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WD Lestari
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Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Transportation

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Torque Load Analysis on Rear Axle Shaft Material AISI 4340 Normalized FA Nurmansyah; L Edahwati; RD Issafira; W Saputro; AK Faizin; TP Sari; WD Lestari; N Adyono
BIOMEJ Vol. 2 No. 2 (2022): BIOMEJ
Publisher : UPN 'Veteran" Jawa Timur

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Abstract

The choice of rear axle shaft material on the car needs to be considered because this component affects the performance of the car. This component material is also very necessary, the selection of components used is AISI 4340 Steel Normalized, where this material contains Nickel, Chrom, and Molybden with the characteristics of the AISI 4340 Steel Normalized material is a steel material that has high hardness properties, can accept great pressure and force and not easy to deform. The method used is the Finite Element Method (FEM), this method uses a meshing size of 5 mm, to get more accurate results. Simulation results from rear axle shaft AISI 4340 Steel Normalized to determine the value of stress, strain, and safety factor from load variations of 1200 Nm, 1400 Nm, 1600 Nm, and 2000. The highest maximum stress value occurs at 2000 Nm torque load of 5.586 x 108 Nm. The highest maximum strain value occurs at a torque load of 2000 Nm of 5.034 x 103. The safest value of the safety factor is 2.1 at a torque load of 1200 Nm. The torque load value is directly proportional to the stress and strain values ​​obtained, because the greater the torque load value, the greater the stress and strain values ​​obtained.
Static Loading Analysis on Universal Joint Using Solidworks M Ikhsan; L Edahwati; RD Issafira; W Saputro; AK Faizin; TP Sari; WD Lestari; N Adyono
BIOMEJ Vol. 2 No. 2 (2022): BIOMEJ
Publisher : UPN 'Veteran" Jawa Timur

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This static loading analysis introduction training aims to analyze parts universal joints to the tensile test as an effort to accelerate the improvement of vocational teachers' abilities. In the introduction to analysis training this time using software CAD/CAM ie solidworks which is one software CAD/ CAM which is widely used in the manufacturing industry in terms of designing or analyzing a product design. The training method is carried out by first installing solidworks software to computer equipment, secondly making parts from universal joints, third assembling from parts that have been made, fourth doing drawing 2D from part and assembly that have been made, the five static loading analyzes of universal joint parts with tensile test. Election software this is based on its advantages such as easy to learn, widely used in industry, and can be used as a reference for studying software another. As a result of this training, it is hoped that the teachers of SMK Turen Malang will be able to make part3D models and 2D working drawings.
Artificial Neural Network Application for Sepsis Prediction: A Preliminary Study AK Faizin; W Saputro; RD Issafira; L Edahwati; WD Lestari; TP Sari; N Adyono
BIOMEJ Vol. 2 No. 2 (2022): BIOMEJ
Publisher : UPN 'Veteran" Jawa Timur

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Sepsis is one of the leading causes of mortality in hospitalized patients. It is very difficult to find the symptoms of sepsis because of their similarity to the symptoms of other diseases. This paper aims to deliver an artificial neural network implementation in medical decisions support. This study tries to predict sepsis and healthy patient based on vital signs such as heart rate, systolic blood pressure, and diastolic blood pressure taken from the MIMIC-III clinical database. There were several extraction processes applied to vital sign signals such as using the statistical tools, discrete wavelet transforms, and Hilbert-Huang Transform. The ANN algorithm predicts the sepsis patient with 96.7% of accuracy. However, based on the medical requirement for artificial intelligent implementation, this result does not satisfy the requirement as the false positive error is 2.9%.
Co-Authors AK Faizin FA Nurmansyah L Edahwati M IKHSAN N Adyono RD Issafira TP Sari W Saputro