DEVELOPMENT OF QUALITY CONTROL COBOTICS AND MECHATRONICS SYSTEM USING ARDUINO UNO AND RASPBERRY PI 5 WITH DBMS

Abstract

Technological innovations have significantly influenced industries, particularly in manufacturing. With increasing consumer demand for high-quality products, businesses continually seek methods to improve production processes and efficiency. Traditionally, quality control in manufacturing has been conducted manually, a process that is time-consuming and prone to human error. However, the emergence of technologies such as database management systems (DBMS), Arduino, Raspberry Pi, and collaborative robots (cobots) has enabled the creation of automated quality control systems that are more accurate and efficient. This study focuses on the development of a Quality Control System using Arduino Uno and Raspberry Pi 5 with DBMS, representing a substantial advancement in quality control technology. This study employs the Experimental Prototyping Method to ensure accuracy, consistency, and uniform testing. A Quality Control Cobotics and Mechatronics System is constructed using integrated hardware and software components. The system incorporates ESP8266 modules, Arduino Unos, Raspberry Pi, and wireless communication to facilitate data collection, remote control, and image processing for visual inspections. The developed cobotic system prototype utilized motors, microcontroller units, UPS batteries, and electrical regulators to operate servo motors and conveyor belt movements. MongoDB served as the database for data classification and real-time updates. The system achieved over 95% inspection accuracy with a 0.5-second response time. Despite these strengths, the results also revealed that camera-based inspections were highly sensitive to lighting conditions, and Arduino’s memory and processing limitations restricted the execution of complex computations. The researchers validated the theoretical framework through preliminary simulations and prototype experiments, confirming the feasibility of the proposed system. The findings supported the hypotheses, demonstrated successful assembly, and highlighted areas for further refinement. The integration of cobotics and mechatronics principles with Arduino Uno, Raspberry Pi 5, and DBMS presented a cost-effective, open-source solution for intelligent manufacturing. This hybrid system successfully bridged mechanical inspection with digital intelligence, offering industries a practical pathway to modernize quality control processes and reduce reliance on manual inspections.