Enhancing yield and process efficiency through dual internet of things and augmented reality for AI-driven human-machine interaction in centering mass production

Abstract

With the emergence of smart manufacturing, artificial intelligence (AI) has become a pivotal technology for enhancing industrial process efficiency and production yield. By integrating data analysis methods, AI can effectively capture process characteristics during manufacturing. However, tasks such as setting machine parameters still rely heavily on human expertise. This study focused on the centering process of optical glass lenses as a case study. To minimize dependence on human expertise, establish real-time diagnostic mechanisms, and shorten calibration times, an intelligent human-machine interactive manufacturing system featuring a Dual Internet of Things (Dual-IoT) architecture and augmented reality (AR) technology was developed. This system employs a feature extraction model that combines root mean square (RMS) with exponentially weighted moving average (EWMA) to analyze time-series signals during processing. Subsequently, an echo state network (ESN) prediction model was established to accurately forecast real-time signals and identify anomalies. In this setup, the control system and AI model are interconnected through a Dual-IoT architecture, enabling real-time data transmission to the intelligent AR-based human-machine interaction system and remote monitoring interface. This setup enables the visualization of process diagnostics and decision-making, providing feedback to the centering machine through remote control mechanisms. According to the verification results, at target specifications of < 0.01 mm roundness and <E0.5 edge cracks, the proposed system enhanced production yield from 64 % to 94 % while reducing production time by 29.2 %. These results confirm the system’s effectiveness in augmenting industrial production processes.