DTA-QC: An AI-Driven Framework for Adaptive Quality Control and Intelligent Test Optimization in 5G Manufacturing

In modern 5G radio manufacturing, traditional quality control methods based on black fixed thresholds are increasingly inadequate, often failing to capture nuanced fault patterns and requiring substantial manual intervention. This study presents DTA-QC, an AI-driven framework for adaptive thresholding and intelligent test optimization in 5G production environments. The proposed system introduces three core innovations: (1) dynamic thresholding using LSTM autoencoders and regression models to detect anomalies under evolving production conditions, (2) supervised fault classification via convolutional neural networks trained on time-windowed sensor data, and (3) a four-level severity classification system (Normal, Warning, Worse, Stop) to support real-time decision-making in manufacturing environments. DTA-QC is implemented and validated on Ericsson AB’s 5G radio production line, achieving high anomaly detection accuracy (ROC-AUC: 0.89-0.94) and significantly reducing manual review efforts, without requiring specialized hardware. To assess generalizability, DTA-QC is further evaluated on a public benchmark dataset. A comparative analysis of three architectural variants revealed trade-offs in complexity, latency, and deployment feasibility. These results underscore the value of embedding AI-driven analytics in industrial test workflows, contributing to the broader goals of intelligent manufacturing and adaptive, data-driven quality assurance.