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Number of items: 11.

Liu, Xiaoyang, Qin, Jian, Zhao, Kai, Featherston, Carol A. ORCID: https://orcid.org/0000-0001-7548-2882, Kennedy, David ORCID: https://orcid.org/0000-0002-8837-7296, Jing, Yucai and Yang, Guotao 2023. Design optimization of laminated composite structures using deep artificial neural network and genetic algorithm. Composite Structures 305 , 116500. 10.1016/j.compstruct.2022.116500
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Qin, Jian, Hu, Fu, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Witherell, Paul, Wang, Charlie C.L., Rosen, David W., Simpson, Timothy, Lu, Yan and Tang, Qian 2022. Research and application of machine learning for additive manufacturing. Additive Manufacturing 52 , 102691. 10.1016/j.addma.2022.102691
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Qin, Jian, Li, Zhuoqun, Wang, Rui, Li, Li, Yu, Zhe, He, Xun and Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940 2021. Industrial Internet of Learning (IIoL): IIoT based Pervasive Knowledge Network for LPWAN – concept, framework and case studies. CCF Transactions on Pervasive Computing and Interaction 3 , pp. 25-39. 10.1007/s42486-020-00050-2
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Qin, Jian, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Grosvenor, Roger ORCID: https://orcid.org/0000-0001-8942-4640, Lacan, Franck ORCID: https://orcid.org/0000-0002-3499-5240 and Jiang, Zhigang 2019. Deep learning-driven particle swarm optimisation for additive manufacturing energy optimisation. Journal of Cleaner Production , p. 118702. 10.1016/j.jclepro.2019.118702
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Chen, Chong, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Kumar, Maneesh ORCID: https://orcid.org/0000-0002-2469-1382, Qin, Jian and Ren, Yunxia 2019. Energy consumption modelling using deep learning embedded semi-supervised learning. Computers and Industrial Engineering 135 , pp. 757-765. 10.1016/j.cie.2019.06.052
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Qin, Jian 2019. Advanced data analytics for additive manufacturing energy consumption modelling, prediction, and management under Industry 4.0. PhD Thesis, Cardiff University.
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Qin, Jian, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940 and Grosvenor, Roger ORCID: https://orcid.org/0000-0001-8942-4640 2018. Multi-source data analytics for AM energy consumption prediction. Advanced Engineering Informatics 38 , pp. 840-850. 10.1016/j.aei.2018.10.008
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Chen, Chong, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Kumar, Maneesh ORCID: https://orcid.org/0000-0002-2469-1382 and Qin, Jian 2018. Energy consumption modelling using deep learning technique — a case study of EAF. Procedia CIRP 72 , pp. 1063-1068. 10.1016/j.procir.2018.03.095
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Qin, Jian, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940 and Grosvenor, Roger ORCID: https://orcid.org/0000-0001-8942-4640 2017. A framework of energy consumption modelling for additive manufacturing using Internet of Things. Procedia CIRP Conference on Manufacturing System 63 , pp. 307-312. 10.1016/j.procir.2017.02.036
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Qin, Jian, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940 and Grosvenor, Roger ORCID: https://orcid.org/0000-0001-8942-4640 2017. Data analytics for energy consumption of digital manufacturing systems using Internet of Things method. Presented at: IEEE International Conference on Automation Science and Engineering, Xi'an, China, 20-23 August 2017. IEEE International Conference on Automation Science and Engineering.
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Qin, Jian, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940 and Grosvenor, Roger ORCID: https://orcid.org/0000-0001-8942-4640 2016. A categorical framework of manufacturing for industry 4.0 and beyond. Procedia CIRP 52 , pp. 173-178. 10.1016/j.procir.2016.08.005
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