AUTOMATION OF RESOURCE TESTING OF HOUSEHOLD APPLIANCES BASED ON A ROBOTIC COMPLEX WITH A MACHINE VISION SYSTEM

Abstract

The article is devoted to the problem of automating resource testing of household appliances under modern requirements for product quality and safety. Resource testing is considered a key stage in ensuring reliability, durability, and compliance with regulatory standards. Increasing market competition, rising consumer expectations, and stricter regulatory demands emphasize the need to improve testing methodologies. Traditional approaches, based on manual execution of procedures, are limited by low productivity, subjective evaluation, and insufficient monitoring capabilities. Within the framework of Industry 4.0, particular importance is attached to the implementation of robotic complexes with machine vision systems, capable of automating the testing process and providing objective, reproducible assessment of product condition. Modern methods of resource testing are analyzed and their limitations identified. A structure of an automated complex is proposed, which includes a six-axis Mitsubishi RV-2AJ manipulator with high positioning repeatability, a sensor system for monitoring mechanical and electrical parameters, and a computer vision subsystem for visual diagnostics. The robotic subsystem ensures repeatability of mechanical actions, control of applied loads, and synchronization with visual diagnostics, significantly increasing the objectivity of testing. The computer vision subsystem is based on an industrial high-resolution camera with hardware synchronization and controlled lighting. Images are tagged with metadata and transmitted for neural network analysis, which detects visual defects and classifies the condition of components. This integration enables correlation of functional degradation with visual signs of damage, providing deeper insight into failure mechanisms. The developed complex has been implemented as a prototype and experimental testing of household appliances has been conducted. The results confirm the effectiveness of the proposed approach: improved objectivity of control, reduced labor costs, accelerated testing cycles, and generation of comprehensive reports combining functional and visual indicators of degradation. The proposed approach can be applied in production laboratories of household appliance enterprises, certification centers, and educational institutions.