In light of Industry 4.0, rapid analysis and optimization of manufacturing processes are emerging as a vital demand of smart manufacturing factories. Ergonomics is an essential aspect of the ongoing screening of working conditions and a fundamental variable in Industry 4.0, as it calls for a flexible manufacturing system to strengthen the competitiveness of factories in the global market. This paper proposes a new augmented reality-based ergonomic evaluation platform: Augmented Reality-based Ergonomic Platform (ARE Platform). Introducing the Augmented Reality technology by superimposing virtual planning objects into the physically existing production environment. Utilizing the motion capture system collects data for a set of ergonomic indexes (RULA, OWAS, and NIOSH), accessibility and visibility verification. ARE platform could be used in the verification phase of smart manufacturing systems to evaluate the level of risk to workers’ bodies during operations in real-time. The platform reduces the time and economic cost of verification and satisfies the rapid response of ergonomic evaluation and feedback in the context of smart manufacturing. Finally, the developed ARE platform is validated in two rigorous automotive assembly cases in the laboratory. Meanwhile, the ergonomic assessment results are analysed and reported for a people-oriented manufacturing system.

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.

With the increasing demand for natural interactions, people have realized that an intuitive Computer-Aided Design (CAD) interaction mode can reduce the complexity of CAD operation and improve the design experience. Although interaction modes like gaze and gesture are compatible with some complex CAD manipulations, they still require people to express their design intentions physically. The brain contains design intentions implicitly and controls the corresponding body parts that execute the task. Therefore, building an end-to-end channel between the brain and computer as an auxiliary mode for CAD manipulation will allow people to send design intentions mentally and make their interaction more intuitive. This work focuses on the 1-D translation scene and studies a spatial visual imagery (SVI) paradigm to provide theoretical support for building an electroencephalograph (EEG)-based brain–computer interface (BCI) for CAD manipulation. Based on the analysis of three spatial EEG features related to SVI (e.g., common spatial patterns, cross-correlation, and coherence), a multi-feature fusion-based discrimination model was built for SVI. The average accuracy of the intent discrimination of 10 subjects was 86%, and the highest accuracy was 93%. The method proposed was verified to be feasible for discriminating the intentions of CAD object translation with good classification performance. This work further proves the potential of BCI in natural CAD manipulation.