Abstract
Automated Pick-and-Place (P&P) machines offer a new prospective for the assembly of Surface Mount Devices (SMDs) on Printed Circuit Boards (PCBs), owing to their advantages of time-effectiveness and precision. SMDs are widely used to improve production efficiency and reduce common defects. However, these machines are quite expensive and not easily available for the local market. This study is based on the design of a P&P machine model that incorporates principles of machine learning theory. The model has been developed in such a way that it can be introduced to the local market in a more cost-effective manner without compromising on its precision and accuracy.
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