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Remanufacturing of end-of-life laptop based on remaining useful life prediction and quality grading with random forest and cluster analysis

Author

Listed:
  • Gurunathan Anandh
  • Shanmugam Prasanna Venkatesan
  • Sandanam Domnic
  • Santosh Awaje
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.

Suggested Citation

  • Gurunathan Anandh & Shanmugam Prasanna Venkatesan & Sandanam Domnic & Santosh Awaje, 2024. "Remanufacturing of end-of-life laptop based on remaining useful life prediction and quality grading with random forest and cluster analysis," International Journal of Process Management and Benchmarking, Inderscience Enterprises Ltd, vol. 17(2), pages 137-152.
    • Handle: RePEc:ids:ijpmbe:v:17:y:2024:i:2:p:137-152
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