Development and Evaluation of a PCB‘s Manual Assembly system using Augmented Reality and Total Quality

2021 ◽  
Author(s):  
Rudieri Dietrich Bauer ◽  
Thiago Luiz Watambak ◽  
Salvador Sergi Agati ◽  
Marcelo da Silva Hounsell ◽  
Andre Tavares da Silva
2014 ◽  
Vol 513-517 ◽  
pp. 854-857
Author(s):  
Kang Gao ◽  
Hai Lin Xu ◽  
De Ming Zhang ◽  
Zhuang Ouyang ◽  
Cheng Yang Wei

We had present a generic software framework introducing augmented reality technology into the virtual assembly system. The framework is built by the means of integrating OSG and osgART into the MFC application framework. On the basis, we developed an assembly operation system. We showcased the example applications, demonstrating the new possibilities created by augmented assembly.


Author(s):  
João Pedro Andrade Caixeta ◽  
André Luís De Araújo

The use of Augmented Reality (AR) systems in construction processes can represent an essential transformation in the communication between design and production. However, supposing that design-production translations can be obtained from several manufacturing methods (such as robotic, manual, modular, non-modular, and others), there are not enough studies that explored the potential uses of AR as an assistant for assembly operations. To tackle this problem, this ongoing research proposes to investigate the potentials of a low-cost and marker-based AR system to conduct different manual assembly processes. With the observational focus on aspects of precision and feasibility, we used scientific reductions based on modeling, simulation, and prototyping to provide inferences about the proposed tool's behavior in the real world.


2021 ◽  
Author(s):  
Mingyu Fu ◽  
Wei Fang ◽  
Shan Gao ◽  
Jianhao Hong ◽  
Yizhou Chen

Abstract Wearable augmented reality (AR) can superimpose virtual models or annotation on real scenes, and which can be utilized in assembly tasks and resulted in high-efficiency and error-avoided manual operations. Nevertheless, most of existing AR-aided assembly operations are based on the predefined visual instruction step-by-step, lacking scene-aware generation for the assembly assistance. To facilitate a friendly AR-aided assembly process, this paper proposed an Edge Computing driven Scene-aware Intelligent AR Assembly (EC-SIARA) system, and smart and worker-centered assistance is available to provide intuitive visual guidance with less cognitive load. In beginning, the connection between the wearable AR glasses and edge computing system is established, which can alleviate the computation burden for the resource-constraint wearable AR glasses, resulting in a high-efficiency deep learning module for scene awareness during the manual assembly process. And then, based on context understanding of the current assembly status, the corresponding augmented instructions can be triggered accordingly, avoiding the operator’s cognitive load to strictly follow the predefined procedure. Finally, quantitative and qualitative experiments are carried out to evaluate the EC-SIARA system, and experimental results show that the proposed method can realize a worker-center AR assembly process, which can improve the assembly efficiency and reduce the occurrence of assembly errors effectively.


Author(s):  
Yue Wang ◽  
Shusheng Zhang ◽  
Xiaoliang Bai

To improve the robustness and applicability of 3D tracking and registration for augmented reality(AR) aided mechanical assembly system, a 3D registration and tracking method based on the point cloud and visual features is proposed. Firstly, the reference model point cloud is used to definite absolute tracking coordinate system, thus the locating datum of the virtual assembly guidance information is determined. Then by adding visual features matching to the iterative closest points (ICP) registration process, the robustness of tracking and registration is improved. In order to obtain sufficient number of visual feature matching points in this process, a visual feature matching strategy based on orientation vector consistency is proposed. Finally, the loop closure detection and global pose optimization from key frames are added in the tracking registration process. The experimental result shows that the proposed method has good real-time performance and accuracy, and the running speed can reach 30 frames per second. Moreover, it also shows good robustness when the camera is moving fast and the depth information is inaccurate, and the comprehensive performance of the proposed method is better than the KinectFusion method.


2021 ◽  
Vol 111 (09) ◽  
pp. 579-582
Author(s):  
Daniel Schulte ◽  
Martin Sudhoff ◽  
Bernd Kuhlenkötter

In diesem Beitrag wird die Konzeption und Erprobung eines Systems zur Datenerfassung mittels Spracherkennung in der manuellen Montage beschrieben. Dieses wurde in einem realen Montagesystem in der Lern- und Forschungsfabrik (LFF) des Lehrstuhls für Produktionssysteme (LPS) zur Prozesszeitaufnahme eingesetzt. Anschließend wurde die Qualität der Daten sowie auf die Bedienerfreundlichkeit untersucht. Es konnte gezeigt werden, dass die Spracherkennung eine gute Ergänzung zur manuellen Datenerfassung darstellt.   This paper describes the design and testing of a system for data acquisition using speech recognition in manual assembly. This was used in a real assembly system in the Learning and Research Factory of the Chair of Production Systems for process time recording. Subsequently, the quality of the data as well as the user-friendliness were examined. It could be shown that speech recognition is a good complement to manual data acquisition.


2020 ◽  
Vol 10 (23) ◽  
pp. 8624
Author(s):  
Maja Turk ◽  
Miha Pipan ◽  
Marko Simic ◽  
Niko Herakovic

Due to increasing competition in the global market and to meet the need for rapid changes in product variability, it is necessary to introduce self-configurable and smart solutions within the entire process chain, including manual assembly to ensure the more efficient and ergonomic performance of the manual assembly process. This paper presents a smart assembly system including newly developed smart manual assembly workstation controlled by a smart algorithm. The smart assembly workstation is self-configurable according to the anthropometry of the individual worker, the complexity of the assembly process, the product characteristics, and the product structure. The results obtained by a case study show that is possible to organize manual assembly process with rapid adaptation of the smart assembly system to new products and workers characteristics, to achieve ergonomic working conditions through Digital Human Modelling (DHM), to minimize assembly time, and to prevent error during the assembly process. The proposed system supports the manual assembly process redesign to ensure a better working environment and aims to have an important value for applying the smart algorithms to manual assembly workstations in human-centered manufacturing systems.


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