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All Journal VANOS Journal of Mechanical Engineering Education Teknobiz : Jurnal Ilmiah Program Studi Magister Teknik Mesin Mechanical Engineering for Society and Industry Automotive Experiences Ramatekno

Karmiadji, Djoko Wahyu

Pancasila University, Indonesia

Author-ID : 838784

Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Transportation

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Evaluation of Operational Loading of the Light-Rail Transit (LRT) in Capital Region, Indonesia Djoko Wahyu Karmiadji; Muchamad Gozali; Anwar Anwar; Hedi Purnomo; Muji Setiyo; Ramli Junid
Automotive Experiences Vol 3 No 3 (2020)
Publisher : Automotive Laboratory of Universitas Muhammadiyah Magelang in collaboration with Association of Indonesian Vocational Educators (AIVE)

In 2015, the Indonesian government issued regulations to accelerate the implementation of integrated Light-Rail Transit (LRT) in the capital region and its surroundings. In order to ensure its operational safety, experimental work is required to test components’ strength of the manufactured LRT structures. Following the JIS 7105 standard test method, the strain and deflection of the structures were measured by vertical load, compression, rotation, and three-point load support test. The critical area estimated in the railroad structure were conducted according to the finite element method, in which strain gauges are installed in areas where the stress concentration exceeds nominal pressure, namely notches, bends, and junction areas. The result shows that the maximum stress on the LRT train structure occurs at the door, where maximum compressive strain value is -1082 μe » -75.74 MPa on the left and the maximum tensile strain value is 597 μe » 41.79 MPa at the right door. The results of fatigue load analysis represent the average stress (σm) and voltage amplitude (σa) at the coordinate system located in the Søderberg triangle. Meanwhile, the camber value with the full vertical load still has a positive value of 3.03 mm, which indicates a safe limit.

Bogie Frame Structure Evaluation for Light-Rail Transit (LRT) Train: A Static Testing Djoko Wahyu Karmiadji; Budi Haryanto; Ogi Ivano; Mustasyar Perkasa; Abdul Rohman Farid
Automotive Experiences Vol 4 No 1 (2021)
Publisher : Automotive Laboratory of Universitas Muhammadiyah Magelang in collaboration with Association of Indonesian Vocational Educators (AIVE)

A new bogie frame of Light-Rail Transit (LRT) is having its strength of structure verified with experimental static testing according to EN 13749 standards. Static testing of bogie frame structure of LRT is performed by using a combination of seven tensile and compression loads that comprise of operational loads (normal service) and over-loads (exceptional service). Measurement parameters of bogie frame are strain and deflection values. The strain and deflection values resulted at every step of the load test were measured and monitored to further be used as analytic data. This data is then compared to the stress data of finite element analysis to check its deviation value. Testing results show the maximum stress value is 81.48 MPa on operational load, meanwhile, for exceptional load case, maximum stress is 120.96 MPa and deflection value is 1.25 mm. The maximum stress value is still below yield strength of bogie frame material S 555J2 (y=355 MPa). According to testing data, structure of bogie frame LRT fulfill as the acceptance criteria.

Theoretical Experiments on Road Profile Data Analysis using Filter Combinations Karmiadji, Djoko Wahyu; Rosyidi, M.; Widodo, Tri; Zaenal, Harris; Nurdam, Nofriyadi; Kadir, Andi M.; Hidayat, Sofwan; Bismantoko, Sahid; Pramana, Nurhadi; Winarno, Winarno
Automotive Experiences Vol 6 No 3 (2023)
Publisher : Automotive Laboratory of Universitas Muhammadiyah Magelang in collaboration with Association of Indonesian Vocational Educators (AIVE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.9901

Identification of road profiles is needed to provide the input of automotive simulation and endurance testing. The analysis with estimation methods is mostly done to identify road profiles. The main goal of analysis methods is to obtain the data of vertical displacements due to road profile measurement. The acceleration data is obtained from measuring road profile by using 4 sensors of accelerometer placed on each car wheel. The measuring data is converted to be vertical displacement data by using a "double integrator", however, it is not easy to get accurate results since the signal obtained carries a lot of noise and it is necessary to design the right filter reduce the noise. In this study, the signal filtering methods reducing the noise were used Fast Fourier Transform (FFT) and Kalman Filter (KF) combination. Experiments were carried out by combining Fast Fourier Transform and Kalman Filters using an input signal with unit (volt) in the time domain. In addition, this research focused on preparing the survey data that has been obtained by eliminating the noise to convert becoming the displacement input data for providing the loads of automotive simulation testing.

