In an environment of intense global competition, both creative and proven strategies need to be considered in order to bring about the effectiveness and efficiency in manufacturing operation. Total Productive Maintenance (TPM) is one of the effective maintenance strategy in enhancing the equipment effectiveness and to achieve a significant competitive advantage. This research paper addresses the impact of three TPM pillars namely planned maintenance (PM), autonomous maintenance (AM) and focused maintenance (FM) on overall equipment effectiveness (OEE) of die attach equipment in the production line of semiconductor industry. The effect of TPM on the OEE is also investigated depending on the equipment types, in where die attach process consist of two models-CANON and ESEC. The primary data was collected from an organization's database and was analysed by SPSS V23. The preliminary results of the analysis showed that the performance of OEE in ESEC is better than the CANON after the implementation of TPM. The analysis also showed that out of the three TPM practices deployed, planned maintenance of equipment by production and maintenance team played the biggest role in increasing the equipment effectiveness. In conclusion, this study provides insights the importance of implementing TPM in order to succeed in a highly demanding market arena.

This paper presents an intuitively straightforward yet comprehensive approach in developing and controlling a Mecanum-wheeled robot (MWR), with decent path tracking performance by using a simple controller as an end objective. The development starts by implementing two computer ball mice as sensors to realize a simple localization that is immune toward wheel slippage. Then, a linearization method by using open-loop step responses is carried out to linearize the actuations of the robot. Open-loop step response is handy, as it directly portrays the non-linearity of the system, thus achieving effective counteraction. Then, instead of creating a lookup table, polynomial regression is used to generate an equation in which the equation later represents an element of the linearizer. Next, a linear angle-to-gain (LA-G) method is introduced for path tracking control. The method is as easy as just linearly maps the summation of two angles-the angle between immediate and desired positions and the MWR's heading angle, into gains to control the wheels. Unlike the conventional control method which involves inverse kinematics, the LA-G method is directly a displacement-controlled approach and does not require the knowledge of parametric values, such as the robot's dimensions and wheel radius. Finally, all the methods are implemented, and the MWR experimentally demonstrates successfully tracking various paths, by merely using proportional controllers.

The addition of omnidirectional capability and modularization to conveyor systems is an exciting and trending topic in current conveyor research. The implementation of omnidirectional modular conveyors is foreseen as mandatory in the future of conveyor technologies due to their flexibility and efficiency. In this paper, an E-pattern omniwheeled cellular conveyor (EOCC) is first introduced. Camera and image processing techniques are utilized to achieve a centralized system, which is more robust than conventional decentralized systems. In order for cartons to maneuver across the EOCC, a unique method for activation of the actuators is subsequently designed. Next, the nominal characteristic of the EOCC is analyzed based on the step responses, which then inspire the proposal of four simple controllers, namely the P–P–P–P controller, tTS + P–P–P–P controller, P–P–PD–PD controller, and tTS + P–P–PD–PD controller (P for proportional, D for derivative and tTS for time-shifting), which are used to evaluate the tracking performance of the EOCC in diagonal-shaped, ∞-shaped (horizontal lemniscus), and 8-shaped (vertical lemniscus) trajectories. The results show that there is no clear winner among the controllers, with each having its own advantages and disadvantages. Nevertheless, such findings provide clearer insight into the EOCC, which is vital for future works. After all, the introduction of the EOCC system in this paper is also anticipated to elevate the benchmarks and competitiveness in the current field of modern conveyor technologies.

Problem statement: Southern Waste Management environment (SWM environment) is a company responsible for the collection and disposal of solid waste for the city of Johor Bahru, a city with over one million populations. The company is implementing an integrated solid waste management system where it involved in the optimization of resources to ensure the effectiveness of its services. Formulating this real life problem into vehicle routing problem with stochastic demand model and using some designed algorithms to minimize operation cost of solid waste management. Approach: The implementation of Ant Colony Optimization (ACO) for solving solid waste collection problem as a VRPSD model was described. A set of data modified from the well known 50 customers problems were used to find the route such that the expected traveling cost was minimized. The total cost was minimized by adopting a preventive restocking policy which was trading off the extra cost of returning to depot after a stock-out with the cost of returning depot for restocking before a stock-out actually occurs. For comparison purposes, Simulated Annealing (SA) was used to generate the solution under the same condition. Results: For the problem size with 12 customers with vehicle capacity 10 units, both algorithms obtained the same best cost which is 69.4358 units. But the percentage deviations of averages from the associated best cost are 0.1322 and 0.7064 for ACS and SA. The results indicated that for all demand ranges, proposed ACO algorithm showed better performance than SA algorithm. Conclusion: SA was able to obtain good solutions for small ranges especially small size of problem. For ACS, it is always provide good results for all tested ranges and problems sizes of the tested problem.

Wireless sensor network (WSN) consists of distributed nodes deployed for monitoring the physical conditions and organizing collected data at the central control unit. Power consumption is the challenges in WSN as the network consists of wireless sensor nodes becomes denser. By utilizing WSN and visible light technology, a simple health monitoring system design can be approached that are smaller in size, faster and lower power consumption. This work focuses on design a low power optical wake-up receiver to reduce the energy consumption of each node in WSN. A wake-up receiver is designed to be always-on for detecting incoming signal and switches on the stand by protocol controller and WSN network for data transmission process. The characteristic of optical transmission and functional circuit of a wake-up receiver has been investigated. A low power optical wake-up receiver has been designed in 180nm Silterra CMOS process technology. The proposed wake-up receiver consumes only 443pW in standby mode and 1.89nW in active mode. The proposed optical wake-up receiver drastically reduces the power consumption by more than one third compared to other wake-up receivers which could be a milestone in the medical field if successfully conducted.