Developing an artificial intelligence quality control framework. A case study of rail industry in South Africa.
Mushi, MK
Mushi, MK
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Abstract
Quality control has become a critical parameter for competitiveness in manufacturing organisations. Although technology and automation have made a difference, many quality issues are still not managed by manual inspection. Due to poor quality control, approximately 20% of the commodity parts from the suppliers to Company X are received with defects. The objective was to develop an Artificial Intelligence (AI) quality control framework using Orange Anaconda 3.4 software. A literature review showed that factors such as fatigue, skills development and quality awareness could contribute to quality issues. Data collected from Company X consisted of historical defects of commodity parts used to train the machine learning (ML) algorithms for more accurate classification with supervised learning. The quantitative results obtained from the ML algorithm simulation with trained data were used to compare Adaboost, CN2 rule inducer, Random Forest and Neural Network Algorithms, revealing the inspection efficiency and accuracy of the classification. Furthermore, the findings indicate that the existing model from Company X could integrate easily with AI to produce a better quality control system, production improvement, optimal efficiency, reduction of defects and greater financial growth within the business. In conclusion, the AI-based quality control framework is recommended to Company X’s suppliers and other OEMs.
Description
Submitted in partial fulfilment of the requirement for the degree, Master’s Degree: Engineering Management in the Department of Industrial Engineering in the Faculty of Engineering and the Built Environment at the Tshwane University of Technology.
Date
2022-05-01
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Tshwane University of Technology
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Keywords
Quality control, Quality control framework, Artificial Intelligence (AI)