DESIGN AND DEVELOPMENT OF SORTING SCREW MACHINE FOR TOOLING WORKSHOP

Abstract

This project is to design and development of sorting screw machine for tooling workshop of Company XY Sdn. Bhd. Company XY Sdn. Bhd is a global leader in engineered materials and optoelectronic component. Efficient screw sorting is essential in tooling workshops, where a wide range of fasteners must be prepared quickly for machining and assembly. Manual sorting is slow, labour-intensive, and prone to error, which reduces overall productivity. This study presents the design and development of a sorting screw machine for tooling workshops to improve efficiency through simple, low-cost automation. The machine integrates a screw feeder, sorting mechanism, and channeling unit equipped with sensors to classify screws by size and type. The design was developed using computer-aided drafting (CAD) and subsequently fabricated into a working prototype. Experimental tests were conducted to measure screw detection, sorting speed, and accuracy across repeated cycles. Data were collected on sorting time, error rate, and throughput to evaluate machine performance. The prototype machine demonstrated significant improvements compared to manual methods. Sorting time was reduced, achieving an average efficiency gain of more than 35%. Accuracy remained stable, with errors within ±2% across multiple trials. Error analysis confirmed consistent performance and reliable operation of the system. Preliminary cost-benefit analysis suggests that the use of this machine can reduce dependence on manual labour, lower operational costs, and improve overall workflow in screw handling. From an industrial perspective, the machine offers a practical solution to enhance productivity and minimize inefficiencies in tooling workshops. From a research perspective, it contributes to the literature on low-cost automation and machine design, demonstrating the feasibility of automated screw sorting for small- to medium-scale applications. In conclusion, the developed sorting screw machine improves speed, accuracy, and efficiency compared to manual methods, offering benefits for both industrial practice and further academic investigation.