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All Journal MESA (Teknik Mesin, Teknik Elektro, Teknik Sipil, Teknik Arsitektur) Journal of Mechanical and Manufacture
Dian Susanto
Sekolah Tinggi Teknologi Garut
Aerospace Engineering Automotive Engineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Evaluasi Ketangguhan Alumunium terhadap Beban Dinamis melalui Pengujian Impak Charpy Takikan V Trisanti, Trisanti; Susanto, Dian; Kasda, Kasda; Rachman, Maulana; Irawan, Yusril
MESA (Teknik Mesin, Teknik Elektro, Teknik Sipil, Teknik Arsitektur) Vol. 7 No. 1 (2023): MESA (Teknik Mesin, Teknik Elektro, Teknik Sipil, Teknik Arsitektur)
Publisher : FAKULTAS TEKNIK UNIVERSITAS SUBANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35569/ftk.v7i1.1949

Abstract

This study evaluates the toughness of aluminum against dynamic loads through V notch Charpy impact testing. Tests were conducted on aluminum specimens with varying temperatures to observe their effect on impact energy, fracture type, and transition temperature. The methodology involved the use of an impact machine to present consistent and reliable data. The main findings include the influence of V notches on the impact characteristics of aluminum, identifying critical zones that may experience deformation or cracks. Results show that impact energy and the predominance of ductile fracture increase with higher temperatures. At low temperatures, brittle fracture is dominant. A transition temperature curve was successfully drawn, showing the change in material properties from ductile to brittle. It has been demonstrated that V notch Charpy impact testing is useful for evaluating aluminum toughness and can help improve the design and safety of aluminum components in dynamic load applications.
Analisis Kinerja Mesin CNC Milling 3 Axis pada Pembuatan Pipa Jet Oil Trisanti, Trisanti; Susanto, Dian; Atmadja, Ifan Haristian
MESA (Teknik Mesin, Teknik Elektro, Teknik Sipil, Teknik Arsitektur) Vol. 8 No. 1 (2024): MESA (Teknik Mesin, Teknik Elektro, Teknik Sipil, Teknik Arsitektur)
Publisher : FAKULTAS TEKNIK UNIVERSITAS SUBANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35569/ftk.v8i1.1950

Abstract

The study evaluated the performance of a 3-axis CNC milling machine in the production of jet oil pipes with a focus on achieving a high level of precision. Through variation of cutting parameters and feeding speed, the machine succeeded in reaching the industry standard for jet oil pipe production. Optimization of parameters improved production efficiency, demonstrated by results that achieved the desired precision tolerance. Cutting tool wear analysis provides insight into machine performance and possible repair actions. This research confirms the potential of 3 axis CNC milling machines in addressing the challenge of high-precision jet oil pipe production, contributing to the development of CNC Milling technology in the context of the oil and gas industry.
FLUID DYNAMIC SIMULATION ON THE FLARE OF COMBUSTION OF GAS FROM BIOMASS GASIFICATION susanto, dian; Kosim, Muhtar; Wibowo, Ari
Jurnal Mekanika dan Manufaktur Vol 3 No 1 (2023)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/jmm.v3i1.5682

Abstract

The use of energy which always comes from fossil fuels will eventually run out, so the development of renewable energy or alternative energy is very important to maintain petroleum reserves and as a substitute for fossil fuels which are the main energy source. One alternative energy is biomass which has not been widely used by the gasification method. The gas produced by the gasification process is utilized by burning it in a flare to get a flame. In this study, the 3D simulation method with Computational Fluid Dynamics (CFD) was used to determine the temperature distribution on the flare walls using CFD simulations and to compare the temperature of the flare walls from the CFD simulation results with the test results. The results of this study, the distribution of combustion occurs in the flare with a temperature of 1106°C in the upper area close to the outlet boundary. The wall temperature comparison shows that the CFD simulation tends to be similar to the test results. This shows that computational fluid dynamic simulations can be used to predict fluid flow rates and combustion reactions.
CNN-BASED ARTIFICIAL INTELLIGENCE (AI) IMPLEMENTATION TO IDENTIFY BASMATI RICE IN SUBANG DISTRICT Masriwilaga, Ari ajibekti; wibowo, ari; susanto, dian
Jurnal Mekanika dan Manufaktur Vol 3 No 1 (2023)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/jmm.v3i1.6371

Abstract

The existence of Basmati rice among the upper middle class in Indonesia is increasingly popular. Unfortunately, this rice is only grown in northern India and Pakistan. Fulfillment of rice must be imported and the price in Indonesia is relatively expensive. Responding to this phenomenon, the Center for Rice Research (BB Padi), the Agricultural Research and Development Agency succeeded in assembling a special rice variety Basmati. And given the name Baroma, an abbreviation of type Basmati Aromatic rice. And Baroma rice was launched in Subang in 2019 until now it has been recorded that several agricultural lands in Subang have planted this type. The more types of rice varieties, the more types of rice will be found. So that it will make consumers difficult to distinguish between types of rice with one another. Therefore, we need a solution to overcome this problem. And one solution that can be used is to use AI technology, as in the research we did. Using the CNN algorithm produces very good accuracy for detecting types of rice such as the type of data used for training data and test data. From the results of the model training carried out, it produces an accuracy rate of 98,52% while model testing to see how well the model predicts the label correctly is 97,80%.
OPTIMIZATION OF PREDICTION AND PREVENTION OF DEFECTS ON METAL BASED ON AI USING VGG16 ARCHITECTURE kosim, muhtar; Wibowo, Ari; Setioputro, Novandri Tri; Kasda; Susanto, Dian
Jurnal Mekanika dan Manufaktur Vol 3 No 1 (2023)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/jmm.v3i1.6542

Abstract

Manufacturing is one of the most valuable industries in the world, it can be automated without limits but still stuck in traditional manual and slow processes. Industry 4.0 is racing to define a new era in digital manufacturing through the implementation of Machine Learning methods. In this era, Machine learning has been widely applied to various fields and will certainly be very good applied in the manufacturing world. One of them is used to predict and prevent defects in metal. The process of predicting and preventing defects in metal is one of the important efforts in improving and maintaining production quality. Accuracy in predicting and preventing defects in metal can be an innovation and competitiveness in technology, both in production methods, and improving product safety and its users. Human operators and inspectors without digital assistance generally can spend a lot of time researching visual data, especially in high-volume production environments. For this reason, there needs to be research in developing Machine Learning technology in an effort to prevent the occurrence of defects in metal. And one of the development of this technology by using Convolutional Neural Network (CNN) architecture Visual Geometry Group 16 layer (VGG16). As for the metal defect dataset with 10 classes with details for training data as many as 17221, and test dataset as many as 4311, From the use of methods and datasets available, has been done training model used and produce very good accuracy, that is equal to 89% and testing with accuracy equal to 76%. And also done Interpreter process against new input data, to know metal defect type, prediction accuracy and appropriate action to prevent and overcome metal defect type result of Interpreter process application.
Co-Authors Ari Wibowo Atmadja, Ifan Haristian Irawan, Yusril Kasda Kasda, Kasda Masriwilaga, Ari Ajibekti Maulana Rachman Muhtar Kosim Setioputro, Novandri Tri Trisanti, Trisanti