Contact Dr Jian Qin
- Tel: +44 (0) 1234 750111
- Email: J.Qin@cranfield.ac.uk
- ORCID
- Google Scholar
Background
Dr Jian Qin is a Lecturer in Digitalisation for Metal Additive Manufacturing at the Welding and Additive Manufacturing Centre, Cranfield University. He leads research on monitoring and automation for wire-based Directed Energy Deposition Additive Manufacturing (w-DEDAM), with a focus on making metal AM processes more efficient, automated, and intelligent.
He received his PhD from Cardiff University in 2019, where his research centred on data-driven approaches to AM systems. Since joining Cranfield, his work has spanned process monitoring, control, and data analysis for w-DEDAM, combining sensor technologies, advanced data analytics, image processing, and automation to improve the reliability and productivity of metal additive manufacturing and to provide digital solutions for its qualification.
His research supports applications across sectors such as aerospace, energy, and defence, and he works closely with industrial partners to translate digital manufacturing technologies into practice.
Research opportunities
- Digitalisation and automation of Directed Energy Deposition Additive Manufacturing (DED-AM)
- In-process monitoring and control
- Image processing and sensor data analytics
- Process stability and defect prevention
- Non-destructive testing (NDT) for DED-AM
- Qualification and certification of metal additive manufacturing
Current activities
- PYRAM funded by EPSRC
- I-Break funded by BEIS
- PhD opportunities:
- Optimising and automating pre-production for wire based Directed Energy Deposition (w-DEDAM) production PhD or MSc by Research
- Quality Assured Large Scale Metal Additive Manufacturing for Energy, Marine, Aerospace & Nuclear Sectors – University of Strathclyde
- 3D temperature field reconstruction from local temperature monitoring in directed energy deposition PhD
- MSc by Research in Manufacturing and Materials (Topic to discuss)
Clients
WAAM3D
Weir Group
GE Avio
Innovate UK
Publications
Articles In Journals
- Chen L, Lasisi S, Vives J, Bird T, Narasiah H, .... (2025). Monitoring framework for physical knowledge exploration in wire-based directed energy additive manufacturing (w-DEDAM). Procedia CIRP, 134
- Feng S, Wainwright J, Wang C, Wang J, Pardal G, .... (2025). Video segmentation of Wire + Arc Additive Manufacturing (WAAM) using visual large model. Sensors, 25(14)
- Wang J, Taylor M, Diao C, Pickering EJ, Qin J, .... (2025). Insights into crack prevention and property improvement for additively manufactured ultra-high-strength steel structures with complex geometries. Additive Manufacturing Letters, 14
- Kim KW, Kamerkar A, Chiu T-E, Abdi I, Qin J, .... (2025). WAAM-ViD: towards universal vision-based monitoring for wire arc additive manufacturing. Frontiers in Manufacturing Technology, 5
- 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, Taraphdar P, Sun Y, Wainwright J, Lai WJ, .... (2024). Knowledge-based bidirectional thermal variable modelling for directed energy deposition additive manufacturing. Virtual and Physical Prototyping, 19(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, Rodrigues 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)
- 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
- Liu X, Qin J, Zhao K, Featherson CA, Kennedy D, .... (2022). Design optimization of laminated composite structures using artificial neural network and genetic algorithm. Composite Structures, 305(February)
- 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
- Hallam JM, Charrett T, Kissinger T, Qin J, Suder W, .... (2025). Range-resolved interferometric layer-height measurement for laser-additive manufacturing
- 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