Contact Dr Jian Qin
- Tel: +44 (0) 1234 750111
- Email: J.Qin@cranfield.ac.uk
- ORCID
- Google Scholar
Areas of expertise
- Industrial Automation
- Manufacturing Systems
- Sensor Technologies
Background
Dr Jian Qin is currently a lecturer in digitalisation for metal Additive Manufacturing at the Welding and Additive Manufacturing Centre at Cranfield University. He is also leading research in the area of monitoring and automation for wire based Direct Energy Deposition Additive Manufacturing (w-DEDAM) processes. Dr Qin's professional journey in academia has been characterized by a strong passion for high-value manufacturing, with a special interest in wire based Direct Energy Deposition Additive Manufacturing process (w-DEDAM). Dr. Qin embarked on his academic journey at Cardiff University in 2015 where he obtained his PhD degree in 2019. His research primarily revolves around monitoring, control, and data analysis for w-DEDAM systems and processes. The intent behind his work is to improve the efficiency, automation and intelligence of DEDAM systems, taking into account the importance of their potential applications in a wide range of industries.
Research opportunities
Process monitoring and control for the metal Additive Manufacturing process and system.
The digital twin of the Additive Manufacturing process and system.
Additive Manufacturing digitalisation and automation.
Clients
WAAM3D
Weir Group
GE Avio
Innovate UK
Publications
Articles In Journals
- Wang B, Ren G, Li H, Zhang J & Qin J. (2024). Developing a Framework Leveraging Building Information Modelling to Validate Fire Emergency Evacuation. Buildings, 14(1)
- Qin J, Vives J, Raja P, Lasisi S, Wang C, .... (2023). Automated Interlayer Wall Height Compensation for Wire Based Directed Energy Deposition Additive Manufacturing. Sensors, 23(20)
- Wang C, Wang J, Bento J, Ding J, Pardal G, .... (2023). A novel cold wire gas metal arc (CW-GMA) process for high productivity additive manufacturing. Additive Manufacturing, 73(July)
- Wang Y, Li H, Li Z, Zhang Y, Qin J, .... (2023). Refining microstructure of medium-thick AA2219 aluminium alloy welded joint by ultrasonic frequency double-pulsed arc. Journal of Materials Research and Technology, 23(March-April)
- Yin Y, Tian Y, Ding J, Mitchell T & Qin J. (2023). Prediction of Electron Beam Welding Penetration Depth Using Machine Learning-Enhanced Computational Fluid Dynamics Modelling. Sensors, 23(21)
- Liu X, Qin J, Zhao K, Featherston CA, Kennedy D, .... (2023). Design optimization of laminated composite structures using artificial neural network and genetic algorithm. Composite Structures, 305(February)
- Hu F, Liu Y, Li Y, Ma S, Qin J, .... (2023). Task-driven data fusion for additive manufacturing: Framework, approaches, and case studies. Journal of Industrial Information Integration, 34
- Evans SI, Wang J, Qin J, He Y, Shepherd P, .... (2022). A review of WAAM for steel construction – Manufacturing, material and geometric properties, design, and future directions. Structures, 44(October)
- Chen C, Wang T, Liu Y, Cheng L & Qin J. (2022). Spatial attention-based convolutional transformer for bearing remaining useful life prediction. Measurement Science and Technology, 33(11)
- Qin J, Wang Y, Ding J & Williams S. (2022). Optimal droplet transfer mode maintenance for wire + arc additive manufacturing (WAAM) based on deep learning. Journal of Intelligent Manufacturing, 33(7)
- Qin J, Hu F, Liu Y, Witherell P, Wang CCL, .... (2022). Research and application of machine learning for additive manufacturing. Additive Manufacturing, 52(April)
- Lu K, Chen C, Wang T, Cheng L & Qin J. (2022). Fault diagnosis of industrial robot based on dual-module attention convolutional neural network. Autonomous Intelligent Systems, 2(1)
- Qin J, Li Z, Wang R, Li L, Yu Z, .... (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(1)
- Qin J, Liu Y, Grosvenor R, Lacan F & Jiang Z. (2020). Deep learning-driven particle swarm optimisation for additive manufacturing energy optimisation. Journal of Cleaner Production, 245
- Chen C, Liu Y, Kumar M, Qin J & Ren Y. (2019). Energy consumption modelling using deep learning embedded semi-supervised learning. Computers & Industrial Engineering, 135
- Qin J, Liu Y & Grosvenor R. (2018). Multi-source data analytics for AM energy consumption prediction. Advanced Engineering Informatics, 38
- Chen C, Liu Y, Kumar M & Qin J. (2018). Energy Consumption Modelling Using Deep Learning Technique — A Case Study of EAF. Procedia CIRP, 72
- Qin J, Liu Y & Grosvenor R. (2017). A Framework of Energy Consumption Modelling for Additive Manufacturing Using Internet of Things. Procedia CIRP, 63
- Qin J, Liu Y & Grosvenor R. (2016). A Categorical Framework of Manufacturing for Industry 4.0 and Beyond. Procedia CIRP, 52
Conference Papers
- Liu Y, Chen C, Wang T, Cheng L & Qin J. (2023). Model-Agnostic Meta-Learning for Fault Diagnosis of Industrial Robots
- Song J, Chen C, Wang T, Deng C, Cheng L, .... (2023). Fault Diagnosis for Industrial Robots Based on Informer
- Liang T, Chen C, Wang T, Zhang A & Qin J. (2022). A Machine Learning-Based Approach for Elevator Door System Fault Diagnosis
- Hu F, Qin J, Li Y, Liu Y & Sun X. (2021). Deep Fusion for Energy Consumption Prediction in Additive Manufacturing
- Hu F, Liu Y, Qin J, Sun X & Witherell P. (2020). Feature-level Data Fusion for Energy Consumption Analytics in Additive Manufacturing
- Qin J, Liu Y & Grosvenor R. (2017). Data analytics for energy consumption of digital manufacturing systems using Internet of Things method