To improve the effectiveness of industrial robots in the textile and garment industry, it is necessary to expand the application range of electrostatic adsorption end effectors and solve the problem of automatically grasping and transferring fabrics during garment processing. Taking weft-knit fabric as an example, this paper begins by analyzing the factors that influence the electrostatic adsorption capacity, and then constructing an electrostatic adsorption capacity model based on the fabric characteristics. Next, the shape arrangement and structural parameters of the electrode plate are optimized by taking the electrostatic adsorption force model and maximizing the adsorption force per unit area. Finally, the adsorption effect of the electrostatic adsorption end effector is verified by simulation and experiment. The verification results show that the electrode with a comb-shaped arrangement and optimized structural parameters can adsorb clothing fabric well and meets the requirements of clothing automated production lines. This study provides a new method for solving the problem of automatically grasping and transferring fabrics and provides technical support for improving automation in the garment industry.

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Virtual fitting technology is widely used in many fields, such as e-commerce, garment computer-aided design, and film and game production. Cloth modeling and simulation play an important role in virtual fitting. The realism and simulation speed directly affect the user experience and visual appearance. This paper first reviews the history of cloth modeling and simulation methods. Then, it focuses on four perspectives: yarn-structure modeling of the cloth modeling level; multi-resolution grid simulation; the relation between human posture and cloth deformation; and collision problems of the cloth simulation level. Yarn-structure modeling considers a unit cell that incorporates the physical characteristics of the fabric and the fabric structure. A multi-resolution grid can integrate techniques from different research fields and may achieve a breakthrough in cloth modeling. Generative adversarial networks (GANs) have potential for dealing with the relation between human posture and cloth deformation by comparing several typical algorithms with GANs. Solving the collision and friction problem depends on choosing the right envelope box for refining the human body surface. Finally, the paper concludes with an analysis of future research trends that could improve the fidelity and speed of a simulation. The paper may serve as a reference for research into cloth modeling and simulation for virtual fitting.

Developing the technology of estimating human body size from two-dimensional images is the key to realising more digitalization and artificial intelligence in the textile and garment industry. Therefore, this paper is an in-depth study of estimating body sizes from two-dimensional images in a self-collected database of human body samples. First, the artificial thresholds in the Canny edge operator were replaced by adaptive thresholds. The improved Canny edge operator was combined with mathematical morphology so that it could detect a clear and complete single human contour. Then a joint point detection algorithm based on a convolution neural network and human proportion is proposed. It can detect human feature points with different body proportions. Finally, front and side images and manual body measurements of 122 males aged 18–22 years were collected as the human sample database, calculating the length and fit of the girth size. Compared with manual body measurement data, the error of human length and girth size parameters within the national standard range of –1.5 ~ 1.5 cm can reach 91% on average. This study provides an accurate and convenient anthropometric method for digital garment engineering, which can be used for online shopping and garment customization, and has a certain practical value.

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