Autonomous Quality Control Using Edge Artificial Intelligence and Cloud Orchestration in Smart Manufacturing Environments

Manufacturing industries are embracing smart solutions to achieve operational excellence by enhancing controllability, visibility, and flexibility.An intelligent quality control system with autonomous capabilities is a critical enabler of smart manufacturing.The autonomous quality control mechanism integrates edge artificial intelligence and cloud orchestration-AI-based applications for real-time anomaly detection and predictive analysis, AI-enabled cloud services for system orchestration, and edge-cloud data governance.The effectiveness of the proposed approach is demonstrated in a case study involving a complex process using Internet-of-Things devices for data acquisition.Understanding the complex, repetitive, and noisy nature of manufacturing processes with advanced machine-learning algorithms often requires substantial data-analytics infrastructure.Moving all data to the cloud for processing and storing is not feasible for operational efficiency when the core functions are repetitive, time-sensitive, and performance-critical. Solutions deployed on edge devices provide limited performance and efficiency due to challenged computing resources.An edge-cloud quality-control framework with real-time anomaly detection and predictive analytics capabilities is proposed.AI-based applications with active learning on the edge provide constant real-time services to detect data anomalies in quality features from quality-control check points.AI-enabled cloud services orchestrate the entire system by continuously monitoring operational conditions and storing all data, validating the use of predictive-quality-control analysis.