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Abstract

In manual percussive riveting training, inconsistencies often occur: either the driven head meets quality standards but rivet/hole interference does not, or vice versa. This work presents the squeezing force-based training platform to enhance trainee skills and improve training efficiency. The quantitative relationship between squeezing force and key joint quality metrics (driven head morphology and rivet/hole interference) is established through systematic experiments. Qualified squeezing force thresholds are determined via experimental design and data analysis, enabling the algorithm to ensure trainees consistently produce joints with both qualified driven head morphology and rivet/hole interference. The comparative training results proves the effectiveness of the proposed squeezing force-based training method. The squeezing force based digital feature analysis and qualified parameter range determination procedures outlined in this work are quite general and can be used for any material/rivet combinations in percussive riveting skills training.

Keywords

percussive riveting skills training squeezing force features riveting impulse qualified parameter ranges

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References

Chandler

TN.

Keeping current in a changing work environment: design issues in repurposing computer-based training for on-the-job training. Int J Ind Ergon 2000; 26: 285–299.

Mital

Pennathur

Huston

, et al. The need for worker training in advanced manufacturing technology (AMT) environments: a white paper. Int J Ind Ergon 1999; 24: 173–184.

Fredericks

Fernandez

JE.

The effect of vibration on psychophysically derived work frequencies for a riveting task. Int J Ind Ergon 1999; 23: 415–429.

Kattel

Fernandez

JE.

The effects of rivet guns on hand-arm vibration. Int J Ind Ergon 1999; 23: 595–608.

Walter

Competency-based on-the-job training for aviation maintenance and inspection—a human factors approach. Int J Ind Ergon 2000; 26: 249–259.

Kraus

Gramopadhye

AK.

Effect of team training on aircraft maintenance technicians: computer-based training versus instructor-based training. Int J Ind Ergon 2001; 27: 141–157.

Pennathur

Mital

Worker mobility and training in advanced manufacturing. Int J Ind Ergon 2003; 32: 363–388.

Sadasivan

Gramopadhye

AK.

Technology to support inspection training in the general aviation industry: specification and design. Int J Ind Ergon 2009; 39: 608–620.

Gooyers

Stevenson

JM.

The impact of an increase in work rate on task demands for a simulated industrial hand tool assembly task. Int J Ind Ergon 2012; 42: 80–89.

10.

Ouellet

Vézina

Work training and MSDs prevention: contribution of ergonomics. Int J Ind Ergon 2014; 44: 24–31.

11.

Al-Ahmari

Ameen

Abidi

, et al. Evaluation of 3D printing approach for manual assembly training. Int J Ind Ergon 2018; 66: 57–62.

12.

Alasim

Nimbarte

Jaridi

Impact of pulling direction and magnitude of force exertion on the activation of shoulder muscle. Int J Ind Ergon 2019; 69: 14–22.

13.

Abubakar

Wang

Key human factors and their effects on human centered assembly performance. Int J Ind Ergon 2019; 69: 48–57.

14.

Wang

Zhang

Squeezing force detection and signal processing for robot assisted pneumatic percussive riveting system. J Manuf Process 2020; 57: 389–399.

15.

Tian

Dan

, et al. Extracting technicians’ skills for human–machine collaboration in aircraft assembly. Biomimetics 2023; 8(8): 604.

16.

Wang

, et al. An adaptive AR guidance interface layout optimization approach for human centered assembly training systems. Adv Eng Inform 2026; 69: 103975.

17.

Eswaran

Bahubalendruni

Augmented reality aided object mapping for worker assistance/training in an industrial assembly context: exploration of affordance with existing guidance techniques. Comput Ind Eng 2023; 185: 109663.

18.

Eswaran

Prasad

Hymavathi

, et al. Augmented reality guided autonomous assembly system: a novel framework for assembly sequence input validations and creation of virtual content for AR instructions development. J Manuf Syst 2024; 72: 104–121.

19.

Grodotzki

Müller

Tekkaya

AE.

Enhancing manufacturing education based on controller-free augmented reality learning. Manuf Lett 2023; 35: 1246–1254.

20.

Chinese Aviation Industry Standard. HB/Z 223.3-2003: aircraft assembling technology, part 3: conventional riveting. Chinese Aviation Industry Standard, 2003.

21.

Zhang

. The FEM investigation into the composite interference-fit joint. MA Thesis, School of Electromechanical Engineering, Xidian University, China, 2011.

Squeezing force based digital feature analysis and qualified parameter range determination for percussive riveting skills training

Abstract

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