Nor Anija Jalaludin

Manufacturing industries often face challenges in optimizing machine performance due to the limitations of traditional downtime monitoring methods, which are time-consuming and prone to errors. The lack of real-time capabilities in these methods leads to delayed identification and resolution of machine issues, ultimately affecting productivity. This research aims to develop a real-time machine downtime monitoring system that leverages sensors, vision systems, and LabVIEW software to enhance the detection and analysis of production line performance. The system uses image processing techniques for product quality assessment, enabling the detection of good and defective products, and integrates vibration sensors to monitor equipment conditions. The Arduino microcontroller is employed to manage sensor data and motor functions, while LabVIEW software facilitates real-time visualization and data analysis. The system demonstrated high accuracy in detecting both product defects and equipment vibrations, although sensitivity to lighting conditions and low-powered motors presents areas for future improvement. The integration of this system into production lines has the potential to significantly reduce downtime and improve operational efficiency, contributing to more automated and reliable industrial processes.

This study proposes a Centralized IoT-based Process Cycle Time Monitoring System (CTMS) to improve the efficiency and accuracy of line balancing studies in a production line with a conveyor system. The traditional approach of measuring cycle time, typically done by manual observation and recording, can be prone to errors due to human factors such as fatigue or inaccuracies in manual recording. The proposed CTMS utilizes a combination of cutting-edge technologies such as a cloud database, android application, LABVIEW GUI, analogue infrared (IR) sensors, and appropriate controllers to improve the reliability of cycle time measurements. The system continuously monitors the production line and provides real-time data on cycle time, downtime, and other relevant metrics, allowing production engineers to quickly identify bottlenecks and areas for improvement. The results of the study demonstrate the potential of the CTMS to be applied and expanded to actual production lines with conveyor systems, providing a valuable tool for production engineers to develop strategies to reduce cycle time and ensure it is kept in check. The proposed CTMS is expected to have a positive impact on the efficiency and profitability of the production line by reducing downtime and increasing productivity.

This paper proposes a system for efficient object recognition based on colour and shape features using a webcam and LabVIEW. The study aims to develop a model scale conveyor belt system for Lego brick sorting based on colour and shape. The proposed system uses the webcam and LabVIEW's graphical programming environment to capture and analyse images, extract colour and shape features, and perform object recognition. An Arduino microcontroller, integrated with LabVIEW software, is used to move the servo and motor of the conveyor belt sorting system based on the analysed image capture from the webcam. The proposed system has the potential to be applied in real-world applications such as the food industry, particularly in fruit and vegetable sorting which will help reduce the amount of time and labour needed for manual sorting and food waste by ensuring that only good quality produce is sold. Another potential real-world application for the proposed system is quality control and defect detection in the manufacturing industry. The system proposed in this paper can sort objects using object recognition based on the object's colour and shape features, with the overall system average reliability percentage is 90%. Overall, the system is applicable in real-world applications if the limitations mentioned are overcome and improved by integrating with the Internet of Things (IOT) for a long-range monitoring system.

Human fingers have a specific role that contributes to different hand functions. Among these fingers, the thumb plays the most special function as an anchor to many hand activities. As a result, the loss of the thumb due to traumatic accidents can be catastrophic as proper hand function will be severely limited. In order to solve this problem, a prosthetic thumb is developed to be worn in complementing the function of the rest of the fingers. The movement of the prosthetic device can be naturally controlled by using electromyogram (EMG) signals. In this work, the EMG signals from the human muscles were measured in different thumb configurations and thumb-tip forces in flexion movement. The muscles involved are the Adductor Pollicis (AP), Flexor Pollicis Brevis (FPB), Abductor Pollicis Brevis (APB) and First Dorsal Interosseous (FDI). The classification of the EMG signals based on different force and thumb configurations is performed using an Artificial Neural Network (ANN). From a series of experiments, the results show that the neural network efficiently classified the signals and a unique set of EMG signals was generated for each thumb movement and force. Therefore, EMG signals were used to control the prosthetic movement with aid from the developed neural network.