Optimization model to grasp cutting pieces of fabric layer by layer by soft gripper

Purpose To setup a theoretical model for grasping cutting pieces of garment better, which will help to design a special soft gripper and push forward the automated level of garment manufacturing. Design/methodology/approach This paper first analyzed the mechanics of the grasping process and concluded the main factors that affect the success of grasping. A theoretical model named grasping fabric model (GFM) was constructed to show the mechanical relationship between the soft gripper and the fabric pieces. Subsequently, two fabric samples were selected and tested for their friction properties and critical buckling force, and the test data were substituted into the theoretical model GFM to obtain the grasping parameters required for fabric grasping layer by layer. Findings It was found that (1) the critical buckling force of the fabric is mainly influenced by the bending stiffness and the deformation length of the fabric during grab. (2) The difference between the friction between the soft gripper and the fabric and the friction between the fabric, that is DF1-2, has an important influence on the accuracy of grasping layer-by-layer. Originality/value It showed that the grasping parameters provided by GFM enable the two samples to be more effectively separated layer by layer, which verifies that the GFM model is strong enough for the possible application in garment automated production.

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Publication Details

Journal
International Journal of Clothing Science and Technology
Published
2024-09-19
DOI
https://doi.org/10.1108/ijcst-09-2023-0137
Primary Topic
Robot Manipulation and Learning
Type
article
Field-Weighted Citation Impact
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article

Optimization model to grasp cutting pieces of fabric layer by layer by soft gripper

Junqiang Su, Yawei Ren, Nan Wang, Guoqing Jin
International Journal of Clothing Science and Technology
Robot Manipulation and Learning
article

Optimization model to grasp cutting pieces of fabric layer by layer by soft gripper

Junqiang Su, Yawei Ren, Nan Wang, Guoqing Jin
article en

Abstract

Purpose To setup a theoretical model for grasping cutting pieces of garment better, which will help to design a special soft gripper and push forward the automated level of garment manufacturing. Design/methodology/approach This paper first analyzed the mechanics of the grasping process and concluded the main factors that affect the success of grasping. A theoretical model named grasping fabric model (GFM) was constructed to show the mechanical relationship between the soft gripper and the fabric pieces. Subsequently, two fabric samples were selected and tested for their friction properties and critical buckling force, and the test data were substituted into the theoretical model GFM to obtain the grasping parameters required for fabric grasping layer by layer. Findings It was found that (1) the critical buckling force of the fabric is mainly influenced by the bending stiffness and the deformation length of the fabric during grab. (2) The difference between the friction between the soft gripper and the fabric and the friction between the fabric, that is DF1-2, has an important influence on the accuracy of grasping layer-by-layer. Originality/value It showed that the grasping parameters provided by GFM enable the two samples to be more effectively separated layer by layer, which verifies that the GFM model is strong enough for the possible application in garment automated production.

International Journal of Clothing Science and TechnologyVol. 37(1)
Jiangnan University (CN), Soochow University (CN), Wuhu Hit Robot Technology Research Institute (CN)
Openalex Percentile: Top 82%
Robot Manipulation and Learning
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Optimization model to grasp cutting pieces of fabric layer by layer by soft gripper — Junqiang Su, Yawei Ren, et al. · International Journal of Clothing Science and Technology (2024) | TGRS Research Map | TGRS