Publications
2023
- Liu, X., Qin, J., Zhao, K., Featherston, C. A., Kennedy, D., Jing, Y. and Yang, G. 2023. Design optimization of laminated composite structures using deep artificial neural network and genetic algorithm. Composite Structures 305, article number: 116500. (10.1016/j.compstruct.2022.116500)
2022
2021
- Qin, J., Li, Z., Wang, R., Li, L., Yu, Z., He, X. and Liu, Y. 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)
2019
- Qin, J., Liu, Y., Grosvenor, R., Lacan, F. and Jiang, Z. 2019. Deep learning-driven particle swarm optimisation for additive manufacturing energy optimisation. Journal of Cleaner Production, pp. 118702. (10.1016/j.jclepro.2019.118702)
- Chen, C., Liu, Y., Kumar, M., Qin, J. and Ren, Y. 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)
- Qin, J. 2019. Advanced data analytics for additive manufacturing energy consumption modelling, prediction, and management under Industry 4.0. PhD Thesis, Cardiff University.
2018
- Qin, J., Liu, Y. and Grosvenor, R. 2018. Multi-source data analytics for AM energy consumption prediction. Advanced Engineering Informatics 38, pp. 840-850. (10.1016/j.aei.2018.10.008)
- Chen, C., Liu, Y., Kumar, M. and Qin, J. 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)
2017
- Qin, J., Liu, Y. and Grosvenor, R. 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)
- Qin, J., Liu, Y. and Grosvenor, R. 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.
2016
Articles
- Liu, X., Qin, J., Zhao, K., Featherston, C. A., Kennedy, D., Jing, Y. and Yang, G. 2023. Design optimization of laminated composite structures using deep artificial neural network and genetic algorithm. Composite Structures 305, article number: 116500. (10.1016/j.compstruct.2022.116500)
- Qin, J. et al. 2022. Research and application of machine learning for additive manufacturing. Additive Manufacturing 52, article number: 102691. (10.1016/j.addma.2022.102691)
- Qin, J., Li, Z., Wang, R., Li, L., Yu, Z., He, X. and Liu, Y. 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)
- Qin, J., Liu, Y., Grosvenor, R., Lacan, F. and Jiang, Z. 2019. Deep learning-driven particle swarm optimisation for additive manufacturing energy optimisation. Journal of Cleaner Production, pp. 118702. (10.1016/j.jclepro.2019.118702)
- Chen, C., Liu, Y., Kumar, M., Qin, J. and Ren, Y. 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)
- Qin, J., Liu, Y. and Grosvenor, R. 2018. Multi-source data analytics for AM energy consumption prediction. Advanced Engineering Informatics 38, pp. 840-850. (10.1016/j.aei.2018.10.008)
- Chen, C., Liu, Y., Kumar, M. and Qin, J. 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)
- Qin, J., Liu, Y. and Grosvenor, R. 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)
- Qin, J., Liu, Y. and Grosvenor, R. 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)
Thesis
Conferences