Remanufacturing of end-of-life laptop based on remaining useful life prediction and quality grading with random forest and cluster analysis
by Gurunathan Anandh; Shanmugam Prasanna Venkatesan; Sandanam Domnic; Santosh Awaje
International Journal of Process Management and Benchmarking (IJPMB), Vol. 17, No. 2, 2024

Abstract: A laptop remanufacturer typically performs recovery, disassembly, functional testing, grading and repair/replacement of parts. The remaining useful life (RUL) of the EOL laptop parts is evaluated and quality graded with the usage statistics to decide the repair/replacement options. Research on RUL prediction and quality grading of EOL laptop parts deserves research attention. This research aims to develop a decision support tool (DST) in Microsoft Excel interfaced with Python for RUL prediction and quality grading of laptop hard disk drive (HDD) and lithium-ion battery (LiB). Random forest (RF) is used for RUL prediction, and the K-means clustering algorithm is applied for quality grading using sample datasets obtained from the online dataset repositories. Typically, a laptop remanufacturer is unfamiliar with machine learning (ML) algorithms; thus, developing a simple user interface is vital. The RF and clustering analysis results suggest that the predicted and experimental values are highly correlated.

Online publication date: Wed, 01-May-2024

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