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PurposeIn order to help companies better grasp the perceptual needs of consumers for patterns, so as to carry out more accurate product pattern development and recommendation, this research develops a product pattern design system based on computer-aided design.Design/methodology/approachFirst, use the Kansei engineering theory and method to obtain the user's perceptual image, and deconstruct and encode the pattern based on the morphological analysis method, then through the BP neural network to construct the mapping relationship between the user's perceptual image and the pattern design elements, and finally calculate and find the corresponding design code combination according to the design goal to guide the pattern design.FindingsTaking costume paper-cut patterns as an example, the feasibility of this system is verified, the design system can well reflect the user's perceptual image in the pattern design and improve the efficiency of pattern customization service.Originality/valueCompared with the traditional method that relies on the designer's personal experience to propose a design plan, this research provides scientific and intelligent design methods for product pattern design.

This paper focuses on tight-fitting sportswear as the research subject and investigates the dynamic influence of comfort perception on overall comfort. By developing a comprehensive evaluation system that encompasses both subjective and objective measures of comfort, this study systematically reveals the interactive mechanisms and dynamic characteristics of multi-dimensional comfort perceptions, including heat and humidity comfort, compression comfort, touch comfort, among others. The findings indicate that (1) local comfort levels fluctuate over time, with varying weights influencing overall comfort; (2) during 6 km/h exercise, key local discomfort sensations impacting overall comfort include restraint feelings of shank, stuffy feeling (cool feeling) of shank, restraint feeling of thigh, etc.; (3) there are significant correlations between stuffy feeling with sticky body feeling and humidity feeling respectively; additionally, notable correlations exist between sticky body feeling with humidity feeling and rough feeling. This study offers provides a more scientific basis for optimizing tight-fitting sportswear design.

In order to improve the precision of clothing development of fast fashion brands, consumers’ sense of experience, and brand loyalty, a design method of clothing pattern is proposed by combining Kansei engineering theory and improved particle swarm optimization (IPSO)–back propagation neural network (BPNN) model. First, based on the theory of Kansei engineering, the perceptual image experiment of clothing patterns was designed, and the mean value of perceptual image evaluation of clothing patterns by young consumers was obtained through an online questionnaire survey. Second, based on the IPSO and the BPNN, the nonlinear correlation mapping model between the design elements of clothing pattern and consumers’ perceptual image is established. Finally, based on the calculation of target image weight by analytic hierarchy process (AHP) method and IPSO-BPNN model, the optimal combination of clothing pattern design elements under the requirement of multi-target image is output. Taking the paper-cut pattern of sweater shirt as an example, the feasibility of this research method is verified. The research not only helped the designer to design a costume pattern that meet the individual emotional needs of consumers, but also provided a clear design index and reference, and made the costume design process more targeted, precise, and intelligent.

In order to understand consumers’ cognition of clothing style and design clothing products more in line with people’s emotional needs, a garment style perceptual image prediction model based on PSO-BP neural network was constructed by taking professional dress as an example. Firstly, the professional dress samples were screened and the style design elements were deconstructed and coded. The Kansei engineering theory and factor analysis method were used to determine the representative adjectives, so as to reduce the cognitive dimension of the target users for the style characteristics and perceptual image of the dress. Then, using the sample style design element code as the input layer and the user’s perceptual image evaluation score as the output layer, the PSO-BP neural network’s perceptual image prediction model for professional dress styles is constructed. Finally, the sample data were input into the PSO-BP model, BP neural network and GA-BP model for simulation and calculation, and the error analysis of the results proved that the PSO-BP prediction model is effective and advanced. Designers can use this model to quickly transform customers’ perceptual needs with dress style design elements, so as to improve the scientificity of design decision-making and better meet customer needs.

PurposeFor comfort evaluation of underwear pressure, this paper proposes an improved GA algorithm to optimize the weight and threshold of BP neural network, namely PSO-GA-BP neural network prediction model.Design/methodology/approachThe objective parameters of underwear, body shape data, skin deformation and other data are selected for simulation experiments to predict the objective pressure and subjective evaluation in dynamic and static state. Compared with the prediction results of BP neural network prediction model, GA-BP neural network prediction model and PSO-BP neural network prediction model, the performance of each prediction model is verified.FindingsThe results show that the BP neural network model optimized by PSO-GA algorithm can accelerate the convergence speed of the neural network and improve the prediction accuracy of underwear pressure.Originality/valuePSO-GA-BP model provides data support for underwear design, production and processing and has guiding significance for consumers to choose underwear.

