Evaluation of 3D printing approach for manual assembly training

Al-Ahmari, Abdulrahman; Ameen, Wadea; Abidi, Mustufa Haider; Mian, Syed Hammad

doi:10.1016/j.ergon.2018.02.004

Cited by 13 publications

(16 citation statements)

References 6 publications

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“…The most frequently used objective measure was performance, either the time needed for completing the assembly after training (task completion time) and/or the accuracy in assembling a product, that is, the error rate or quality of task processing (task accuracy). Task completion time (TCT) was measured in 20 out of 24 articles (Doolani, Owens, et al, 2020; Al-Ahmari et al, 2018; Carlson et al, 2015; Cooper et al, 2018; deMoura & Sadagic, 2019; Gavish et al, 2015; Hoedt et al, 2017; Hou & Wang, 2013; Hou et al, 2013, 2015; Koumaditis et al, 2020; Kwiatek et al, 2019; Langley et al, 2016; Loch et al, 2019; Murcia-Lopez & Steed, 2018; Oren et al, 2012; Roldan et al, 2019; Velaz et al, 2014; Webel et al, 2013; Werrlich, Nguyen, et al, 2018; Westerfield et al, 2015). Similarly, task accuracy (TA) was also collected in 20 of the 24 articles (Doolani, Owens, et al, 2020; Al-Ahmari et al, 2018; deMoura & Sadagic, 2019; Gavish et al, 2015; González-Franco et al, 2016; Hoedt et al, 2017; Hou & Wang, 2013; Hou et al, 2013, 2015; Koumaditis et al, 2020; Langley et al, 2016; Loch et al, 2019; Murcia-Lopez & Steed, 2018; Oren et al, 2012; Roldan et al, 2019; Velaz et al, 2014; Webel et al, 2013; Werrlich, Lorber, et al, 2018; Werrlich, Nguyen, et al, 2018; Westerfield et al, 2015).…”

Section: Resultsmentioning

confidence: 99%

“…Task completion time (TCT) was measured in 20 out of 24 articles (Doolani, Owens, et al, 2020; Al-Ahmari et al, 2018; Carlson et al, 2015; Cooper et al, 2018; deMoura & Sadagic, 2019; Gavish et al, 2015; Hoedt et al, 2017; Hou & Wang, 2013; Hou et al, 2013, 2015; Koumaditis et al, 2020; Kwiatek et al, 2019; Langley et al, 2016; Loch et al, 2019; Murcia-Lopez & Steed, 2018; Oren et al, 2012; Roldan et al, 2019; Velaz et al, 2014; Webel et al, 2013; Werrlich, Nguyen, et al, 2018; Westerfield et al, 2015). Similarly, task accuracy (TA) was also collected in 20 of the 24 articles (Doolani, Owens, et al, 2020; Al-Ahmari et al, 2018; deMoura & Sadagic, 2019; Gavish et al, 2015; González-Franco et al, 2016; Hoedt et al, 2017; Hou & Wang, 2013; Hou et al, 2013, 2015; Koumaditis et al, 2020; Langley et al, 2016; Loch et al, 2019; Murcia-Lopez & Steed, 2018; Oren et al, 2012; Roldan et al, 2019; Velaz et al, 2014; Webel et al, 2013; Werrlich, Lorber, et al, 2018; Werrlich, Nguyen, et al, 2018; Westerfield et al, 2015). Additionally, in ten articles statements were made about the training time required with the respective technology to learn how to fulfill the defined assembly task (Doolani, Owens, et al, 2020; Carlson et al, 2015; Gavish et al, 2015; González-Franco et al, 2016; Murcia-Lopez & Steed, 2018; Oren et al, 2012;…”

Section: Resultsmentioning

confidence: 99%

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Effects of Augmented Reality-, Virtual Reality-, and Mixed Reality–Based Training on Objective Performance Measures and Subjective Evaluations in Manual Assembly Tasks: A Scoping Review

Daling

Schlittmeier

2022

Hum Factors

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Objective The present scoping review aims to transform the diverse field of research on the effects of mixed reality-based training on performance in manual assembly tasks into comprehensive statements about industrial needs for and effects of mixed reality-based training. Background Technologies such as augmented and virtual reality, referred to as mixed reality, are seen as promising media for training manual assembly tasks. Nevertheless, current literature shows partly contradictory results, which is due to the diversity of the hardware used, manual assembly tasks as well as methodological approaches to investigate the effects of mixed reality-based training. Method Following the methodological approach of a scoping review, we selected 24 articles according to predefined criteria and analyzed them concerning five key aspects: (1) the needs in the industry for mixed reality-based training, (2) the actual use and classification of mixed reality technologies, (3) defined measures for evaluating the outcomes of mixed reality-based training, (4) findings on objectively measured performance and subjective evaluations, as well as (5) identified research gaps. Results Regarding the improvement of performance and effectiveness through mixed reality-based training, promising results were found particularly for augmented reality-based training, while virtual reality-based training is mostly—but not consistently—as good as traditional training. Application Mixed reality-based training is still not consistently better, but mostly at least as good as traditional training. However, depending on the use case and technology used, the training outcomes in terms of assembly performance and subjective evaluations show promising results of mixed reality-based training.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Effects of Augmented Reality-, Virtual Reality-, and Mixed Reality–Based Training on Objective Performance Measures and Subjective Evaluations in Manual Assembly Tasks: A Scoping Review