The Road Safety: Utilising Machine Learning Approach for Predicting Fatality in Toll Road Accidents Mutharuddin, Mutharuddin; Rosyidi, M.; Karmiadji, Djoko Wahyu; Fitri, Hastiya Annisa; Irawati, Novi; Waskito, Dwitya Harits; Mardiana, Tetty Sulastry; Subaryata, Subaryata; Nugroho, Sinung
Automotive Experiences Vol 7 No 2 (2024)
Publisher : Automotive Laboratory of Universitas Muhammadiyah Magelang in collaboration with Association of Indonesian Vocational Educators (AIVE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.11082

Road safety is one of the critical government transportation concerns, especially on the toll roads. With the increasing number of toll roads as part of infrastructure planning, road traffic accidents are significantly escalating. Developing a system that predicts accidents on toll roads will benefit to reduce the harm that is caused by traffic accidents. This study will propose a method for analysing toll road accidents in Indonesia using historical toll road accident data as a dataset to become a pattern to examine the frequency of accidents. This dataset consists of various parameters from three main factors that cause accidents: human, environmental, and road infrastructure factors. Machine learning technique will be mainly used to determine the most influencing factors by employing classifiers such as Logistic Regression (LR), Decision Tree (DT), Gaussian Naïve Bayes (GNB), and K-Nearest Neighbors (KNN) can construct the prediction model. Fourteen subfactors from the data were used to predict the future fatalities caused by accidents, which allowed the system to forecast the accident fatality. The results show accuracy performance on the test set with LR, DT, KNN, and GNB models, 85.3%, 79.4%, 87.1%, and 77.1%, respectively. The KNN Classifier model has the most minor error value of 0.6 compared to the other models. The study’s findings will help analyse the causal factors involved in toll road accidents and could be utilised by road authorities to employ risk control options to mitigate the ramifications.

Experimental and Finite Element Study of Rollover Protection Structure for a 22-Seat Man Hauler Superstructure Vehicle Gozali, Muchamad; Karmiadji, Djoko Wahyu; Libyawati, Wina; Haryanto, Budi; Masrur, Muhamad; Setyawan, Arief; Sulistiyo, Wahyu; Nuramin, Makmuri; Anwar, Anwar; Susilo, Budi
Automotive Experiences Vol 7 No 3 (2024)
Publisher : Automotive Laboratory of Universitas Muhammadiyah Magelang in collaboration with Association of Indonesian Vocational Educators (AIVE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.11380

The application of man hauler which classified as heavy-duty vehicle and operated on the upper ground mining, requires high safety measurement as arrange in the UN-ECE No. 66. The safety measure demands vehicles to undergo both structural testing and analysis. The investigation of structural testing for heavy-duty vehicles has been developed to the rollover testing that used tilting platform, to see the deformation impact toward the residual space and foresight opportunities for further development on the vehicle structure or warning system. Rollover testing is costly and time consuming, so new or developed vehicle structure needs finite element model analysis, to predict the deformation level due to rollover incident. Both testing have the same goal which is to confirm the vehicle structure able to protect the passenger compartment. Therefore, this study aims to present a guidance to test a complete set of 22-seat man hauler vehicle with stress distribution analysis, quasi-static loading test of body section, and tilting platform. The results of the stress distribution test are that the load is concentrated on the element number 148 in the rear UNP 100 profile. The results of the quasi-static loading test are that the maximum stress that occurs is 33 % b the allowable stress. The simulation result under this condition shows that the maximum deflection value occurred in the side frame structure is 167.9 mm. The largest deformation due to rolling test occurred at point E has value of 27 mm located on the right side that experienced impact on the floor during the test. The overall testing and analysis are able to verify and confirm the vehicle structural strength, that the vehicle able to withstand the rollover impact and to protect the passengers.

Co-Authors Abdul Rohman Farid Anwar Anwar Anwar Anwar Anwar Anwar Arief Setyawan Basori Basori Bismantoko, Sahid Budi Haryanto Budi Haryanto Budi Haryanto Budi Prasetyo Budi Susilo Deddy Supriyatna Eko Prastyo Handayani Farid, Abdul Rohman Fitri, Hastiya Annisa Gozali, Muchamad Harris Zaenal, Harris Hedi Purnomo Hidayat, Sofwan Hijaz Ahmad Isach W.Z. Karmiadji Ivano, Ogi Jalaluddin, Mai Nursherida Junid, Ramli Kadir, Andi M. Mardiana, Tetty Sulastry Masrur, Muhamad Muchamad Gozali Muji Setiyo Mustasyar Perkasa Mutharuddin, Mutharuddin Novi Irawati Nugroho, Sinung Nuramin, Makmuri Nurdam, Nofriyadi Ogi Ivano Perkasa, Mustasyar Pramana, Nurhadi Purnomo, Hedi Rachmanu, Fatkur Ramli Junid Romahadi, Dedik Rosyidi, M. Subaryata, Subaryata Thirunavukkarasu Raja Tri Widodo Tuessi Ari Purnomo Turmudi, Agung Wahyu Sulistiyo Waskito, Dwitya Harits Wina Libyawati, Wina Winarno Winarno Yudi Irawadi

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