10.1016\/j.procir.2017.03.346","article-title":"Mobilizing SMEs towards industrie 4.0-enabled smart products","volume":"63","author":"Issa","year":"2017","journal-title":"Procedia CIRP"},{"key":"10.3233\/ICA-210662_ref6","first-page":"27","article-title":"DRAFT modeling, simulation, information technology & processing roadmap\u00a0\u2013 technology area 11","author":"Shafto","year":"2010","journal-title":"National Aeronautics and Space Administration"},{"key":"10.3233\/ICA-210662_ref7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2514\/6.2016-5470","article-title":"Architecture to geometry\u00a0\u2013 Integrating system models with mechanical design","author":"Bajaj","year":"2016","journal-title":"AIAA Space and Astronautics Forum and Exposition, SPACE 2016"},{"key":"10.3233\/ICA-210662_ref8","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/978-3-319-38756-7_4","article-title":"Digital Twin: Mitigating unpredictable, undesirable emergent behavior in complex systems","author":"Grieves","year":"2017","journal-title":"Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches. Springer International Publishing"},{"issue":"9-12","key":"10.3233\/ICA-210662_ref9","doi-asserted-by":"crossref","first-page":"3563","DOI":"10.1007\/s00170-017-0233-1","article-title":"Digital twin-driven product design, manufacturing and service with big data","volume":"94","author":"Tao","year":"2018","journal-title":"International Journal of Advanced Manufacturing Technology"},{"key":"10.3233\/ICA-210662_ref10","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1016\/j.promfg.2017.07.198","article-title":"A review of the roles of digital twin in CPS-based production systems","volume":"11","author":"Negri","year":"2017","journal-title":"Procedia Manufacturing"},{"key":"10.3233\/ICA-210662_ref11","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.compind.2018.10.008","article-title":"A Bayesian framework to estimate part quality and associated uncertainties in multistage manufacturing","volume":"105","author":"Papananias","year":"2019","journal-title":"Computers in Industry"},{"key":"10.3233\/ICA-210662_ref12","unstructured":"EPSRC Future Metrology Hub. UK metrology research roadmap; 2020."},{"issue":"2","key":"10.3233\/ICA-210662_ref13","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1016\/S0007-8506(07)60032-8","article-title":"Chatter stability of metal cutting and grinding","volume":"53","author":"Altintas","year":"2004","journal-title":"CIRP Annals\u00a0\u2013 Manufacturing Technology"},{"issue":"3","key":"10.3233\/ICA-210662_ref14","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1115\/1.1580852","article-title":"An improved time domain simulation for dynamic milling at small radial immersions","volume":"125","author":"Campomanes","year":"2003","journal-title":"Journal of Manufacturing Science and Engineering, Transactions of the ASME"},{"issue":"15","key":"10.3233\/ICA-210662_ref15","doi-asserted-by":"publisher","first-page":"2154","DOI":"10.1016\/j.jmatprotec.2010.07.033","article-title":"Modelling and simulation of micro-milling cutting forces","volume":"210","author":"Afazov","year":"2010","journal-title":"Journal of Materials Processing Technology"},{"issue":"5","key":"10.3233\/ICA-210662_ref16","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1016\/S0890-6955(99)00080-2","article-title":"Process simulation using finite element method \u2013 prediction of cutting forces, tool stresses and temperatures in high-speed flat end milling","volume":"40","author":"\u00d6zel","year":"2000","journal-title":"International Journal of Machine Tools and Manufacture"},{"key":"10.3233\/ICA-210662_ref17","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.jmatprotec.2015.02.019","article-title":"3-D finite element process simulation of micro-end milling Ti-6Al-4V titanium alloy: Experimental validations on chip flow and tool wear","volume":"221","author":"Thepsonthi","year":"2015","journal-title":"Journal of Materials Processing Technology"},{"key":"10.3233\/ICA-210662_ref18","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1016\/j.compstruct.2017.06.012","article-title":"A review on finite element method for machining of composite materials","volume":"176","author":"Shetty","year":"2017","journal-title":"Composite Structures"},{"issue":"2","key":"10.3233\/ICA-210662_ref19","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1016\/j.cirp.2014.05.007","article-title":"Virtual process systems for part machining operations","volume":"63","author":"Altintas","year":"2014","journal-title":"CIRP Annals\u00a0\u2013 Manufacturing Technology"},{"issue":"2","key":"10.3233\/ICA-210662_ref20","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1115\/1.2899674","article-title":"Overview of modeling and simulation of the milling process","volume":"113","author":"Smith","year":"1991","journal-title":"Journal of Engineering for Industry"},{"key":"10.3233\/ICA-210662_ref21","unstructured":"Greis NP, Nogueira ML, Bhattacharya S, Schmitz T. Physics-guided machine learning for self-aware machining. in: 2020 AAAI Spring Symposium on AI and Manufacturing; 2020."