Product Quality Output Measurement for Preventive Maintenance on Computer Numerical Control (CNC) Machines at an Electronic Manufacturing Industry

Aizat Haikal Apandi; Adam Sharrif; Kamarulzaman Mahmad Khairai

doi:10.62048/qjms.v2i1.56

Penulis

Aizat Haikal Apandi Centre Science and Mathematics, University Malaysia Pahang As-Sultan Abdullah
Adam Sharrif Centre Science and Mathematics, University Malaysia Pahang As-Sultan Abdullah
Kamarulzaman Mahmad Khairai Kuliyyah of Engineering, International Islamic University Malaysia

DOI:

https://doi.org/10.62048/qjms.v2i1.56

Kata Kunci:

Computer Numerical Control, overall equipment effectiveness, preventive maintenance, predictive maintenance, quality improvement, machine learning

Abstrak

Mesin Computer Numerical Control (CNC) menghilangkan material dari sebuah benda kerja dengan menggunakan kontrol digital untuk menghasilkan komponen yang dirancang secara khusus. Penting untuk menjaga akurasi dan presisi mesin ini di bawah kondisi yang menantang setelah penggunaan jangka panjang. Penelitian ini bertujuan untuk mengevaluasi kualitas produk CNC menggunakan Overall Equipment Effectiveness (OEE) dan meningkatkan kinerja jangka panjang melalui pendekatan berbasis data. Metode penelitian ini berfokus pada analisis laju scrap, penggunaan u-chart untuk memantau stabilitas, serta penerapan model regresi machine learning—K-Nearest Neighbour (KNN) dan Random Forest (RF)—untuk memprediksi laju scrap. Prediksi tersebut membantu mengidentifikasi waktu yang tepat untuk melakukan pemeliharaan pencegahan, sehingga presisi mesin dapat tetap terjaga seiring waktu. Penelitian ini juga memanfaatkan visualisasi hasil menggunakan Microsoft Power BI untuk meningkatkan interpretasi data dan memfasilitasi respons cepat terhadap potensi masalah. Hasil penelitian menunjukkan bahwa RF memiliki kinerja lebih baik dibandingkan KNN dalam memprediksi laju scrap. Penggunaan stacking pada model-model tersebut lebih lanjut meningkatkan akurasi, sehingga memberikan alat pengambilan keputusan yang lebih andal dalam mengantisipasi masalah kualitas. Dengan mendeteksi anomali secara dini, produsen dapat melakukan pemeliharaan tepat waktu, meminimalkan waktu henti, serta memperpanjang umur operasional mesin CNC. Kesimpulannya, integrasi analisis laju scrap, pengendalian proses statistik, dan teknik pembelajaran mesin yang canggih dapat secara efektif menjaga kualitas produk serta mengurangi ketidakakuratan. Perusahaan sebaiknya mengadopsi perencanaan pemeliharaan yang lebih proaktif dengan memanfaatkan peramalan yang lebih baik.

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