A knowledge-supported approach for garment pattern design using fuzzy logic and artificial neural networks

Xing, Yingmei; Wang, Jianping; Zeng, Xiaoyang; Yang, Yalan; Xu, Shuo

doi:10.1007/s11042-020-10090-6

Cited by 19 publications

(14 citation statements)

References 34 publications

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“…Virtual reality technology makes use of computer to simulate a three-dimensional virtual world, and then uses sensory simulation to make users have an immersive feeling [ 4 ]. The application of virtual reality technology in clothing design is to design, analyze.…”

Section: Related Discussionmentioning

confidence: 99%

3D Garment Design Model Based on Convolution Neural Network and Virtual Reality

Liu

2022

Computational Intelligence and Neuroscience

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The development of virtual reality technology has promoted the unceasing reform and development in the field of fashion design. Aiming at the key technologies and research difficulties in 3D clothing design, the structure of convolution neural network was designed, and a 3D clothing design model based on convolution neural network and virtual reality was constructed. By designing experiments, the average error, matching rate, prediction accuracy, pressure value, and other evaluation indexes are used to measure the performance of convolution neural network model in virtual reality three-dimensional clothing design. The results show that: first, the convolution neural network has the fastest training speed, with the maximum error of 1.63% and the average error of 0.48%. Second, with the increase of parameters, the matching degree of each part of the version and the corresponding data of the human body will gradually increase, the matching rate will slowly improve, and finally tends to 0.9 or so to achieve stability, Thirdly, the prediction accuracy of the convolution neural network is 92.689%, and the loss value is the least. Fourthly, in the virtual fitting and garment wearing experiments, the fit of the sample is within the allowed range, indicating that the automatic generation of fit sample has wearability and rationality.

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Section: Related Discussionmentioning

confidence: 99%

3D Garment Design Model Based on Convolution Neural Network and Virtual Reality

Liu

2022

Computational Intelligence and Neuroscience

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Section: Methodsmentioning

confidence: 99%

“…The first group consists of data concerning the proportions, conformations and postures (in either static or dynamic situations) of the human body, the degree of development of muscle tissue, the degree of development and the distribution of adipose tissue, gender, age, etc. 17,18 The second group comprises data concerning the details of the model, the characteristics of the raw materials and accessories used during the manufacturing process (the physical properties, colours and structure of the design), silhouette, sewing accuracy lines, etc.…”

Section: Methodsmentioning

confidence: 99%

A new approach to determining the constructive balance for the design of customised patterns of men garments

Avădanei

Olaru

Dulgheriu

et al. 2021

Journal of Engineered Fibers and Fabrics

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The objective of this study was to develop a new method of designing ergonomically shaped men’s jackets that perfectly adapt to the customer’s body shape and style (i.e. custom made jackets). The study included 10 adult subjects who prefer to wear formal jackets to work. These subjects underwent 3D scans, and the images were used to execute the study objective. The constructive balance values of men’s jacket patterns were determined by considering the characteristics of the subject’s torso and preferred style. The longitudinal contours of the torso were described by parabolic equations, which were solved with the Gauss method. The obtained value of the constructive balance (1.5 ÷ 3 cm) was used to design jackets using a particular module of Gemini CADs, which allows the mathematical relations between the positions of specific points on the contour of a men’s jacket to be expressed. The pieces are designed in Gemini’s geometric layer. The designer can establish the mathematical relations based on his/her pattern-making competencies and human body and garment evaluation results. In a 3D virtual environment (Lectra-Modaris 3D), the customer’s avatars were dressed with the same jacket model but with different bust lines and various widths on the lateral side. The bust line level was established by using the backside length (measured from the seven-vertebra until the armpit level) and an allowance value, which was established after the human body backside curvature, garment silhouette, number of layers, etc. were analysed. Two variants of the jacket model were designed: a regular- and a medium-fit at the bust level (the value of the constructive bust allowance was 7 ÷ 10 cm). The garment position on the body was evaluated by studying its relative displacement when the avatar performed regular movements (moving his upper limbs). Thirteen cases were considered significant and used to analyse the jacket hemline relative displacement (relative to its horizontal position). These cases were analysed with factorial programming and a rotatable compound central programme with two independent variables. The best shape, size and position of the jacket on the virtual body were determined for the following situation: the value of the backside allowance (longitudinal direction) was between 1.6 and 4 cm, and the width of the jacket on the lateral side was calculated with the percentage of the constructive bust allowance ranging from 49% to 51%. With these parameters, the garment appears to fit the body well.

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“…Fuzzy logic and artificial neural network are hybridised in the work of (Wang, et al, 2020) for garment pattern design. Genetic and fuzzy logic hybridisation is also used in a model for e-customisation for garment recommendation system.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Computational Intelligence System for Fashion Design

Sajja

2021

International Journal of System Dynamics Applications

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The domain of fashion design evolves continuously and is highly personalised, demanding intelligent and customised recommendation. The traditional artificial intelligence-based systems offer solutions based on stored knowledge; hence they can be quickly obsolete and require high effort. To meet the fashion designers’ needs and provide tailor-made recommendations effectively, a hybrid genetic-fuzzy system is proposed with interactive fitness functions. The system is based on generic hybrid architecture using fuzzy logic and genetic algorithm, which can be used to evolve various products in different domains and tested with interactive fuzzy fitness functions. The design of the generic architecture meets the research gap identified through an in-depth literature survey. To prove the utility of the architecture, an experiment is carried out showing encoding scheme, genetic operators, fuzzy membership functions, and fuzzy rules. The results are also discussed, along with the comparison, advantages, applications, and possible future enhancements.

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A knowledge-supported approach for garment pattern design using fuzzy logic and artificial neural networks

Cited by 19 publications

References 34 publications

3D Garment Design Model Based on Convolution Neural Network and Virtual Reality

3D Garment Design Model Based on Convolution Neural Network and Virtual Reality

A new approach to determining the constructive balance for the design of customised patterns of men garments

Hybrid Computational Intelligence System for Fashion Design

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