},{"issue":"2","key":"10.3233\/ICA-210662_ref22","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1016\/S0007-8506(07)62995-3","article-title":"Task specific uncertainty in coordinate measurement","volume":"50","author":"Wilhelm","year":"2001","journal-title":"CIRP Annals\u00a0\u2013 Manufacturing Technology"},{"issue":"4","key":"10.3233\/ICA-210662_ref23","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/S0952-1976(03)00078-2","article-title":"AI and machine learning techniques for managing complexity, changes and uncertainties in manufacturing","volume":"16","author":"Monostori","year":"2003","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"2","key":"10.3233\/ICA-210662_ref24","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1016\/j.cirp.2012.05.001","article-title":"Complexity in engineering design and manufacturing","volume":"61","author":"Elmaraghy","year":"2012","journal-title":"CIRP Annals\u00a0\u2013 Manufacturing Technology"},{"issue":"2","key":"10.3233\/ICA-210662_ref25","doi-asserted-by":"publisher","first-page":"187","DOI":"10.3233\/ICA-150483","article-title":"An integrated feature-based dynamic control system for on-line machining, inspection and monitoring","volume":"22","author":"Li","year":"2015","journal-title":"Integrated Computer-Aided Engineering"},{"key":"10.3233\/ICA-210662_ref26","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.arcontrol.2016.09.008","article-title":"Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges","volume":"42","author":"Tidriri","year":"2016","journal-title":"Annual Reviews in Control"},{"issue":"1","key":"10.3233\/ICA-210662_ref27","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1051\/ijmqe\/2012032","article-title":"Uncertainty associated with form assessment in coordinate metrology","volume":"4","author":"Forbes","year":"2013","journal-title":"International Journal of Metrology and Quality Engineering"},{"key":"10.3233\/ICA-210662_ref28","first-page":"185","article-title":"Data-driven monitoring of cyber-physical systems leveraging on big data and the internet-of-things for diagnosis and control","volume":"1507","author":"Niggemann","year":"2015","journal-title":"CEUR Workshop Proceedings"},{"key":"10.3233\/ICA-210662_ref29","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1016\/j.procir.2017.12.213","article-title":"Online learning of stability lobe diagrams in milling","volume":"67","author":"Friedrich","year":"2018","journal-title":"Procedia CIRP"},{"issue":"7553","key":"10.3233\/ICA-210662_ref30","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"issue":"7676","key":"10.3233\/ICA-210662_ref31","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1038\/nature24270","article-title":"Mastering the game of Go without human knowledge","volume":"550","author":"Silver","year":"2017","journal-title":"Nature"},{"key":"10.3233\/ICA-210662_ref32","doi-asserted-by":"crossref","unstructured":"Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. 2015; 349(6245).","DOI":"10.1126\/science.aaa8415"},{"issue":"3","key":"10.3233\/ICA-210662_ref33","doi-asserted-by":"publisher","first-page":"273","DOI":"10.3233\/ICA-180596","article-title":"Multi-object tracking with discriminant correlation filter based deep learning tracker","volume":"26","author":"Yang","year":"2019","journal-title":"Integrated Computer-Aided Engineering"},{"issue":"1","key":"10.3233\/ICA-210662_ref34","doi-asserted-by":"publisher","first-page":"37","DOI":"10.3233\/ICA-180587","article-title":"Neural networks for recognizing human activities in home-like environments","volume":"26","author":"Rodriguez\u00a0Lera","year":"2018","journal-title":"Integrated Computer-Aided Engineering"},{"issue":"1","key":"10.3233\/ICA-210662_ref35","doi-asserted-by":"publisher","first-page":"85","DOI":"10.3233\/ICA-180584","article-title":"DeepEye: Deep convolutional network for pupil detection in real environments","volume":"26","author":"Vera-Olmos","year":"2018","journal-title":"Integrated