Online Measurement and Detection Technology and its Optimized Application in Intelligent Manufacturing Environment

In the intelligent manufacturing environment, online metrology and detection technology ensures the consistency and stability of product quality by real-time monitoring and analysis of key data in the production process. However, traditional online detection methods face challenges such as insufficient accuracy, delayed response and high cost, which affect production efficiency and quality control. To solve these problems, this paper proposes an optimization scheme based on multi-sensor fusion, machine learning algorithm, adaptive control, intelligent fault prediction and edge computing technology. First, multiple sensors are used for data collection, integrating multimodal information such as vision, laser, and ultrasound to improve measurement accuracy. Second, the detection model is optimized through machine learning algorithms, and adaptive control technology dynamically adjusts the production process according to real-time data. Intelligent fault prediction analyzes historical data to provide early warning of equipment problems, and edge computing improves the real-time and efficiency of data processing. Experimental results show that the surface defect detection accuracy of the optimized system is 98%, production efficiency is improved by about 60%, and the scrap rate is reduced by 4.85%. This research provides practical solutions for quality control and production optimization in intelligent manufacturing, significantly improves the intelligence and automation level of the production line, and promotes the development of the manufacturing industry in a direction of higher efficiency, lower cost, and higher precision.