Experimental study on vibration reduction performance of aerial assembly anti-vibration exoskeleton

doi:10.16265/j.cnki.issn1003-3033.2019.11.021

Abstract

Abstract: In order to evaluate anti-vibration exoskeleton's ability to reduce vibration of aerial assembly power tools, aviation assembly riveting process was simulated and riveting test of its vibration reduction performance was designed. Then a weighted average method was proposed to comprehensively evaluate its performance with mean value of pressure signals and weight coefficient of different postures during riveting period and riveting interval under conditions of wearing or not wearing exoskeleton. The results show that anti-vibration exoskeleton reduces 59.9% of riveting vibration and 50.4% of palm pressure when impacted by riveter for tester 1, and 49.5% and 36.9% respectively for tester 2. Its reduction performance is negatively related with pressure of riveting interval while wearing exoskeleton at 0.01, and it has no correlation with that in case of not wearing exoskeleton and riveting operating posture. It is also found that exoskeleton can reduce vibration of riveter by more than 50% and has an important protective effect on arms of assembly workers.

Key words: aircraft assembly, power tools, aerial assembly anti-vibration exoskeleton, weighted average method, musculoskeletal diseases, vibration reduction

CLC Number:

X912.9

WU Xiaodi, WANG Haibo, ZHANG Le, CHEN Yupeng, XUE Chaojun. Experimental study on vibration reduction performance of aerial assembly anti-vibration exoskeleton[J]. China Safety Science Journal, 2019, 29(11): 129-134.

References

[1] YANG Jenpin,WENG Pinsu,CHEN Yuju, et al. Quality identification of the riveting process by QNN model[C]. 2010 First International Conference on Pervasive Computing, Signal Processing and Applications, 2010: 944-947.
[2] HOGAN D A M, GREINER B A, O'SULLIVAN L. The effect of manual handling training on achieving training transfer, employee's behaviour change and subsequent reduction of work-related musculoskeletal disorders: a systematic review[J]. Ergonomics, 2014, 57(1): 93-107.
[3] MILOSEVIC M, MCCONVILLE K M V. Measurement of vibrations and evaluation of protective gloves for work with hand-held power tools in industrial settings[C]. 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007: 2 281-2 284.
[4] 朱隽沛, 李怀仙, 王海波, 等. 机身装配工人肌肉骨骼损伤调查和人机工效负荷水平研究[J]. 工业工程. 2018, 21(1): 89-95.
ZHU Junpei, LI Huaixian, WANG Haibo, et al. A study of the ergonomic load level in the prevalence of Work-related Musculoskeletal Disorders among the plane's fuselage assembly workers [J]. Industrial Engineering Journal, 2018, 21(1): 89-95.
[5] MELHORN J M, WILKINSON L, RIGGS J D. Management of musculoskeletal pain in the workplace[J]. Journal of Occupational and Environmental Medicine, 2001, 43(2): 83-93.
[6] KIHLBERG S, HAGBERG M. Hand-arm symptoms related to impact and nonimpact hand-held power tools[J]. International Archives of Occupational and Environmental Health, 1997, 69(4): 282-288.
[7] 熊若鑫, 宋元斌, 王宇轩. 基于DNN的作业姿势评估方法及应用[J]. 中国安全科学学报, 2018, 28(5): 105-110.
XIONG Ruoxin, SONG Yuanbin, WANG Yuxuan. Deep neural network based posture assessment method and its application[J]. China Safety Science Journal,2018, 28(5): 105-110.
[8] WESTON E B, ALIZADEH M, KNAPIK G G, et al. Biomechanical evaluation of exoskeleton use on loading of the lumbar spine[J]. Applied Ergonomics, 2018, 68: 101-108.
[9] GRIFFIN M J. Evaluating the effectiveness of gloves in reducing the hazards of hand-transmitted vibration[J]. Occup Environ Med, 1998, 55(5): 340-348.
[10] JING Xingjian, ZHANG Linli, FENG Xiao, et al. A novel bio-inspired anti-vibration structure for operating hand-held jackhammers[J]. Mechanical Systems and Signal Processing, 2019, 118: 317-339.
[11] 张慧. 城市供水管网压力分区及低压改善[D]. 杭州:杭州电子科技大学, 2019.3.
ZHANG Hui. Pressure zoning and low pressure improvement of urban water supply pipeline network[D]. Hangzhou: Hangzhou Dianzi University, 2019.3.

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