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Abstrak :
[Komposit bermatriks aluminium dengan penguat partikel Al2O3 berukuran nano umum digunakan untuk aplikasi dengan performa yang tinggi karena aluminium memiliki sifat ringan dan Al2O3 memiliki performa yang baik pada suhu tinggi. Pada penelitian ini, penambahan Al2O3 dengan fraksi volum 0,2%, 0,5%, 0,7%, 1,0%, and 1,2% dilakukan untuk menentukan titik optimum dari kelima komposisi. Magnesium sebanyak 10 wt.% ditambahkan sebagai wetting agent. Hasil penelitian menunjukkan kekuatan optimum dicapai dengan penambahan fraksi volum nano-Al2O3 sebanyak 0,2% dengan 200,84 MPa dan keuletan yang baik, didukung dengan rendahnya porositas, rendahnya aglomerasi, dan pembentukan dimple pada permukaan patah. ......Aluminium Matrix Composites (AMCs) reinforced with nano-sized Al2O3 particles are widely used for high performance application because aluminium has light weight and alumina has good performance at high temperature. In this study, the percentage of nano-sized Al2O3 with volume fraction 0.2%, 0.5%, 0.7%, 1.0%, and 1.2% are performed to determine the optimum point of the fifth variation. Magnesium with 10 wt.% are added as a wetting agent. The result showed the optimum strength was reached by 0.2 %Vf nano-Al2O3 reinforced composite with 200.84 MPa and enough ductility, supported by evidence low porosity, low agglomeration, and dimples formation on SEM image. , Aluminium Matrix Composites (AMCs) reinforced with nano-sized Al2O3 particles are widely used for high performance application because aluminium has light weight and alumina has good performance at high temperature. In this study, the percentage of nano-sized Al2O3 with volume fraction 0.2%, 0.5%, 0.7%, 1.0%, and 1.2% are performed to determine the optimum point of the fifth variation. Magnesium with 10 wt.% are added as a wetting agent. The result showed the optimum strength was reached by 0.2 %Vf nano-Al2O3 reinforced composite with 200.84 MPa and enough ductility, supported by evidence low porosity, low agglomeration, and dimples formation on SEM image. ]

Abstrak :
Rasio kekuatan dan densitas aluminium yang tinggi serta ketahanan aluminium yang tinggi terhadap korosi membuat aluminium menjadi material yang populer digunakan oleh industri aviasi. Sebagai salah satu aplikasinya, aluminium kerap digunaakn sebagai komponen pada helikopter. Menurut Rashid, dkk rotor adalah komponen yang paling rentan pada helikopter. Lalu Weber, dkk mengatakan bahwa 65% pilot pernah menerbangkan helikopter dengan kondisi rotor yang rusak. Banyaknya faktor yang dapat menyebabkan kerusakan pada rotor mempersulit penemuan solusi untuk kegagalan komponen. Ketangguhan patah adalah ketahanan suatu material akan perambatan retak. Penambahan laju retak dapat terjadi karena beberapa mekanisme seperti fatigue, creep, kesalahan pemilihan material, SCC, dan lainnya. Karena itu, rekayasa material mengenai ketangguhan patah menarik sangat menarik untuk dipelajari. Metode eksperimental trial and error untuk rekayasa material memerlukan banyak waktu panjang, biaya tinggi, dan akurasi penelitian yang sangat ditentukan oleh kemampuan peneliti. Metode pembelajaran mesin regresi menggunakan data dokumentasi terdahulu sehingga dapat memangkas waktu dan biaya untuk rekayasa material. Pada penelitian ini berhasil dikembangkan model pembelajaran mesin dengan menggunakan algoritma XGBoost. Kemampuan prediksi cukup baik, dibuktikan dari perbandingan nilai aktual dan prediksi serta nilai metrik model sebesar 0,906. ......The high strength-to-density ratio and corrosion resistance of aluminum have made it a popular material in the aviation industry. One of its applications is in helicopter components. According to Rashid et al., the rotor is the most vulnerable component in a helicopter. Furthermore, Weber et al. stated that 65% of pilots have flown helicopters with damaged rotors. The numerous factors that can cause rotor damage make finding solutions to component failures challenging. Fracture toughness is the resistance of a material to crack propagation. Increased crack propagation can occur due to various mechanisms such as fatigue, creep, material selection errors, SCC (Stress Corrosion Cracking), and others. Therefore, studying fracture toughness in materials engineering is highly interesting. Traditional trial-and-error experimental methods for materials engineering require extensive time, high costs, and research accuracy heavily dependent on the abilities of the researchers. Regression machine learning methods using past documentation data can help reduce time and costs in materials engineering. In this study, a machine learning model using the XGBoost algorithm was successfully developed. The predictive capability was quite good, as evidenced by the comparison between actual and predicted values, as well as a model metric value of 0.906.

Abstrak :
This atlas provides an in-depth understanding of the metallurgy and fracture behavior of aluminum-silicon casting alloys, which are used in a wide variety of automotive, aerospace, and consumer product applications. The atlas includes over 300 high-definition microfractographs of fracture profiles and fracture surfaces, accompanied with detailed descriptions and analysis of the fracture features and their significance in the selection, processing, properties, and performance of the alloy.