Computer-Aided Engineering"},{"issue":"5","key":"10.3233\/ICA-210662_ref36","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1243\/095440505X32274","article-title":"Machine-learning techniques and their applications in manufacturing","volume":"219","author":"Pham","year":"2005","journal-title":"Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture"},{"key":"10.3233\/ICA-210662_ref37","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.neucom.2020.07.088","article-title":"A comprehensive review on convolutional neural network in machine fault diagnosis","volume":"417","author":"Jiao","year":"2020","journal-title":"Neurocomputing"},{"key":"10.3233\/ICA-210662_ref38","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.ymssp.2018.05.050","article-title":"Deep learning and its applications to machine health monitoring","volume":"115","author":"Zhao","year":"2019","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.3233\/ICA-210662_ref39","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1016\/j.addma.2017.11.012","article-title":"Acoustic emission for in situ quality monitoring in additive manufacturing using spectral convolutional neural networks","volume":"21","author":"Shevchik","year":"2018","journal-title":"Additive Manufacturing"},{"key":"10.3233\/ICA-210662_ref40","doi-asserted-by":"publisher","DOI":"10.3390\/jmmp3020045"},{"key":"10.3233\/ICA-210662_ref41","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.measurement.2018.06.028","article-title":"A machine-learning based solution for chatter prediction in heavy-duty milling machines","volume":"128","author":"Oleaga","year":"2018","journal-title":"Measurement: Journal of the International Measurement Confederation"},{"issue":"7","key":"10.3233\/ICA-210662_ref42","doi-asserted-by":"crossref","first-page":"5990","DOI":"10.1109\/TIE.2017.2774777","article-title":"A new convolutional neural network-based data-driven fault diagnosis method","volume":"65","author":"Wen","year":"2018","journal-title":"IEEE Transactions on Industrial Electronics"},{"key":"10.3233\/ICA-210662_ref43","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1109\/MED.2019.8798497","article-title":"Surface defect detection for automated inspection systems using convolutional neural networks","author":"Konrad","year":"2019","journal-title":"27th Mediterranean Conference on Control and Automation, MED 2019\u00a0\u2013 Proceedings"},{"issue":"4","key":"10.3233\/ICA-210662_ref44","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1007\/s40684-018-0057-y","article-title":"Smart Machining Process Using Machine Learning: A Review and Perspective on Machining Industry","volume":"5","author":"Kim","year":"2018","journal-title":"International Journal of Precision Engineering and Manufacturing\u00a0\u2013 Green Technology"},{"key":"10.3233\/ICA-210662_ref45","unstructured":"Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et\u00a0al. Generative adversarial networks; 2014. Available from: http:\/\/arxiv.org\/abs\/1406.2661."},{"key":"10.3233\/ICA-210662_ref46","unstructured":"Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks; 2015. Available from: http:\/\/arxiv.org\/abs\/1511.06434."},{"key":"10.3233\/ICA-210662_ref47","unstructured":"Brock A, Donahue J, Simonyan K. Large scale GAN training for high fidelity natural image synthesis. 2018. Available from: http:\/\/arxiv.org\/abs\/1809.11096."},{"key":"10.3233\/ICA-210662_ref48","doi-asserted-by":"crossref","first-page":"5908","DOI":"10.1109\/ICCV.2017.629","article-title":"StackGAN: Text to photo-realistic image synthesis with stacked generative adversarial networks","author":"Zhang","year":"2017","journal-title":"2017 IEEE International Conference on Computer Vision (ICCV). vol. 2017-Octob. IEEE"},{"key":"10.3233\/ICA-210662_ref49","unstructured":"Donahue C, McAuley J, Puckette M. Adversarial audio synthesis. 2018. Available from: http:\/\/arxiv.org\/abs\/1802.04208."},{"key":"10.3233\/ICA-210662_ref50","doi-asserted-by":"crossref","unstructured":"Yu L, Zhang W, Wang J, Yu Y. SeqGAN: Sequence generative adversarial nets with policy gradient; 2016.","DOI":"10.1609\/aaai.v31i1.10804"},{"key":"10.3233\/ICA-210662_ref51","unstructured":"Saatchi Y, Wilson AG. Bayesian GAN; 2017. Available from: http:\/\/arxiv.org\/abs\/1705.09558."},{"key":"10.3233\/ICA-210662_ref52","unstructured":"Arjovsky M, Chintala S, Bottou L. Wasserstein GAN; 2017. Available from: http:\/\/arxiv.org\/abs\/1701.07875."