Scopus
YÖKSİS DOI Eşleşti
SJR Q1
Machinability of different Cu-Gr composites in milling: Performance parameters prediction via machine learning models
Expert Systems with Applications · Mayıs 2025
YÖKSİS Kayıtları
Machinability of different Cu-Gr composites in milling: Performance parameters prediction via machine learning models
Expert Systems with Applications · 2025 SCI-Expanded
Dr. Öğr. Üyesi ÜSAME ALİ USCA →
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Makale Bilgileri
ISSN09574174
Yayın TarihiMayıs 2025
Cilt / Sayfa272
Scopus ID2-s2.0-85217245336
Özet
The machinability of copper-graphite (Cu-Gr) composites has gained significant attention due to their unique thermal, electrical, and mechanical properties. This study experimentally investigates the machinability performances (such as surface roughness, flank wear, cutting temperature, and energy consumption) of Cu-Gr hybrid composite materials during milling. It predicts these parameters with machine learning models. The study aims to contribute to sustainable and optimized manufacturing processes by analyzing the effects of different cutting parameters and cooling/lubrication conditions on this performance. Furthermore, advanced artificial intelligence-based models predict machining outcomes, providing a robust framework for process enhancement and industrial implementation. Although there are comprehensive studies on the machining performances of metal matrix composites in the literature, there is limited information on Cu-Gr composites’ mechanical and thermal behaviors in milling processes. To address this deficiency, a full factorial experimental plan was applied on six different Cu-Gr composites and the effects of different cutting speeds, feed rates and cooling/lubrication environments (Dry, MQL, cryogenic LN2) on flank wear, surface roughness, cutting temperature and energy consumption were analyzed. The materials used in the study were prepared by mixing graphite and hard phases (Al2O3 and Cr3C2) in specific proportions, and these composites were compared in terms of machinability. Afterward, the output parameters of the experimental results are predicted by employing the well-known machine learning models and the experimental results. The results manifested that Gradient-Boosted Decision Tree Regression performs better than the other ten machine learning models in predicting machinability parameters. Finally, this study highlights potential areas for future research and provides a practical guide for optimizing Cu-Gr composites in manufacturing processes and achieving sustainability goals. It has engineering value in efficiency, cost reduction, and developing environmentally friendly applications, especially for the automotive, aerospace, and energy sectors.
Yazarlar (6)
1
Serhat Şap
ORCID: 0000-0001-5177-4952
2
Erdi Acar
ORCID: 0000-0002-1451-7874
3
Ünal Değirmenci
4
Üsame Ali Usca
5
Samet Memiş
ORCID: 0000-0002-0958-5872
6
Ramazan Şener
ORCID: 0000-0001-6108-8673
Anahtar Kelimeler
Cu-Gr composites
Machinability Metrics
Machine Learning
Prediction
Regression
Kurumlar
Bandırma Onyedi Eylül University
Bandirma Turkey
Bingöl Üniversitesi
Bingol Turkey
Çanakkale Onsekiz Mart Üniversitesi
Canakkale Turkey
Inönü Üniversitesi
Malatya Turkey
Q&CO Quantum Computing Inc.
Istanbul Türkiye
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Expert Systems with Applications
Q1
SJR Skoru1,854
H-Index290
YayıncıElsevier Ltd
ÜlkeUnited Kingdom
Artificial Intelligence (Q1)
Computer Science Applications (Q1)
Engineering (miscellaneous) (Q1)
Metrikler
8
Atıf
6
Yazar
5
Anahtar Kelime