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All Journal JMES The International Journal of Mechanical Engineering and Sciences
Arif Wahjudi
Institut Teknologi Sepuluh Nopember
Energy Materials Science & Nanotechnology Mechanical Engineering

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Automated Corrosion Detection on Steel Structures Using Convolutional Neural Network Mohammad Khoirul Effendi; Bara Atmaja; Arif Wahjudi; Dedi Budi Purwanto
JMES: The International Journal of Mechanical Engineering and Sciences Vol 7 No 1 (2023)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v7i1.15881

Abstract

Steel is a material that is widely used in industry and construction. The tensile and compressive force of steel is relatively high compared to other materials. On the opposite, low corrosion resistance is the main weakness of steel, which can encourage steel deterioration and fatal accidents for the user. Furthermore, regular visual inspection by a human should be performed to prevent catastrophic incidents. However, human visual inspection increases the risk of work accidents and reduces work effectiveness. Therefore, a drone with a camera is one solution to increase efficiency, increase security levels, and minimize difficulties or risks during corrosion inspection. In this research, the drone has been used to capture corroded video of a construction structure. The convolutional neural network (CNN) method is then used to detect the location of the corroded images. This study has been conducted on Surabaya’s Petekan-bridge with the Mobilenet V1 SSD pre-training model. In this study, the distance between a drone and the detected object varied between 1 and 2 m. Next, the drone speed was varied into 0.6 m/s, 0.9m/s, and 1.3m/s. As a result, CNN can detect corrosion on the surface of steel materials with the best accuracy is 84.66% and minimum total loss value of 1.673 by applying 200 images, 200000 epochs, batch size at 4, learning rate at 0.001 and 0.1, the distance at 1 m, drone speed at 0.6 m/s.
Optimization of 3D Printing Parameter Process for Product Tensile Strength from PLA Materials Using the Taguchi Method I Made Londen Batan; Arleta Listiyana Chandradewi; Arif Wahjudi; Dinny Harnany
JMES: The International Journal of Mechanical Engineering and Sciences Vol 7 No 2 (2023)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v7i2.16985

Abstract

Three-dimensional printing or 3D Printing is one of the revolutionary machines in addictive manufacturing techniques to create three-dimensional objects with complex structures. Until now there are many techniques in 3D printing, one of which is Fused Deposition Modeling (FDM), which is currently widely used because of its ease and low operational costs. However, in the printing process, there are important things that must receive attention, namely the process parameters. Because this is what really determines the quality of the printout. In this research, an analysis of the effect of process parameters such as: infill rate, infill pattern, extrusion temperature and layer thickness were carried out on the tensile strength of the printed product. The method used is the Taguchi method with the Orthogonal Array L 9 (3 4) experimental design. Three tensile test specimens were printed for each variation using a Cubic Chiron 3D printer, so a total of 27 specimens were printed. All specimens were tensile tested according to ASTM D638 standard, the results were analysed based on the average value and signal to ratio (SNR) value and their significance by analysis of variance (ANOVA). The results of the analysis show that the infill rate, infill pattern and layer thickness have a significant effect on the tensile strength of the printing results. The optimal value of the tensile strength is 56,876 MPa, occurs in the concentric pattern with an infill rate of 90%, and a layer thickness of 0.2 mm. From the confirmation test, the confidence interval values were obtained from 55,477 MPa to 58,275 MPa, meaning that the optimal predictive value was not significantly different from the confirmation test value.
Numerical Study of Blended Winglet Geometry Variations on Unmanned Aerial Vehicle Aerodynamic Performance Fungky Dyan Pertiwi; Arif Wahjudi
JMES: The International Journal of Mechanical Engineering and Sciences Vol 6 No 1 (2022)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v6i1.12317

Abstract

An unmanned aerial vehicle (UAV) is an unmanned aircraft that can be controlled remotely or flown automatically. Nowadays, the use of UAVs is extensive, not only limited to the military field but also in civilian tasks such as humanitarian search and rescue (SAR) tasks, railroad inspections, and environmental damage inspections. Therefore, study on UAV becomes essential to answer the challenges of its increasingly widespread use. This study explores the addition of a blended winglet on the swept-back wing of the UAV. It is to predict the effect of the aerodynamic performance. The backpropagation neural network (BPNN) method helps to predict the aerodynamic performance of the UAV in the form of a lift-drag coefficient ratio (CL/CD) and drag coefficient at 0O angle of attack (CD0). It is based on blended winglet parameters such as height, tip chord, and cant angle. The obtained BPNN modeling has a network architecture of 3 inputs, 2 hidden layers, and 1 output with a mean square error (MSE) of 4.9462e-08 and 4.4756e-06 for the relationships between blended winglet parameters with CL/CD and CD0, respectively.
The Effect of Rice Husk as Additive in Injection Molding Process Dinny Harnany; I Made Londen Batan; Arif Wahjudi; Sylvia Ayu Pradanawati
JMES: The International Journal of Mechanical Engineering and Sciences Vol 6 No 2 (2022)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v6i2.14182

Abstract

This study investigated the moldability and the mechanical properties of bio-composite with rice husk as natural reinforcement. Natural materials that are abundant in nature can be used as reinforcement for polymer materials. Natural materials as reinforcement in plastic materials were used to obtain alternative materials in an injection molding process. With rice husk, polypropylene, and MAPP, four compositions of bio-composite materials were made and used as raw material injection molding process. The moldability from this material was observed through visualization of the product. The mechanical properties of the materials were observed by the tensile strength and impact test on the injection molding product. The result showed that these materials could be injected to form ASTM D638-03 Type V tensile test and ASTM D256-04 impact test specimens. Visually, the more rice husk on the bio-composite material, the darker the product color. The differences in tensile strength values decreased along with increased rice husk content. All bio-composite materials had roughly the same tensile strength value and were lower than polypropylene, except RH-5%. The impact value of bio-composites was lower than polypropylene impact value and tended to decline along with the increase in the rice husk content. Scanning electron microscope (SEM) analyzes were done on the fracture side of the impact specimen. Microscale voids decreased and were rarely found by adding rice husk to the material bio-composite. On the other hand, rice husk breakage and pullout phenomenon on bio-composite material were found.
Determination of Injection Molding Process Parameters using Combination of Backpropagation Neural Network and Genetic Algorithm Optimization Method Arif Wahjudi; Thenny Daus Salamoni; I Made Londen Batan; Dinny Harnany
JMES: The International Journal of Mechanical Engineering and Sciences Vol 5 No 2 (2021)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25807471.v5i2.8592

Abstract

Co-Authors Arleta Listiyana Chandradewi Bara Atmaja Dedi Budi Purwanto Dinny Harnany Fungky Dyan Pertiwi I Made Londen Batan Mohammad Khoirul Effendi Sylvia Ayu Pradanawati Thenny Daus Salamoni