scholarly journals Evaluation of Fitts’ Throughput as a Possible Performance Measure for Manual Assembly Tasks

2020 ◽  
Author(s):  
Pradyumna Bettagere Jagadish
Keyword(s):  
Performance Measure ◽  
Manual Assembly ◽  
Assembly Tasks

Industrial Perspectives on Assistive Systems for Manual Assembly Tasks

2018 ◽  
Author(s):  
Josef Wolfartsberger ◽  
Jean D. Hallewell Haslwanter ◽  
Roman Froschauer ◽  
René Lindorfer ◽  
Mario Jungwirth ◽  
...  
Keyword(s):  
Manual Assembly ◽  
Assistive Systems ◽  
Assembly Tasks

scholarly journals CAD Feature Recognition as a Means to Prevent Ergonomics Issues during Manual Assembly Tasks

2017 ◽  
Author(s):  
Marco Marconi ◽  
Michele Germani ◽  
Claudio Favi ◽  
Roberto Raffaeli
Keyword(s):  
Feature Recognition ◽  
Manual Assembly ◽  
Assembly Tasks

scholarly journals Edge Computing Driven Scene-aware Intelligent Augmented Reality for Manual Assembly

2021 ◽  
Author(s):  
Mingyu Fu ◽  
Wei Fang ◽  
Shan Gao ◽  
Jianhao Hong ◽  
Yizhou Chen
Keyword(s):  
Augmented Reality ◽  
Cognitive Load ◽  
High Efficiency ◽  
Computing System ◽  
Edge Computing ◽  
Assembly Process ◽  
Manual Assembly ◽  
Worker Center ◽  
Assembly Operations ◽  
Assembly Tasks

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.

scholarly journals BREP Identification During Voxel-Based Collision Detection for Haptic Manual Assembly

2011 ◽  
Author(s):  
Daniela Faas ◽  
Judy M. Vance
Keyword(s):  
Collision Detection ◽  
Force Feedback ◽  
Boundary Representation ◽  
Small Subset ◽  
Manual Assembly ◽  
Assembly Simulation ◽  
Geometric Boundary ◽  
Novel Method ◽  
Constraint Enforcement ◽  
Assembly Tasks

This paper presents a novel method to tie geometric boundary representation (BREP) to voxel-based collision detection for use in haptic manual assembly simulation. Virtual Reality, in particular haptics, has been applied with promising results to improve preliminary product design, assembly prototyping and maintenance operations. However, current methodologies do not provide support for low clearance assembly tasks, reducing the applicability of haptics to a small subset of potential situations. This paper discusses a new approach, which combines highly accurate CAD geometry (boundary representation) with voxel models to support a hybrid method involving both geometric constraint enforcement and voxel-based collision detection to provide stable haptic force feedback. With the methods presented here, BREP data can be accessed during voxel-based collision detection. This information can be used for constraint recognition and lead to constraint-guidance during the assembly process.

scholarly journals Empirical relationships between algorithmic SDA-M-based memory assessments and human errors in manual assembly tasks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Benjamin Strenge ◽  
Thomas Schack
Keyword(s):  
Mental Representation ◽  
Cognitive Resources ◽  
Empirical Relationships ◽  
Manual Assembly ◽  
Human Errors ◽  
Theoretical Assumptions ◽  
Individual Memory ◽  
Lot Sizes ◽  
Technical Systems ◽  
Assembly Tasks

AbstractThe majority of manufacturing tasks are still performed by human workers, and this will probably continue to be the case in many industry 4.0 settings that aim at highly customized products and small lot sizes. Technical systems could assist on-the-job training and execution of these manual assembly processes, using augmented reality and other means, by properly treating and supporting workers’ cognitive resources. Recent algorithmic advancements automatized the assessment of task-related mental representation structures based on SDA-M, which enables technical systems to anticipate mistakes and provide corresponding user-specific assistance. Two studies have empirically investigated the relations between algorithmic assessments of individual memory structures and the occurrences of human errors in different assembly tasks. Hereby theoretical assumptions of the automatized SDA-M assessment approaches were deliberately violated in realistic ways to evaluate the practical applicability of these approaches. Substantial but imperfect correspondences were found between task-related mental representation structures and actual performances with sensitivity and specificity values ranging from 0.63 to 0.72, accompanied by prediction accuracies that were highly significant above chance level.

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