},{"key":"10.3233\/ICA-210662_ref53","unstructured":"Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville A. Improved Training of Wasserstein GANs; 2017. Available from: http:\/\/arxiv.org\/abs\/1704.00028."},{"key":"10.3233\/ICA-210662_ref54","unstructured":"Karras T, Aila T, Laine S, Lehtinen J. Progressive growing of gans for improved quality, stability, and variation; 2017. Available from: http:\/\/arxiv.org\/abs\/1710.10196."},{"key":"10.3233\/ICA-210662_ref55","unstructured":"Mirza M, Osindero S. Conditional generative adversarial nets; 2014. Available from: http:\/\/arxiv.org\/abs\/1411.1784."},{"key":"10.3233\/ICA-210662_ref56","unstructured":"Chen X, Duan Y, Houthooft R, Schulman J, Sutskever I, Abbeel P, Cremers D, Reid I, Saito H, Yang MH, editors. InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets. Cham: Springer International Publishing; 2016. Available from: http:\/\/arxiv.org\/abs\/1606.03657."},{"key":"10.3233\/ICA-210662_ref57","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/978-3-319-71249-9_8","article-title":"Guiding InfoGAN with semi-supervision","author":"Spurr","year":"2017","journal-title":"Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science. Cham: Springer International Publishing"},{"key":"10.3233\/ICA-210662_ref58","first-page":"1","article-title":"Gansynth: Adversarial neural audio synthesis","author":"Engel","year":"2019","journal-title":"7th International Conference on Learning Representations, ICLR 2019"},{"key":"10.3233\/ICA-210662_ref59","first-page":"2506","article-title":"Voice impersonation using generative adversarial networks","volume":"2018","author":"Gao","year":"2018","journal-title":"ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing\u00a0\u2013 Proceedings"},{"key":"10.3233\/ICA-210662_ref60","first-page":"5679","article-title":"Speech waveform synthesis from mfcc sequences with generative adversarial networks","volume":"2018","author":"Juvela","year":"2018","journal-title":"ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing\u00a0\u2013 Proceedings"},{"key":"10.3233\/ICA-210662_ref61","first-page":"1","article-title":"RelGAN: Relational generative adversarial networks for text generation","author":"Nie","year":"2019","journal-title":"ICLR"},{"key":"10.3233\/ICA-210662_ref62","doi-asserted-by":"crossref","unstructured":"Karras T, Laine S, Aila T. A style-based generator architecture for generative adversarial networks. 2018. Available from: http:\/\/arxiv.org\/abs\/1812.04948.","DOI":"10.1109\/CVPR.2019.00453"},{"issue":"12","key":"10.3233\/ICA-210662_ref63","doi-asserted-by":"crossref","first-page":"6690","DOI":"10.1002\/mp.12625","article-title":"Deep reinforcement learning for automated radiation adaptation in lung cancer","volume":"44","author":"Tseng","year":"2017","journal-title":"Medical Physics"},{"key":"10.3233\/ICA-210662_ref64","unstructured":"Esteban C, Hyland SL, R\u00e4tsch G. Real-valued (medical) time series generation with recurrent conditional GANs; 2017. Available from: http:\/\/arxiv.org\/abs\/1706.02633."},{"issue":"3","key":"10.3233\/ICA-210662_ref65","doi-asserted-by":"crossref","first-page":"3265","DOI":"10.1109\/TPWRS.2018.2794541","article-title":"Model-free renewable scenario generation using generative adversarial networks","volume":"33","author":"Chen","year":"2018","journal-title":"IEEE Transactions on Power Systems"},{"key":"10.3233\/ICA-210662_ref66","unstructured":"Mogren O. C-RNN-GAN: Continuous recurrent neural networks with adversarial training; 2016. Available from: http:\/\/arxiv.org\/abs\/1611.09904."},{"key":"10.3233\/ICA-210662_ref67","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.neucom.2018.05.024","article-title":"An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition","volume":"310","author":"Wang","year":"2018","journal-title":"Neurocomputing"},{"key":"10.3233\/ICA-210662_ref68","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1016\/j.knosys.2018.12.019","article-title":"A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults","volume":"165","author":"Han","year":"2019","journal-title":"Knowledge-Based Systems"},{"key":"10.3233\/ICA-210662_ref69","first-page":"1","article-title":"Data augmentation for intelligent manufacturing with generative adversarial framework","author":"Wang","year":"2019","journal-title":"1st International Conference on Industrial Artificial Intelligence, IAI 2019"},{"issue":"3","key":"10.3233\/ICA-210662_ref70","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1109\/TSM.2019.2925361","article-title":"AdaBalGAN: An improved generative adversarial network with imbalanced learning for