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Afianto
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Control System for Swerve Drive Wheeled Automated Guided Vehicle with Ultra-Wide Band Local Positioning System Arifianto, Mada Jimmy Fonda; Afianto; Suhendra
Indonesian Journal of Engineering Research Vol 4 No 1 (2023): Indonesian Journal of Engineering Research
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/ijer.v4i1.45

Abstract

Control system for Automates Guided Vehicle (AGV) is used to control the motor movement so that the AGV can go to a position in certain speed and orientation. Discussion in this paper is about how to control 4-wheel swerve drive-based AGV. Mechanical structure of each wheel is built in such a way that the wheel can move forward / backward and the orientation can be adjusted while moving. Each wheel requires two BLDC motors to drive and to steer the AGV. In this project, the power each motor is 200W which is drove and controlled by Syntron LS20530G motor driver that connected to a computer via CAN-bus. An Ultra-Wide Band (UWB)-based Local Positioning System (LPS) is used to monitor the position of the AGV as well as a guide for non-physical paths so that AGV moves according to a given path virtually. The experimental results show that AGV can move in a straight line with a speed of 50m/min. Wheels can also be steered according to a certain angle so that the AGV can move towards the destination point. The steering angle of each wheel is between -135 degrees to 135 degrees.
Application of Deep Learning YOLO in IoT System for Personal Protective Equipment Detection Nugroho, Waluyo; Rifdah Zahabiyah; Afianto; Mada Jimmy Fonda Arifianto
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 8 No 2 (2024)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v8i2.2187

Abstract

The use of Personal Protective Equipment (PPE) is a critical step in ensuring worker safety in various sectors, including industry, construction, and health. However, violations in using PPE often occur, which can increase the risk of work accidents. This study aims to develop a deep learning-based PPE detection system using the YOLOv8 algorithm. This method was chosen because of its superior ability to detect objects in real time with high accuracy. The training data consists of various images of workers in different work environments, label to recognize types of PPE such as helmets, masks, and safety vests. The developed system was tested on a test dataset to evaluate model performance based on metrics such as confusion matrix, inference speed, and detection error rate. The experimental results show that the YOLOv8 model can detect PPE with an accuracy level of up to 95%. The implementation of this system is expected to be an effective solution in increasing compliance with the use of PPE and preventing work accidents.
Design and Implementation of an IoT-Enabled Deep Learning Vision System for Automated Dimensional Measurement in Smart Manufacturing Nugroho, Waluyo; Afianto; Agus Ponco
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 9 No 2 (2025): (In Press)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v9i2.2855

Abstract

The rapid advancement of Industry 4.0 has brought the convergence of Internet of Things (IoT), computer vision, and deep learning to enhance automation and precision in manufacturing. This paper presents the design and implementation of an IoT-enabled deep learning vision system for automated dimensional measurement, integrated with programmable logic controller (PLC) control and real-time monitoring. The system employs a Raspberry Pi 5 as an edge computing unit, Logitech C270 camera for visual data acquisition, and an Omron CP2E PLC for process control. A YOLOv5 deep learning model is trained to detect and measure object dimensions with sub-millimeter accuracy. The Node-RED platform is utilized for dashboard visualization and communication, interfaced through Omron FINS protocol, with MySQL as the database for data logging. Experimental results show a high detection accuracy of 98.6% and an average measurement error of less than 0.5 mm, demonstrating the system’s effectiveness for smart manufacturing applications.
Development of Digital Twin Modeling for Smart Factory using OpenPLC-Based Control System Arifianto, Mada Jimmy; Afianto; Waluyo Nugroho
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 9 No 2 (2025): (In Press)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v9i2.2856

Abstract

This research discusses how to design and develop Digital Twin modeling in a Smart Factory system with an OpenPLC-based control system. The main problems in conventional manufacturing systems are limited production line flexibility, difficulty in monitoring and controlling complex devices, and process visualization that is limited to technical data without a real depiction. Through the design of a laboratory-scale mechanical system, the creation of a control board based on an STM32H7 microcontroller with an OpenPLC platform, and the development of a Digital Twin application using Three.js and Node.js, this research produces a Smart Factory prototype that can be visualized in three dimensions and operated in real time via a local network or the internet. Test results show that the system is able to display sensor conditions, control actuators, and accelerate decision-making with shorter response times. The implementation of this system supports production process efficiency and offers an alternative industrial automation solution that is economical, flexible, and more intuitive in the implementation of digital transformation.
Co-Authors Agus Ponco Arifianto, Mada Jimmy Mada Jimmy Fonda Arifianto Nugroho, Waluyo Rifdah Zahabiyah Suhendra