Browsing by Author "Alrufaihi, Duaa"

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    Feature reduction and selection for use in machine learning for manufacturing
    (IOS Press, 2022-09-08) Alrufaihi, Duaa; Oleghe, Omogbai; Almanei, Mohammed; Jagtap, Sandeep; Salonitis, Konstantinos
    In a complex manufacturing system such as the multistage manufacturing system, maintaining the quality of the products becomes a challenging task. It is due to the interconnectivity and dependency of factors that can affect the final product. With the increasing availability of data, Machine Learning (ML) approaches are applied to assess and predict quality-related issues. In this paper, several ML algorithms, including feature reduction/selection methods, were applied to a publicly available multistage manufacturing dataset to predict the characteristic of the output measurements in (mm). A total of 24 prediction models were produced. The accuracy of the prediction models and the execution time were the evaluation metrics. The results show that uncontrolled variables are the most common features that have been selected by the selection/reduction methods suggesting their strong relationship to the quality of the product. The performance of the prediction models was heavily dependent on the ML algorithm.
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    Methodology to identify and quantify sources of process scrap on shop floor
    (SSRN, 2020-10-26) Alrufaihi, Duaa; Fernandes, Audrey; Jaysukh, Bhavya Gangar; Goldery, Pauline; Kaur, Rashmeet; Alvarez, Samuler Tirado; Navarro, Daniel Vazquez; Salonitis, Konstantinos
    Poor quality costs are the total financial losses caused by the products or services not being perfect. Process scrap is a major contributing factor to these losses. Identification of different sources of scrap and the resulting costs is paramount for Continuous Improvement. Furthermore, quantification of this scrap and how the data can be visualised will facilitate decision making by upper management. For this, a methodology that acts as a guide will prove to be of great use in the analysis of scrap generation in any manufacturing plant. In the case study presented, a comprehensive list of the possible sources was made. However, only those which were responsible for a major part of the cost and of great concern were shortlisted. Once identified, several measurement systems were proposed to accurately quantify the resultant scrap. This was then followed by data visualisation using a dashboard that gave a weekly update on the levels of scrap generated