Daling

Schlittmeier

2022

Hum Factors

View full text Add to dashboard Cite

show abstract

“…First, NASA-TLX has been widely used to measure the workload in a vast arrange of tasks (Akyeampong, Udoka, Caruso, & Bordegoni, 2014;Orlandi & Brooks, 2018;Shakouri, Ikuma, Aghazadeh, & Nahmens, 2018). In AM, NASA-TLX was used as a determining criterion for the choice of a new assembly method, in which the mental load was a critical factor of the operator's performance (Al-ahmari, Ameen, & Haider, 2018). In many cases of manufacture and health procedures, NASA-TLX has been used to determine workload in repetitive activities using the analysis to improve the tasks reducing the level of workload during their execution (Buchert et al, 2019, Dodou, Fan, Sc, Jelı, & Breedveld, 2015.…”

Section: Discussionmentioning

confidence: 99%

Application of NASA-TLX and SHERPA in Setting Up Fused Filament Fabrication Equipment

2019

Proceedings of the Human Factors and Ergonomics Society Annual

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The use of new technologies in productive processes nowadays poses a challenge to the users, mainly because those technologies create them a necessity for learning and adaptation. This study focused on the assessment and analysis of mental workload and human error as experienced by users of Fused Filament Fabrication (FFF) printers, which is the most recent development in a series of technologies known as Additive Manufacturing (AM). Eight subjects were asked to complete the setting up of three commercial FFF equipment. Hierarchical Task Analysis (HTA) was used to describe the tasks. NASA-TLX method was used to assess the mental workload involved, and SHERPA method was applied to identify human errors while completing the task. The results of HTA show that leveling the printing bed is the most demanding activity on the subject's concentration. NASA-TLX shows that Mental Demand subscale had the highest average punctuation with 78.75 points, followed by Temporal Demand with an average of 61 points. As for SHERPA analysis, 59 human errors were identified in that particular task. In detail, 54.2% were action errors, 22.0% were selection errors, and 18.6% were checking errors. The combination of HTA, NASA-TLX and SHERPA techniques, permitted a complete analysis of the interaction between humans and this technology, opening the way to improvements in the design of FFT equipment which possibly might prevent future effects on user’s health.

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“…Ouellet 10 reported their researches in work training and musculoskeletal disorders in meat-processing enterprise, especially concerning about the worker removed the fat and bones from the pork hams. Al-Ahmari 11 proposed an assembly training method using 3D printing parts. It was claimed that the proposed training method was better than conventional drawing and virtual reality.…”

Section: Introductionmentioning

confidence: 99%

Squeezing force-based training method for manual pneumatic percussive riveting

Dong

et al. 2023

Second International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2023)

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Manual percussive riveting quality varies due to the different operations of the workers, which result from different training categories and processes. The paper proposed a squeezing force based percussive riveting training system to improve the recruits’ operation skills and the training efficiency. Force sensor is installed in the bucking bar to gather the squeezing force signal of the percussive riveting. The squeezing force signal of the expert technicians is used as the standard operation of the manual pneumatic percussive riveting. Quantitative indices are extracted from the squeezing force signals in order to provide the recruits with quantitative parameter for efficient training. Data-driven training categories based on the proposed training platform are developed to assure the operators maintain the qualified skills constantly. The squeezing force-based training system for pneumatic percussive riveting provides good platform for the recruits training. The proposed system can be used for high-skilled technicians training with good quality and efficiency.

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Evaluation of 3D printing approach for manual assembly training

Cited by 13 publications

References 6 publications

Effects of Augmented Reality-, Virtual Reality-, and Mixed Reality–Based Training on Objective Performance Measures and Subjective Evaluations in Manual Assembly Tasks: A Scoping Review

Effects of Augmented Reality-, Virtual Reality-, and Mixed Reality–Based Training on Objective Performance Measures and Subjective Evaluations in Manual Assembly Tasks: A Scoping Review

Application of NASA-TLX and SHERPA in Setting Up Fused Filament Fabrication Equipment

Squeezing force-based training method for manual pneumatic percussive riveting

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