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Muhamad Adriansyah
Universitas Riau Kepulauan
Aerospace Engineering Automotive Engineering Control & Systems Engineering Engineering Industrial & Manufacturing Engineering

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Analyis Of Gear Strenght In Machine Construction amiko amiko; Yuki Alvandi Pratama; Beny Gusman; Muhamad Adriansyah; Fardin Hasibuan; Eddy Efiano
METALOGRAM Metalogram Vol.2 No.2 (April, 2026)
Publisher : Universitas Riau Kepulauan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33373/mtlg.v2i2.7457

Abstract

Gears are essential components in mechanical transmission systems, responsible for transferring power between shafts in various types of machinery. The strength of gears plays a critical role in the efficiency and reliability of these systems, as improper load handling can lead to material failure or gear damage. This study aims to analyze the strength of gears by considering several factors such as material properties, geometry, and operational conditions. The materials tested in this research include carbon steel SAE 1045, alloy steel SAE 4140, and stainless steel (SS 304), all of which are commonly used in gear applications. The gear geometry analyzed includes variations in module (2 mm and 3 mm), the number of teeth (20 teeth), and the pressure angle (20 degrees). The research methodology includes Finite Element Analysis (FEA) to simulate stress distribution on gears under both dynamic and static loading conditions, along with experimental testing to validate the simulation results. The results show that SAE 4140 alloy steel exhibits superior tensile strength and wear resistance compared to SAE 1045 carbon steel, though at a higher cost. Stainless steel (SS 304) offers excellent corrosion resistance but lower tensile strength, making it less suitable for high-load applications. Additionally, increasing the gear size (3 mm module) reduces stress on the teeth but increases the overall size and weight of the gear. This study provides important insights into material selection and gear design, helping to improve the strength and durability of mechanical transmission systems.
AI-Based Material Selection Evaluation for Heavy Construction Machinery Components Yuki Alvandi; Beny Gusnal; Muhamad Adriansyah
METALOGRAM Metalogram Vol.02 No.3 (August,2026)
Publisher : Universitas Riau Kepulauan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33373/mtlg.v2i3.7456

Abstract

Selecting the right material for heavy construction machinery components is a crucial step in ensuring operational performance, efficiency, and sustainability. Artificial intelligence (AI) technology offers a new approach to this selection process with its fast and high-precision data analysis capabilities. This study aims to evaluate the effectiveness of AI implementation in material selection for heavy construction machinery components. The study utilized machine learning algorithms, such as random forest and artificial neural networks, to analyze material parameters including strength, wear resistance, density, and production cost. The results showed that the AI-based method can improve material selection efficiency by up to 35% compared to conventional methods. In addition, this method is also able to reduce selection errors that often occur in manual approaches. By utilizing AI, the selection process becomes faster, more accurate, and more sustainable, supporting the development of more modern and environmentally friendly construction technologies.
Co-Authors amiko amiko Beny Gusman Beny Gusnal Eddy Efiano Fardin Hasibuan Yuki Alvandi Yuki Alvandi Pratama