Traditional patterns are widely used in the modern design due to their long history, rich connotation, and beautiful form. However, the current application of traditional patterns in the modern design is mostly based on the designer's subjective preferences, not from the perspective of consumers, to explore their feelings about traditional patterns, and which design factors have an impact on consumers, which is the main reason why modern applications of traditional patterns cannot meet the esthetic needs of modern consumers. Therefore, to make better inheritance of the traditional pattern and meet the needs of contemporary consumers, this article takes the caisson lotus pattern of Mogao Cave in the Tang dynasty as an example and first, using the theory of Kansei engineering to investigate the perceptual cognition of the young consumers aged 20–35 years old on the lotus pattern, then use SPSS 24.0 software to analyze the perceptual evaluation data, find the design element combination code corresponding to the perceptual vocabulary, and establish a mathematical model that can predict consumers’ emotional imagery of the lotus pattern of the caisson in the Tang dynasty. Through the verification of the model, the test results show that the model has a high degree of credibility; designers can use this model to quickly evaluate and redesign the lotus pattern to better meet the needs of modern consumers. At the same time, the method of this paper can also be applied to other design fields with user-centered concerns.

In order to solve the problem of mismatch between consumers’ personalized needs and clothing pattern design, a method of clothing pattern image design was proposed based on Kansei engineering theory to obtain a perceptual consumer image. Then, a correlation model between clothing pattern design elements and perceptual images of young people was established through the quantitation theory type I, and the mapping relationship between the two and the degree of influence on consumer preference was presented by the diagram method. The paper-cut pattern of a T shirt is taken as an example to verify the feasibility of this research method. The results show that it not only provides designers with clear design indicators and references, but also makes the design process more objective and scientific.

Purpose The purpose of this paper is to study the style design methods of professional female vests that meet the emotional needs of consumers. Design/methodology/approach Using the theory of kansei engineering as a guide to screen representative samples of female professional vests and relevant emotional vocabularies of styles, through morphological analysis, style design elements of female professional vests are extracted, the fifth-order semantic difference questionnaire was used to establish the perceptual assessment matrix for design elements, the correlation analysis method and multiple linear regression analysis were used to analyze the results of the perceptual evaluation of the sample, find out the relationship between the perceptual vocabulary and design elements of professional female vest styles, and establish a regression model, finally, it is verified by random samples of the market, so as to guide the development of new products. Findings The seven design elements extracted from professional female vest styles have an impact on consumer perception, by using a linear analysis method, the correspondence between perceptual perception of consumers and style design elements can be quantified and a model can be established to accurately predict consumers’ perceptual intentions. Originality/value The application of perceptual engineering in the style design of professional female vests provides a new idea for the design of clothing styles. It helps garment companies and designers to determine the development direction of professional woman’s vest styles, while the research results provide design reference for other products.

In order to improve the efficiency and accuracy of human body shape prediction, principal component analysis method (PCA) is proposed to reduce the dimension of related variables and eliminate the multicollinearity among variables. Then, the transformed variables are input into genetic algorithm and BP neural network, and a new method of human body shape prediction is designed. To avoid the problems that slow convergence speed and easy falling into local minima of BP neural network, the genetic algorithm is used to optimize the weights and thresholds of BP neural network. Moreover, to prove the superiority of PCA–GA–BP model, the prediction results are compared with those of other algorithms. Body sizes of 18–25-year-old, 26–44-year-old and 45–59-year-old males were selected as experimental data to analyze these models. The prediction results of GA–BP, PCA–BP, BP, SVM and K -means were compared with PCA–GA–BP neural network. The results show that the prediction effect of PCA–GA–BP neural network is significantly better than that of GA–BP, PCA–BP, BP, SVM and K -means prediction models, which can accurately predict and cluster the human body shape. The model has better prediction and classification and simpler structure.

Purpose The purpose of this paper is to analyze the influence of underwear on the microenvironment of human clothing. Design/methodology/approach Based on the basic laws of energy and mass conservation, the paper combined the theory of heat and mass transfer to establish the simulation of the influence of underwear on human thermal reaction in microclimate and prediction model of human thermal reaction law. Findings The impact on the microenvironment affected by tighter underwear is less than the effect of loose underwear and computational flow dynamics (CFD) can accurately predict the thermal reaction parameters’ values of the human body. Originality/value It can be effectively used for the prediction of heat exchange between human body and environment in high-temperature environment and human thermophysiological parameters, and overcomes the individual differences of human experiments and the danger and repeatability of high-temperature environmental experiments.