Research on Optimization Technologies for the Operation of Mechanical Automation Equipment Based on PLC Control

To address issues such as response latency, high energy consumption, and increased failure-induced downtime in mechanical automation systems controlled by PLCs, this study conducts optimization research on the PLC control architecture for automotive component assembly lines. By developing a PID parameter optimization model based on genetic algorithms, implementing Modbus real-time data acquisition and feedback control strategies, and achieving hardware-software synergy optimization across PLC programming architecture, field hardware integration, and host computer monitoring, experimental results demonstrate significant improvements: the average equipment response time decreased from 1.2s to 0.5s; energy consumption ratio dropped from 35% to 28%; and monthly failure rate fell from 8% to 3%. During a 72-hour continuous operation test, the mean response time was 0.51s with a standard deviation of 0.032s, while the maximum PLC scanning cycle reached 9.8 ms, indicating excellent system stability and reliability. The study confirms that this optimization approach effectively enhances operational efficiency, energy efficiency, and fault tolerance in PLC-controlled mechanical automation systems.