Digital twin is one of the approaches to transform classical and traditional manufacturing to smart manufacturing and Industry Revolution 4.0, which received much attention in the manufacturing domain. Digital twin in manufacturing offers many advantages and solutions include close-loop feedback system, continuous real-time monitoring, or data acquisition through establishing a cyber-physical integration. The low cost and flexibility implementation of Programmable Logic Controller (PLC) enable the technology to become one of the most versatile tools and essential equipment for the automation industry today. However, PLC itself may not be suitable for real-time monitoring and control due to the characteristic of ladder diagram and normally it requires to work with extra devices to achieve the mentioned performance. The main objective of this paper is to develop a Graphic User Interface (GUI) as the cyber entity for real-monitoring and controlling towards the manufacturing process. The interaction between physical sensors, ladder diagram and GUI established cyber-physical integration system and provide engineers a real-time friendly approach to monitor and control the production. The ladder diagram and GUI control system are established by using CX-ONE (CX-Programmer and CX-Designer) together with OMRON PLC CP1L-E model training kits to prototyping the manufacturing cell. The result showed that the GUI allow operator to monitor as well as to control the real-time production.

This paper reports on the design and engineering analysis of a low-cost coconut peeling machine for small medium-sized enterprise. The aim of the design was to develop a machine that could remove the exterior coconut husk in a hexagonal form. The concept adopted was based on vertical trimmings and the mechanism composed of blades, and holder to clamp the young coconut. By using finite element analysis, the viability of the proposed design for the coconut peeler machine and its mechanical systems were examined. The effect of loading and the rupture force on the holders were also analysed. In addition, the cost estimation was calculated and compared with the market price. Finally, the optimized body frame was examined and optimization was done to minimize the weight of the overall structure without compromising on cost and quality.

In this work, the design and optimisation of an upright assembly for a passenger vehicle were investigated by performing static analysis. Upright assembly is a critical mounting part of a suspension system because it connects all suspension, steering and braking components together to the chassis. Reduction in weight of this component translates to improved performance. The objective of this research was to optimise the upright to reduce the component’s unsprung mass. Solidwork was used to create the model and the static analysis was then performed using the Ansys software, with mild steel as the initial material and the constraints as well as loads were calculated for various scenarios such as braking and cornering maneuvers. The design was optimized by topology optimisation method which considered the areas with low induced stress for material removal. The geometry was then redesigned based on the resulting topology and static analysis was repeated with using different aluminium alloys. The weight of the component was successfully reduced by 50% after the topology optimisation. The use of aluminium, which has a low material density than mild steel, allowed a further weight reduction of 30%. It was also revealed that in comparison to mild steel, the strength was retained with a 15% increase in factor of safety when using aluminium 7075-T6 alloy.

Authentication is most important for security. There are many different systems for recognizing the person. The traditional authentication systems such as passwords have drawbacks. It is easy to be stolen. Biometric authentication systems provide the best security. However, a current technique that widely used for identification which is fingerprint has its own disadvantages. Furthermore, current techniques such as facial recognition, iris recognition and voice recognition that used to recognize person still compromise the security walls. In this recent years, electroencephalograph (EEG) signal has been discovered that it has the potential to become one of the biometric authentication systems. It is brain activities for a human. It is unique due to the EEG signal is different from person to person. In this paper, power spectral density analysis was used to analyse the electroencephalography (EEG) signal. K-nearest neighbor classifier was used for classification in this paper. The accuracy results of alpha (8-13 Hz), beta (13-30 Hz), combined alpha and beta (8-30 Hz) and combined theta, alpha, beta and gamma (4-40 Hz) frequency bands were compared. Overall, the percentage of accuracy was above 80%. The most suitable frequency bands for human EEG-based biometric identification in this experiment was the combined theta, alpha, beta, and gamma (4-40 Hz). The percentage of accuracy for this frequency band was the highest among the others which is 89.21%.