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Title: A framework for digitisation of manual manufacturing task knowledge using gaming interface technology
Author: Prabhu, Vinayak Ashok
ISNI:       0000 0004 5916 3947
Awarding Body: Cranfield University
Current Institution: Cranfield University
Date of Award: 2015
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Intense market competition and the global skill supply crunch are hurting the manufacturing industry, which is heavily dependent on skilled labour. Companies must look for innovative ways to acquire manufacturing skills from their experts and transfer them to novices and eventually to machines to remain competitive. There is a lack of systematic processes in the manufacturing industry and research for cost-effective capture and transfer of human skills. Therefore, the aim of this research is to develop a framework for digitisation of manual manufacturing task knowledge, a major constituent of which is human skill. The proposed digitisation framework is based on the theory of human-workpiece interactions that is developed in this research. The unique aspect of the framework is the use of consumer-grade gaming interface technology to capture and record manual manufacturing tasks in digital form to enable the extraction, decoding and transfer of manufacturing knowledge constituents that are associated with the task. The framework is implemented, tested and refined using 5 case studies, including 1 toy assembly task, 2 real-life-like assembly tasks, 1 simulated assembly task and 1 real-life composite layup task. It is successfully validated based on the outcomes of the case studies and a benchmarking exercise that was conducted to evaluate its performance. This research contributes to knowledge in five main areas, namely, (1) the theory of human-workpiece interactions to decipher human behaviour in manual manufacturing tasks, (2) a cohesive and holistic framework to digitise manual manufacturing task knowledge, especially tacit knowledge such as human action and reaction skills, (3) the use of low-cost gaming interface technology to capture human actions and the effect of those actions on workpieces during a manufacturing task, (4) a new way to use hidden Markov modelling to produce digital skill models to represent human ability to perform complex tasks and (5) extraction and decoding of manufacturing knowledge constituents from the digital skill models.
Supervisor: Tiwari, Ashutosh Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available