Piolo, Christian Alison Maulion and Pratama, Aditya T. and Schwung, Andreas (2020) Knowledge Extraction and Validation Methods for Process Optimization and Fault Detection in an Augmented Reality Application. Bachelor thesis, Swiss German University.

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Abstract

With the growing complexity of processes in factories, expert knowledge is considered a very valuable asset to have. However, expert knowledge is difficult to obtain and teach. This process requires time, practice and experience which the experts have obtained throughout their years of work. Fortunately, with the rapid technological advancements, there are many options that can help assist with this problem. One of the prominent technologies companies have tried implementing into their processes are augmented reality. Since this is still a new tool, there are many questions about its plausibility and effectiveness when used as a tool for learning and process optimization. In this thesis, it will aim to use augmented reality to assist in knowledge internalization, process optimization and fault detection and then formulating a solution developed in an augmented reality application. This paper discusses the proper ways to extract knowledge through the use of questionnaires, eFMEA and pFMEA and then implementing them into the HoloLens and testing the effectiveness of the implementation through time studies and tests.

Item Type:	Thesis (Bachelor)
Uncontrolled Keywords:	Knowledge Management; Augmented Reality; Expert Knowledge; FMEA
Subjects:	T Technology > T Technology (General) > T55.4 Industrial engineering. Management engineering
Divisions:	Faculty of Engineering and Information Technology > Department of Industrial Engineering
Depositing User:	Faisal Ifzaldi
Date Deposited:	02 Nov 2020 14:04
Last Modified:	02 Nov 2020 14:04
URI:	http://repository.sgu.ac.id/id/eprint/1847

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