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All Journal Jurnal Rekayasa Material, Manufaktur & Energi Malikussaleh Journal of Mechanical Science Technology
Rizqullah, Akbar
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Aerospace Engineering Automotive Engineering Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Initial Crack Analysis on 175 Nm Torque Differential Helical Gear Using Finite Element Method for Structural Failure Prediction Fauzan, Fauzan; Harmin, Amalia; Isra, Muhammad; Rizqullah, Akbar; Rizki, Muhammad Nuzan
Malikussaleh Journal of Mechanical Science and Technology Vol. 9 No. 1 (2025): Malikussaleh Journal of Mechanical Science and Technology (MJMST)
Publisher : Malikussaleh University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/mjmst.v9i1.21607

Abstract

Differential helical gears are critical components in power transmission systems of vehicles, responsible for transmitting torque from the engine to the drive wheels. These components operate under continuous high torque loads and are susceptible to failure due to material fatigue and the presence of initial cracks. This study aims to predict the initial crack location and evaluate the potential failure of a differential helical gear subjected to a 175 Nm torque using the Finite Element Method (FEM). The gear was modeled with actual dimensions and simulated using ANSYS software under two conditions: without cracks and with an initial crack. The simulation results show that the maximum shear stress without a crack is 45.82 MPa, while with an initial crack, it increases to 66.14 MPa, exceeding the allowable shear stress of ASTM A36 material at 45.45 MPa. This significant increase in stress due to the crack indicates a high risk of structural failure. Therefore, finite element analysis proves to be an effective tool for early crack detection and stress distribution evaluation, which is essential for improving the reliability of gear design and material selection.
Feasibility Analysis and Implementation of FreeCAD Open Source Computer-Aided Design (CAD) Software as an Alternative Replacement for Inventor Student Version Software Isra, Muhammad; Zulfri, Muhammad; Fauzan, Fauzan; Harmin, Amalia; Rizqullah, Akbar; Rizki, Muhammad Nuzan
Malikussaleh Journal of Mechanical Science and Technology Vol. 9 No. 1 (2025): Malikussaleh Journal of Mechanical Science and Technology (MJMST)
Publisher : Malikussaleh University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/mjmst.v9i1.21740

Abstract

Industry 4.0 is supported by nine foundational pillars, including Big Data and Analytics, Autonomous Robots, Simulation, Horizontal and Vertical System Integration, the Industrial Internet of Things (IIoT), Augmented Reality, Cloud Computing, Additive Manufacturing, and Cybersecurity. This study focuses on horizontal and vertical system integration, which enables seamless information flow across departments, suppliers, and customers through smart systems and IoT devices. One important tool in supporting industrial integration is Computer Aided Design (CAD), which plays a key role in product design and the generation of blueprints that bridge design and manufacturing functions. Most widely used CAD software such as AutoCAD, SolidWorks, Inventor, and CATIA are commercial and come with high licensing costs. At Samudra University, Autodesk Inventor Student Version was previously utilized for educational purposes; however, due to its feature limitations and temporary license, an alternative was sought. In collaboration with the Mechanical Engineering Department, FreeCAD was explored as an Open Source solution. This research aims to identify a free and open-source CAD software that offers features comparable to commercial CAD tools and can be further developed without licensing constraints. The methodology involves collecting various Open Source CAD applications, establishing evaluation criteria, testing core functionalities, and selecting the most suitable application to support academic and industrial design needs.
Machine Learning-Based Regression Model for Predicting Global Horizontal Radiation and Global Horizontal Irradiance: A Case Study in Banda Aceh Fajar Sabri, M Salamul; Muhammad, Ikramullah; Rizqullah, Akbar; Fikri, Thaharul; Fajri, Nural; Mizanus Sabri, Faris Ahmad
Rekayasa Material, Manufaktur dan Energi Vol 8, No 2: JULI 2025
Publisher : Fakultas Teknik UMSU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/rmme.v8i2.26011

Abstract

Global Horizontal Radiation (GHR) and Global Horizontal Illumination (GHI) are critical environmental parameters that play a vital role in solar energy development, precision agriculture, and sustainable urban planning. However, their prediction remains challenging due to the high variability caused by atmospheric conditions. This study evaluates the performance of various machine learning models in predicting GHR and GHI using a comprehensive dataset comprising 29 environmental features. The models tested include Linear Regression, Random Forest Regressor, XGBoost Regressor, LightGBM Regressor, Support Vector Regressor (SVR), and Artificial Neural Network (ANN). The results consistently show that ensemble-based models, particularly LightGBM Regressor, provide the best predictive performance for both target variables, achieving very high R-squared values (approaching 0.999). XGBoost and Random Forest also demonstrate highly competitive performance. ANN performs well, while Linear Regression and SVR show lower accuracy. These findings underscore the significant potential of advanced machine learning models in predicting environmental parameters with high accuracy, which has important implications for renewable energy optimization, smart agriculture, and sustainable urban planning.
Co-Authors Amalia Harmin Fajar Sabri, M Salamul Fajri, Nural Fauzan Fikri, Thaharul Mizanus Sabri, Faris Ahmad Muhammad Isra Muhammad Nuzan Rizki Muhammad Zulfri Muhammad, Ikramullah