wafer defective pattern recognition","volume":"32","author":"Wang","year":"2019","journal-title":"IEEE Transactions on Semiconductor Manufacturing"},{"issue":"4","key":"10.3233\/ICA-210662_ref71","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1016\/j.eng.2019.04.012","article-title":"Applying neural-network-based machine learning to additive manufacturing: Current applications, challenges, and future perspectives","volume":"5","author":"Qi","year":"2019","journal-title":"Engineering"},{"key":"10.3233\/ICA-210662_ref72","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.jmsy.2018.01.003","article-title":"Deep learning for smart manufacturing: Methods and applications","volume":"48","author":"Wang","year":"2018","journal-title":"Journal of Manufacturing Systems"},{"issue":"0","key":"10.3233\/ICA-210662_ref73","first-page":"1","article-title":"Convolutional and generative adversarial neural networks in manufacturing","volume":"0","author":"Kusiak","year":"2019","journal-title":"International Journal of Production Research"},{"key":"10.3233\/ICA-210662_ref74","first-page":"1","article-title":"A deep learning-based method for the design of microstructural materials","author":"Tan","year":"2019","journal-title":"Structural and Multidisciplinary Optimization"},{"issue":"i","key":"10.3233\/ICA-210662_ref75","first-page":"1","article-title":"GAN-SRAF: Sub-resolution assist feature generation using conditional generative adversarial networks","author":"Alawieh","year":"2019","journal-title":"Proceedings\u00a0\u2013 Design Automation Conference"},{"issue":"2019","key":"10.3233\/ICA-210662_ref76","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1016\/j.promfg.2020.05.059","article-title":"Anomaly detection in milling tools using acoustic signals and generative adversarial networks","volume":"48","author":"Cooper","year":"2020","journal-title":"Procedia Manufacturing"},{"issue":"10","key":"10.3233\/ICA-210662_ref77","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.3233\/ICA-210662_ref78","doi-asserted-by":"crossref","unstructured":"Schmitz TL, Smith KS. Machining dynamics. Cham: Springer International Publishing; 2019.","DOI":"10.1007\/978-3-319-93707-6"},{"key":"10.3233\/ICA-210662_ref79","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1007\/978-3-030-48791-1_14","article-title":"Towards a digital twin with generative adversarial network modelling of machining vibration","author":"Zotov","year":"2020","journal-title":"Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. Cham: Springer International Publishing"},{"key":"10.3233\/ICA-210662_ref80","doi-asserted-by":"crossref","unstructured":"Huang X, Belongie S. Arbitrary style transfer in real-time with adaptive instance normalization. 2017. Available from: http:\/\/arxiv.org\/abs\/1703.06868.","DOI":"10.1109\/ICCV.2017.167"},{"key":"10.3233\/ICA-210662_ref81","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.jprocont.2017.06.012","article-title":"Statistical process monitoring as a big data analytics tool for smart manufacturing","volume":"67","author":"He","year":"2018","journal-title":"Journal of Process Control"},{"key":"10.3233\/ICA-210662_ref82","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1016\/j.compind.2018.10.008","article-title":"An intelligent metrology informatics system based on neural networks for multistage manufacturing processes","volume":"82","author":"Papananias","year":"2019","journal-title":"Procedia CIRP"},{"key":"10.3233\/ICA-210662_ref83","first-page":"2261","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017","journal-title":"Proceedings\u00a0\u2013 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017"},{"key":"10.3233\/ICA-210662_ref84","first-page":"1","article-title":"Deep residual learning for image recognition","author":"He","year":"2015","journal-title":"Multimedia Tools and Applications"}],"container-title":["Integrated Computer-Aided Engineering"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/ICA-210662","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:14:30Z","timestamp":1777454070000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/ICA-210662"}},"subtitle":[],"editor":[{"given":"Lazaros","family":"Iliadis","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,8,27]]},"references-count":84,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.3233\/ica-210662","relation":{},"ISSN":["1069-2509","1875-8835"],"issn-type":[{"value":"1069-2509","type":"print"},{"value":"1875-8835","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,27]]}}}