Special Issue on “Mechanical Behaviour of Aluminium Alloys”

Branco, R.; Berto, Filippo; Kotousov, Andrei

doi:10.3390/app8101854

Cited by 6 publications

(7 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Jumlah tersebut naik 4,45% dibandingkan produksi tahun sebelumnya yang berjumlah 65,1 juta metrik ton (Data aluminum 2022). Penggunaan aluminium yang luas di berbagai aspek industri modern mendorong engineer untuk lebih teliti dalam mengetahui sifat mekanik aluminium, hal ini bertujuan untuk mencegah terjadinya kegalan pada material, sifat mekanik suatu material memiliki peran yang penting dalam menentukan bahan untuk komponen industri modern demi mencegah terjadinya kegagalan terhadap komponen industri secara prematur (Branco, 2018). Kekuatan tarik dan kekuatan luluh merupakan dua diantara sifat mekanik material, sifat mekanik material dipengaruhi oleh beberapa faktor seperti sruktur mikro, komposisi kimia dan berbagai perlakuan panas (heat treatment) (Antonio Augusto Morini, Manuel J. Ribeiro, 2019).…”

Section: Iunclassified

Pemilihan Algoritma Machine Learning Yang Optimal Untuk Prediksi Sifat Mekanik Aluminium

Leni

2023

JEEMM

View full text Add to dashboard Cite

<em>This study designs and compares optimal machine learning models to predict the mechanical properties of aluminum, including Yield Strength (YS) and Tensile Strength (TS), based on the percentage composition of aluminum's chemical elements. The machine learning modeling in this study has nine input variables consisting of aluminum chemical elements such as Al, Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, and two output or target variables consisting of YS and TS. Additionally, Heatmap correlation is used to observe the correlation between chemical elements and the mechanical properties of aluminum. Three machine learning algorithms, namely Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN), are compared in this study. The comparison of these algorithms shows that Random Forest (RF) outperforms the other algorithms in predicting YS with MAE of 11.44, RMSE of 14.282, and R value of 0.93. On the other hand, ANN performs better in predicting TS with MAE of 19.593, RMSE of 22.005, and R value of 0.947.</em>

show abstract

Section: Iunclassified

Pemilihan Algoritma Machine Learning Yang Optimal Untuk Prediksi Sifat Mekanik Aluminium

Leni

2023

JEEMM

View full text Add to dashboard Cite

show abstract

“…This mission can be achieved by replacing the welding traditionally used with their lighter equivalents and incorporating new innovations in the welding process (Stojanovic et al, 2018;Sathari et al, 2014). A noticeable indication of this phenomenon is the widespread usage of aluminum alloys, especially in the aerospace and automotive sectors (Branco et al, 2018). Technological issues with welding therefore restrict such materials area of use.…”

Section: Introductionmentioning

confidence: 99%

Management and Production Engineering Review

Dandge¹,

Chakraborty

2021

View full text Add to dashboard Cite

This paper is the first to optimize the friction stir welding (FSW) process considering the Clamp Pitch (mm) and Clamping Torque effect using the Various combinations of parameters were constructed using factorial design and responses, resulting in a comprehensive factorial analysis. Conspicuous changes in the tensile strength, yield strength, hardness, and power profile were observed for all amalgamations of parameters. Significant parameters of the FSW process have been considered in many optimization studies, however, the effect of the Clamp Pitch (mm) and Clamping Torque (Nm) has been never studied. Three levels of three parameters were used in the experiments: Clamp Pitch, tool rotational speed and Clamping Torque. The full factorial analysis was performed, was applied as an approach for selecting the values of the Significant factors of the parameters. For each result the three key parameters were important with p-values of less than 0.05, suggesting their significance in the phase of FSW. Mathematical models built with high R-sq. and least percentage error were adequate for the investigated responses. The findings were gained by important parameter values factors of 30 mm, 1800 rpm and 70 Nm for the take into consideration parameter range for the Clamp Pitch, rotational speed and Clamping Torque respectively.

show abstract

“…Besides, aluminum alloys are the most frequent type of non-ferrous material employed for an extensive range of applications, namely in the automotive, aerospace, and structural industries, among others [4]. Widespread use of these alloys in the modern world is due to an exceptional blend of material properties, combining low density, excellent strength, corrosion resistance, toughness, electrical and thermal conductivity, recyclability and manufacturability.…”

Section: Introductionmentioning

confidence: 99%

“…Widespread use of these alloys in the modern world is due to an exceptional blend of material properties, combining low density, excellent strength, corrosion resistance, toughness, electrical and thermal conductivity, recyclability and manufacturability. Another key factor is the relatively low cost of aluminum machining, making its alloys very attractive for applications in different sectors [4].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Bilinear Stress-Strain Curve of Aluminum Alloys Using Artificial Intelligence and Big Data

Fernández

Rodríguez-Prieto

Camacho

2020

Metals

View full text Add to dashboard Cite

Aluminum alloys are among the most widely used materials in demanding industries such as aerospace, automotive or food packaging and, therefore, it is essential to predict the behavior and properties of each component. Tools based on artificial intelligence can be used to face this complex problem. In this work, a computer-aided tool is developed to predict relevant mechanical properties of aluminum alloys—Young’s modulus, yield stress, ultimate tensile strength and elongation at break. These predictions are based on the alloy chemical composition and tempers, and are employed to estimate the bilinear approximation of the stress-strain curve, very useful as a decision tool that helps in the selection of materials. The system is based on the use of artificial neural networks supported by a big data collection about technological characteristics of thousands of commercial materials. Thus, the volume of data exceeds 5 k entries. Once the relevant data have been retrieved, filtered and organized, an artificial neural network is defined and, after the training, the system is able to make predictions about the material properties with an average confidence greater than 95 % . Finally, the trained network is employed to show how it can be used to support decisions about engineering applications.

show abstract

Special Issue on “Mechanical Behaviour of Aluminium Alloys”

Abstract: Aluminium alloys are the most common type of non-ferrous material utilised for a wide range of engineering applications, namely in the automotive, aerospace, and structural industries, among others. [...]

Cited by 6 publications

References 12 publications

Pemilihan Algoritma Machine Learning Yang Optimal Untuk Prediksi Sifat Mekanik Aluminium

Pemilihan Algoritma Machine Learning Yang Optimal Untuk Prediksi Sifat Mekanik Aluminium

Management and Production Engineering Review

Prediction of the Bilinear Stress-Strain Curve of Aluminum Alloys Using Artificial Intelligence and Big Data

Contact Info

Product

Resources

About