Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ComTech: Computer, Mathematics and Engineering Applications Jurnal Polimesin
Mochamad Edoward Ramadhan
Universitas Jember
Automotive Engineering Computer Science & IT Control & Systems Engineering Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Heat Treatment and Its Effect on Tensile Strength of Fused Deposition Modeling 3D-Printed Titanium-Polylactic Acid (PLA) Darsin, Mahros; Susanti, Rizqa Putri; Sumarji, Sumarji; Ramadhan, Mochamad Edoward; Sidartawan, Robertus; Yudistiro, Danang; Basuki, Hari Arbiantara; Wibowo, Robertoes Koekoeh Koentjoro; Djumhariyanto, Dwi
ComTech: Computer, Mathematics and Engineering Applications Vol. 15 No. 2 (2024): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v15i2.11255

Abstract

Titanium is a biocompatible metal commonly applied in biomedical fields such as bone and dental implants. Recently, the produced titanium-Polylactic Acid (PLA) filament for 3D printing Fused Deposition Modeling (FDM) technique is easier to operate and affordable. This filament contains less than 20% PLA, which is also biocompatible but hydrophobic and capable of producing inflammation of the surrounding artificial living tissue. Therefore, a heat treatment is needed to reduce or even eliminate PLA. The research aimed to optimize the mechanical properties and biocompatibility of titanium-PLA filaments through heat treatment, demonstrating significant advancements in 3D printing applications for biocompatible materials. A Thermogravimetric Analysis (TGA) was carried out to find out the right temperature for reducing PLA levels. Specimens were heat treated with four temperatures at 100oC, 160oC, 190oC, and 543oC, and two holding times of 60 and 120 minutes. The mass of the specimens was weighed before and after heat treatment to determine the mass reduction and tested for tensile, micrograph, and fractography observation. The result is a meagre mass reduction. The highest tensile strength of the heat-treated specimen with a heat treatment temperature of 160oC and a holding time of 60 minutes is 18.310 MPa. However, it is still below the strength of the non-heat treated specimen, 19.890 MPa. Specimens with low tensile strength have a microstructure that shows an uneven distribution of titanium particles. Last, fractography shows porosity in the specimens with the lowest tensile strength.
Myoelectric grip force prediction using deep learning for hand robot Anam, Khairul; Ardhiansyah, Dheny Dwi; Hana Sasono, Muchamad Arif; Nanda Imron, Arizal Mujibtamala; Rizal, Naufal Ainur; Ramadhan, Mochamad Edoward; Muttaqin, Aris Zainul; Castellini, Claudio; Sumardi, Sumardi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3228-3240

Abstract

Artificial intelligence (AI) has been widely applied in the medical world. One such application is a hand-driven robot based on user intention prediction. The purpose of this research is to control the grip strength of a robot based on the user’s intention by predicting the grip strength of the user using deep learning and electromyographic signals. The grip strength of the target hand is obtained from a handgrip dynamometer paired with electromyographic signals as training data. We evaluated a convolutional neural network (CNN) with two different architectures. The input to CNN was the root mean square (RMS) and mean absolute value (MAV). The grip strength of the hand dynamometer was used as a reference value for a low-level controller for the robotic hand. The experimental results show that CNN succeeded in predicting hand grip strength and controlling grip strength with a root mean square error (RMSE) of 2.35 N using the RMS feature. A comparison with a state-of-the-art regression method also shows that a CNN can better predict the grip strength.
Characterization of FDM 3D Printed Parts Using TPU + PETG Filaments For Shin Guard Products Darsin, Mahros; Yulio, Agit Yoga; Syuhri, Ahmad; Ramadhan, Mochamad Edoward; W.C.S, I Made Ivan; Sumarji, Sumarji
Jurnal Polimesin Vol 22, No 1 (2024): February
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v22i1.4122

Abstract

3D printing machines are used to print products that support sports activities, such as shin guards. During sports, shin guards are protective equipment to prevent injury to the lower legs. Filaments that are suitable for making shin guards are thermoplastic polyurethane (TPU) and polyethylene terephthalate (PETG) because they have impact resistance properties needed to protect the feet during sports. The variation is the level parameter layer height, nozzle temperature, printing speed, and bed temperature. Next, an impact test will be carried out to determine the optimal parameter variation on the 3D printing machine, which is expected to be a reference for printing quality products. This study uses a 3D printer, Ender v3, to print specimens and shin guard products. The material used is TPU+PETG filament. The Taguchi method with the orthogonal matrix L9(3)4 was repeated thrice for each experiment. After that, an analysis of variance was carried out. Parameter variations used in the study were layer height (0.1 mm, 0.2 mm, 0.3 mm), nozzle temperature (220℃, 225℃, 230℃), printing speed (45mm/s, 45mm/s, 50mm/s) and bed temperature. (70℃, 75℃, 80℃). In this study, Charpy impact testing will be carried out. The combination of factors that can produce an optimal impact test is layer height level 2 (0.2 mm), nozzle temperature level 1 (220℃), printing speed level 3 (50 mm/s) and bed temperature level 2 (75℃) with an impact strength value the highest was 27.20 and the lowest was 11.07. The combination of factors that have the most significant effect on the impact test strength values is layer height 63.97%, nozzle temperature 6.19%, printing speed 2.07% and bed temperature 4.74%.
Co-Authors Ahmad Syuhri Anam, M. Khairul Ardhiansyah, Dheny Dwi Aris Zainul Muttaqin Castellini, Claudio Danang Yudistiro Dwi Djumhariyanto Hana Sasono, Muchamad Arif Hari Arbiantara Basuki Mahros Darsin Nanda Imron, Arizal Mujibtamala Rizal, Naufal Ainur Robertus Sidartawan Sumardi . Sumarji Susanti, Rizqa Putri W.C.S, I Made Ivan Wibowo, Robertoes Koekoeh Koentjoro Yulio, Agit Yoga