Abstract
The Advanced Manufacturing Research Centre (AMRC) group is part of the UK’s High Value Manufacturing (HVM) Catapult, whose mission is to accelerate the concepts-to-commercial-reality process and create a sustainable future for high-value manufacturing.
Many manufacturers rely on the manufacture of a small number of high-value workpieces. In contrast to high-volume production, any workpieces rejected in high-value manufacturing represent a large individual investment of resources. Nonetheless, the reasons for rejection are often difficult to determine, which hinders the improvement of the manufacturing process.
The reason for this difficulty is the lack of data available on the manufactured workpiece - due to it undergoing several processes, the scarcity of data collected, and the limited sample size in low-volume, high-value manufacturing. Early prediction of workpiece failure would increase productivity and reduce waste by an early stop of the manufacturing process.
The workpieces of interest for this Data Study Group are high-value, low-yield ones since they are either made from expensive materials or undergo expensive treatments. Thus, any improvements that lead to fewer rejected pieces will be of high business value and save resources.
AMRC has recently collected data on 16 such products, from their forging through machining to their final quality check. This challenge aims to explore the potential of this novel dataset for high-value, low-yield research with a particular emphasis on failure prediction, data augmentation, and data viability.
Citation information
Data Study Group Team. (2023, September 08). Data Study Group Final Report: AMRC - Data augmentation and synthetic data generation for low frequency and sparse data problems. Zenodo: https://zenodo.org/record/8328245
Additional information
- Kieran Baker, King’s College London
- Tereso del Rio, Coventry University
- Guowei Huang, Alliance Manchester Business School, University of Manchester
- Kavi Jayathunge, Bournemouth University
- Linglong Qian, King’s College London
- Mercededh Rezaei, Queen Mary University
- Mohamad Reza Shahabian Alashti, University of Hertfordshire
- Stefan Schoepf, University of Cambridge
- Daria Semochkina, University of Southampton
- Victoria Volodina, University College London
- Bing Wang, Henley Business School, University of Reading
- Wenjie Zheng, The Alan Turing Institute