Due to the complexity of the automobile manufacturing process, some flexible and delicate assembly work relies on manual operations. However, high-frequency and high-load repetitive operations make assembly workers prone to physical fatigue. This study proposes a method for evaluating human physical fatigue for the manual assembly of automobiles with methods: NIOSH (National Institute for Occupational Safety and Health), OWAS (Ovako Working Posture Analysis System) and RULA (Rapid Upper Limb Assessment). The cerebral oxygenation signal is selected as an objective physiological index reflecting the human fatigue level to verify the proposed physical fatigue evaluation method. Taking auto seat assembly and automobile manual assembly as an example, 18 group experiments were carried out with the ARE platform (Augmented Reality-based Ergonomic Platform). Furthermore, predictions of metabolic energy expenditure were performed for experiments in Tecnomatix Jack. Finally, it is concluded that the proposed physical fatigue evaluation method can reflect the human physical fatigue level and is more accurate than the evaluation of metabolic energy consumption in Tecnomatix Jack because of the immersion that comes with the AR devices and the precision that comes with motion capture devices.

Industrial knowledge management (KM) remains highly fragmented, with knowledge capture, structuring, and application often treated as isolated activities. Conventional approaches depend on static documentation and experience-based practices, which limit their responsiveness in dynamic, operational environments. In this paper, we design a comprehensive framework for the integration of artificial intelligence (AI) and augmented reality (AR) in industrial maintenance and diagnostics. The framework leverages AI techniques, including natural language processing (NLP) for extracting and structuring domain knowledge and machine learning (ML) models for predictive fault classification. These AI capabilities are seamlessly combined with AR technologies to deliver immersive, context-aware, in-situ task guidance, thereby enhancing decision-making, reducing downtime, and supporting efficient, knowledge-based maintenance processes. A case study is employed to demonstrate the framework’s feasibility by developing a prototype system deployed in a maintenance setting. The AI module clusters maintenance actions and predicts task categories, while the AR component renders step-by-step maintenance instructions anchored in the physical workspace. This integration enables the transition from unstructured, digital maintenance records to step-by-step in-situ repair guidance, reducing reliance on expert memory or static manuals. The results show that combining AI-driven text analytics with AR-based visualisation creates a cohesive knowledge workflow that improves operational efficiency. The proposed framework offers a scalable approach to embedding KM into frontline industrial routines, laying the foundation for more adaptive, technician-centred knowledge systems.