Structured Defect Data-Based K-Means Clustering Analysis and Framework for Quality Control (QC) Prioritization in Manufacturing
DOI:
https://doi.org/10.22441/pasti.2024.v18i3.011Kata Kunci:
Defects, Quality Control, K-Means Clustering, Manufacturing, PrioritizationAbstrak
Quality Control (QC) sangat penting dalam manufaktur untuk memastikan kualitas produk dan meminimalisir cacat produk. Namun, meningkatnya kompleksitas produk dan proses manufaktur telah membuat identifikasi dan prioritas cacat untuk QC menjadi lebih menantang, sementara sebagian besar studi hanya berfokus pada inspeksi visual. Oleh karena itu, studi ini mengusulkan analisis berbasis data cacat terstruktur dan kerangka kerja untuk menemukan prioritas dalam proses QC. Kerangka kerja ini menggunakan pengelompokan K-Means untuk mengelompokkan cacat berdasarkan karakteristiknya, seperti jenis, lokasi, dan tingkat keparahan yang memengaruhi tingkat biaya perbaikan. Untuk memvalidasi model, Davis-Bouldin Index dan Silhouette Score digunakan untuk mengukur kualitas model. Eksplorasi data menunjukkan bahwa setiap fitur memiliki hubungan serta dampak terhadap biaya perbaikan di mana tingkat keparahan yang lebih besar sejalan dengan biaya perbaikan yang lebih tinggi. Penemuan menunjukkan bahwa cluster 0 adalah yang harus diprioritaskan karena memiliki biaya perbaikan tertinggi di antara yang lain. Hasil penelitian menunjukkan bahwa kerangka kerja tersebut dapat secara efektif mengidentifikasi dan memprioritaskan cacat untuk QC, yang berpotensi mengarah pada peningkatan kualitas produk dan pengurangan biaya manufaktur.
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