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All Journal ELKHA : Jurnal Teknik Elektro Mechatronics, Electrical Power, and Vehicular Technology
Rusman Rusyadi
Mechatronics Department, Faculty of Engineering, Swiss German University (SGU), EduTown BSDCity, Tangerang 15339
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering

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Machine Vision Implementation in Rapid PCB Prototyping Murijanto, Yosafat Surya; Rusyadi, Rusman; Sinaga, Maralo
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 2, No 2 (2011)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (452.807 KB) | DOI: 10.14203/j.mev.2011.v2.79-84

Abstract

Image processing, the heart of machine vision, has proven itself to be an essential part of the industries today. Its application has opened new doorways, making more concepts in manufacturing processes viable. This paper presents an application of machine vision in designing a module with the ability to extract drills and route coordinates from an un-mounted or mounted printed circuit board (PCB). The algorithm comprises pre-capturing processes, image segmentation and filtering, edge and contour detection, coordinate extraction, and G-code creation. OpenCV libraries and Qt IDE are the main tools used. Throughout some testing and experiments, it is concluded that the algorithm is able to deliver acceptable results. The drilling and routing coordinate extraction algorithm can extract in average 90% and 82% of the whole drills and routes available on the scanned PCB in a total processing time of less than 3 seconds. This is achievable through proper lighting condition, good PCB surface condition and good webcam quality. 
Monitoring Vibration of A Model of Rotating Machine Djajadi, Arko; Azavi, Arsi; Rusyadi, Rusman; Sinaga, Erikson
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 2, No 1 (2011)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (229.64 KB) | DOI: 10.14203/j.mev.2011.v2.51-56

Abstract

Mechanical movement or motion of a rotating machine normally causes additional vibration. A vibration sensing device must be added to constantly monitor vibration level of the system having a rotating machine, since the vibration frequency and amplitude cannot be measured quantitatively by only sight or touch. If the vibration signals from the machine have a lot of noise, there are possibilities that the rotating machine has defects that can lead to failure. In this experimental research project, a vibration structure is constructed in a scaled model to simulate vibration and to monitor system performance in term of vibration level in case of rotation with balanced and unbalanced condition. In this scaled model, the output signal of the vibration sensor is processed in a microcontroller and then transferred to a computer via a serial communication medium, and plotted on the screen with data plotter software developed using C language. The signal waveform of the vibration is displayed to allow further analysis of the vibration. Vibration level monitor can be set in the microcontroller to allow shutdown of the rotating machine in case of excessive vibration to protect the rotating machine from further damage. Experiment results show the agreement with theory that unbalance condition on a rotating machine can lead to larger vibration amplitude compared to balance condition. Adding and reducing the mass for balancing can be performed to obtain lower vibration level. 
Comparison and Analysis of Neural Solver Methods for Differential Equations in Physical Systems Sim, Fabio M; Budiarto, Eka; Rusyadi, Rusman
ELKHA : Jurnal Teknik Elektro Vol. 13 No. 2 October 2021
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/elkha.v13i2.49097

Abstract

Differential equations are ubiquitous in many fields of study, yet not all equations, whether ordinary or partial, can be solved analytically. Traditional numerical methods such as time-stepping schemes have been devised to approximate these solutions. With the advent of modern deep learning, neural networks have become a viable alternative to traditional numerical methods. By reformulating the problem as an optimisation task, neural networks can be trained in a semi-supervised learning fashion to approximate nonlinear solutions. In this paper, neural solvers are implemented in TensorFlow for a variety of differential equations, namely: linear and nonlinear ordinary differential equations of the first and second order; Poisson’s equation, the heat equation, and the inviscid Burgers’ equation. Different methods, such as the naive and ansatz formulations, are contrasted, and their overall performance is analysed. Experimental data is also used to validate the neural solutions on test cases, specifically: the spring-mass system and Gauss’s law for electric fields. The errors of the neural solvers against exact solutions are investigated and found to surpass traditional schemes in certain cases. Although neural solvers will not replace the computational speed offered by traditional schemes in the near future, they remain a feasible, easy-to-implement substitute when all else fails.
Co-Authors Arko Djajadi Arsi Azavi Eka Budiarto, Eka Erikson Sinaga Maralo Sinaga Sim, Fabio M Yosafat Surya Murijanto