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Computing Optimization of a Parallel Structure-Based Monolithic Gripper for Manipulation Using Weight Method-Based Grey Relational Analysis

Computing Optimization of a Parallel Structure-Based Monolithic Gripper for Manipulation Using Weight Method-Based Grey Relational Analysis

Ngoc Le Chau, Nhat Linh Ho, Tran The Vinh Chung, Shyh-Chour Huang, Thanh-Phong Dao
Copyright: © 2021 |Volume: 12 |Issue: 3 |Pages: 36
ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781799860280|DOI: 10.4018/IJACI.2021070103
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MLA

Le Chau, Ngoc, et al. "Computing Optimization of a Parallel Structure-Based Monolithic Gripper for Manipulation Using Weight Method-Based Grey Relational Analysis." IJACI vol.12, no.3 2021: pp.39-74. http://doi.org/10.4018/IJACI.2021070103

APA

Le Chau, N., Ho, N. L., Tran The Vinh Chung, Huang, S., & Dao, T. (2021). Computing Optimization of a Parallel Structure-Based Monolithic Gripper for Manipulation Using Weight Method-Based Grey Relational Analysis. International Journal of Ambient Computing and Intelligence (IJACI), 12(3), 39-74. http://doi.org/10.4018/IJACI.2021070103

Chicago

Le Chau, Ngoc, et al. "Computing Optimization of a Parallel Structure-Based Monolithic Gripper for Manipulation Using Weight Method-Based Grey Relational Analysis," International Journal of Ambient Computing and Intelligence (IJACI) 12, no.3: 39-74. http://doi.org/10.4018/IJACI.2021070103

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Abstract

This study proposes an integration of the weight method and grey relational analysis to optimize a monolithic gripper. This gripper is desired for use in the assembling industry of cylindrical parts with diameters from 500 µm to 800 µm. The weight factor for each response is calculated accurately. Response surface methodology and Taguchi method are utilized to build an experiment matrix, and grey relational analysis is utilized to predict optimal results. The results found that the predicted displacement value is 0.5699 µm, and the predicted frequency value is 780.9 Hz. Compared to the initial design, the quality of responses is improved by 7.53% for the natural frequency and 35.29% for the output displacement. The error between the predicted result and the verified result is 1.15% for the natural frequency and 16.62% for the output displacement, respectively. It implies that the proposed method has a statistical accuracy.

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