Integrated energy optimization for metal waste cleaning-24 robot in local manufacturing based on multi-objective approach

Abstract

Modern manufacturing industries face increasing pressure to enhance operational efficiency while reducing energy costs and environmental impact. This research develops a metal waste cleaning robot with integrated multi-objective energy optimization for local manufacturing applications. The robot integrates 28 main components including dual motor systems (80 W drive motor, 60 W arm motor), HC-SR04 ultrasonic sensor, ESP32 microcontroller, and hierarchical thermal protection. Non-dominated Sorting Genetic Algorithm II (NSGA-II) simultaneously optimizes energy consumption, coverage completeness, and operational time. The multi-objective optimization framework achieves significant energy reductions through three independent mechanisms: trajectory planning optimization reduces total energy consumption by 30% (from 235.7 Wh to 165 Wh per cycle), adaptive control systems reduce motor power consumption by 50% (from 280 W to 140 W) through dynamic voltage adjustment based on environmental complexity, and strategic base station placement reduces travel distance by 20% (from 150 m to 120 m per cycle), resulting in corresponding energy savings. ANSYS validation confirms structural stability with maximum equivalent elastic strain of 7.6839 × 10 −5 m/m and maximum equivalent deformation of 6.710 × 10 −5 m (67.10 μm) under operational loading, demonstrating that the structure operates well within the elastic limit with safety factor >5. The robot demonstrates total power consumption of 165 W with 75.4% cleaning efficiency, reducing operational time from 35 min (manual methods) to 8.4 min across four material types (aluminum, copper, steel, glass). Performance testing shows 76.7% efficiency for chip cleaning (7 min) and 87.5% efficiency for metal dust cleaning (5 min). The hierarchical thermal protection system ensures operational safety with motor temperature sensors providing 35% protection effectiveness. This integrated optimization framework provides validated solutions for local manufacturing industries with limited technology accessibility, contributing to sustainable energy-efficient industrial robot for metal waste management in developing countries.

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