Statistical process monitoring for e-waste based on beta regression and particle swarm optimization
International Journal of Quality & Reliability Management
ISSN: 0265-671X
Article publication date: 11 January 2022
Issue publication date: 19 July 2022
Abstract
Purpose
E-waste management can reduce relevant impact of the business activity without affecting reliability, quality or performance. Statistical process monitoring is an effective way for managing reliability and quality to devices in manufacturing processes. This paper proposes an approach for monitoring the proportion of e-waste devices based on Beta regression model and particle swarm optimization. A statistical process monitoring scheme integrating residual useful life techniques for efficient monitoring of e-waste components or equipment was developed.
Design/methodology/approach
An approach integrating regression method and particle swarm optimization algorithm was developed for increasing the accuracy of regression model estimates. The control chart tools were used for monitoring the proportion of e-waste devices from fault detection of electronic devices in manufacturing process.
Findings
The results showed that the proposed statistical process monitoring was an excellent reliability and quality scheme for monitoring the proportion of e-waste devices in toner manufacturing process. The optimized regression model estimates showed a significant influence of the process variables for both individually injection rate and toner treads and the interactions between injection rate, toner treads, viscosity and density.
Originality/value
This research is different from others by providing an approach for modeling and monitoring the proportion of e-waste devices. Statistical process monitoring can be used to monitor waste product in manufacturing. Besides, the key contribution in this study is to develop different models for fault detection and identify any change point in the manufacturing process. The optimized model used can be replicated to other Electronic Industry and allows support of a satisfactory e-waste management.
Keywords
Acknowledgements
The author thanks the Brazilian Electronic Company for valuable opportunities and the National Council of Scientific and Technological Development (CNPq) for the financial support of this work. The author gratefully acknowledges the anonymous reviewers for their valuable comments, which contributed to improving the paper quality.
Funding: This work was supported by the National Council of Scientific and Technological Development (CNPq) [grant number 309812/2021-6].
Disclosure statement: The author declares that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Citation
Oliveira Sant’Anna, A.M. (2022), "Statistical process monitoring for e-waste based on beta regression and particle swarm optimization", International Journal of Quality & Reliability Management, Vol. 39 No. 7, pp. 1663-1675. https://doi.org/10.1108/IJQRM-09-2021-0344
Publisher
:Emerald Publishing Limited
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