J
Jian Qin
Researcher at Cardiff University
Publications - 19
Citations - 1026
Jian Qin is an academic researcher from Cardiff University. The author has contributed to research in topics: Energy consumption & Engineering. The author has an hindex of 7, co-authored 12 publications receiving 660 citations. Previous affiliations of Jian Qin include Cranfield University.
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A categorical framework of manufacturing for industry 4.0 and beyond
TL;DR: In this paper, an implementation structure of Industry 4.0, consisting of a multi-layered framework is described, and is shown how it can assist people in understanding and achieving the requirements of Industry 5.0.
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Research and Application of Machine Learning for Additive Manufacturing
Jian Qin,Fu Hu,Yang Liu,Paul Witherell,Charlie C. L. Wang,David W. Rosen,T.E. Simpson,Yuan Lu,Qian Tang +8 more
TL;DR: In this article , the authors employ a systematic literature review method to identify, assess, and analyse published literature on additive manufacturing, including design for additive manufacturing (DfAM), material analytics, in situ monitoring and defect detection, property prediction and sustainability.
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Energy consumption modelling using deep learning embedded semi-supervised learning
TL;DR: Results derived from the proposed deep learning embedded semi-supervised learning approach reveal that deep learning (DLeSSL based) outperforms theDeep learning (supervised) and deepLearning (label propagation based) when the labelled data is limited.
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A framework of energy consumption modelling for additive manufacturing using Internet of Things
TL;DR: An Internet of Things (IoT) framework is designed for understanding and reducing the energy consumption of AM processes, and a novel energy consumption analysis proposal is proposed for this system specifically.
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Energy consumption modelling using deep learning technique — a case study of EAF
TL;DR: The result shows the predicting performance of the deep learning model is better than the conventional machine learning models, e.g., linear regression, support vector machine and decision tree.