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All Journal Dinamika Teknik Mesin Jurnal Rekayasa Material, Manufaktur & Energi Jurnal Surya Teknika International Journal of Innovation in Mechanical Engineering and Advanced Materials
Yuda Perdana Kusuma
Universitas Andalas
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics

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Uji Kinerja Pengolahan Traktor Roda Empat Model At 5470 Dengan Bajak Piring (Disk Plow) Pada Tanah Dilahan Percobaan BPTP Sumatera Barat Desmarita Leni; Veny Selviyanty; Yuda Perdana Kusuma
Jurnal Surya Teknika Vol 9 No 2 (2022): JURNAL SURYA TEKNIKA
Publisher : Fakultas Teknik UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jst.v9i2.4369

Abstract

The use of tractors as a source of propulsion in land cultivation for agriculture is expected to ease the work of farmers and speed up the processing time of the land. This study tested the performance of the Iseki AT 5470 4-wheel tractor using a disk plow on dry soil in the pilot land of the West Sumatra AIAT. The 4-wheel tractor test was carried out on land with a size of 20 X 10 meters. The plowing pattern used in the study was a plow pattern from the edge. The measurement of the width of the plow using the plow implement was 1 meter and the depth of the plow was 15.7 cm. The efficiency of the tractor was 69.63% with fuel consumption of 6.5 liters. The low efficiency of the tractor is due to the processing area being too small, so there are many turns that waste a lot of time during testing
Perbandingan Alogaritma Machine Learning Untuk Prediksi Sifat Mekanik Pada Baja Paduan Rendah Desmarita Leni; Yuda Perdana kusuma; Ruzita Sumiati; Muchlisinalahuddin .; Adriansyah .
Rekayasa Material, Manufaktur dan Energi Vol 5, No 2: September 2022
Publisher : Fakultas Teknik UMSU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/rmme.v5i2.11407

Abstract

The development of industrial technology encourages companies to be selective in determining the mechanical properties of materials, one of which is low-alloy steel. The purpose of knowing the mechanical properties of low alloy steel is to support the success of a construction product, transportation, machine elements, and so on. Heat treatment of metal is one of the test methods to determine the mechanical properties of steel by heating the steel at a certain temperature. The selection of low alloy steel composition has various variations to be applied so as to obtain the desired mechanical properties. The mechanical properties of low-alloy steel are strongly influenced by the composition contained in the steel. If the composition of the steel is added to a new element, the mechanical properties of the steel will change, so it needs to be retested. This research uses machine learning modeling to predict the mechanical properties of low-alloy steels based on their chemical compositions. This study compares three algorithms, namely decision tree (DT), random forest (RF), and artificial neural network (ANN), where the ANN algorithm has better performance by producing an RMSE value of 6.187 with training cycle parameter settings of 30.000, learning rate 0.007, momentum 0.9, and size of hidden layer 9.
Evaluasi sifat mekanik baja paduan rendah bedasarkan komposisi kimia dan suhu perlakuan panas menggunakan teknik exploratory data analysis (EDA) D. Leni; F. Earnestly; R. Sumiati; A. Adriansyah; Y.P. Kusuma
Dinamika Teknik Mesin Vol 13, No 1 (2023): Dinamika Teknik Mesin
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/dtm.v13i1.624

Abstract

This research aims to evaluate the relationship between the chemical composition of low alloy steel, temperature, and the mechanical properties of low alloy steel using Exploratory Data Analysis (EDA) techniques. The low alloy steel dataset is visualized using a correlation heat map, which shows a relationship between the mechanical properties of low alloy steel and its chemical composition and heat treatment temperature. Based on the results of the correlation heat map, an evaluation is carried out using scatterplots. The visualization results with scatterplots show a trend line indicating a linear relationship between YS and the elements V, Ni, and Mn, as well as a positive relationship between TS and V. In addition, there are determination coefficients (R²) that show how well the trend line can explain the variation of the data. The R² values obtained for V are 0.405, Ni is 0.226, Mn is 0.159, and Mo is 0.130, while El and RA have a positive correlation with temperature with R²values of 0.166 and 0.320, respectively. It can be concluded that the evaluation results using scatterplots and R² show that variations in chemical composition and heat treatment temperature affect the mechanical properties of low alloy steel. The correlations that occur between these variables can help in determining the pattern of the relationship and evaluating how well the trend line can explain the variation of the data. The use of correlation heat maps and scatter plots can help in decision-making and developing low-alloy steel materials that meet specific needs.
THE IMPLEMENTATION OF PANDAS PROFILING AS A TOOL FOR ANALYZING MECHANICAL PROPERTIES DATA OF NICKEL-BASED SUPERALLOYS BASED ON ALLOY CHEMICAL COMPOSITION Desmarita Leni; Yuda Perdana Kusuma; Muchlisinalahuddin Muchlisinalahuddin; Ruzita Sumiati; Hendri Candra Mayana
International Journal of Innovation in Mechanical Engineering and Advanced Materials Vol 4, No 3 (2022)
Publisher : Universitas Mercu Buana, Prodi S2 Teknik Mesin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijimeam.v4i3.19439

Abstract

The purpose of this study is to evaluate the mechanical properties of nickel-based superalloys with variations in alloy chemical compositions using the Exploratory Data Analysis (EDA) method with the assistance of the pandas profiling library on Google Colab. In this study, data from 312 tensile tests of nickel-based superalloys were used as research samples, with alloy chemical compositions including carbon (C), manganese (Mn), silicon (Si), chromium (Cr), nickel (Ni), molybdenum (Mo), vanadium (V), nitrogen (N), niobium (Nb), cobalt (Co), tungsten (W), aluminum (Al), and titanium (Ti), as well as mechanical properties such as yield strength (YS), tensile strength (TS), and elongation (EL). The methodology used in this study was the EDA method with the assistance of the pandas profiling library on Google Colab, which enables the automatic creation of a dataset report, presenting information on various aspects such as data structure, descriptive statistics, correlation, distribution, and missing values. The results show that yield strength has a fairly high correlation with titanium (0.51), medium correlations with nickel (0.25), vanadium (0.2), and cobalt (0.2). Tensile strength in nickel-based superalloys has a fairly high correlation with yield strength (0.88), carbon (0.49), and cobalt (0.55), and medium correlations with titanium (0.25) and vanadium (0.25). Elongation in nickel-based superalloys has a negative and fairly high correlation with tensile strength (-0.62) and yield strength (-0.58). Some warnings for missing data and zero values in some variables were identified. These results indicate that the pandas profiling library can be used as a tool to analyze the data of mechanical properties of nickel-based superalloys quickly and easily, and provide clear information on data patterns, data structure, and correlation among data.
Co-Authors Adriansyah Adriansyah Desmarita Leni F. Earnestly Hendri Candra Mayana Muchlisinalahuddin Ruzita Sumiati Veny Selviyanty