Search Results (62)

In the context of Industry 4.0, user demands for industrial robots have shifted toward diversification and experience-orientation. Effectively integrating users’ affective imagery requirements into industrial-robot form design remains a critical challenge. Traditional methods rely heavily on designers’ subjective judgments and lack objective data on user cognition. To address these limitations, this study develops a comprehensive methodology grounded in Kansei engineering that combines Extenics-based semantic analysis, eye-tracking experiments, and user imagery evaluation. First, we used web crawlers to harvest user-generated descriptors for industrial floor-cleaning robots and applied Extenics theory to quantify and filter key perceptual imagery features. Second, eye-tracking experiments captured users’ visual-attention patterns during robot observation, allowing us to identify pivotal design elements and assemble a sample repository. Finally, the semantic differential method collected users’ evaluations of these design elements, and correlation analysis mapped emotional needs onto stylistic features. Our findings reveal strong positive correlations between four core imagery preferences—“dignified,” “technological,” “agile,” and “minimalist”—and their corresponding styling elements. By integrating qualitative semantic data with quantitative eye-tracking metrics, this research provides a scientific foundation and novel insights for emotion-driven design in industrial floor-cleaning robots. Full article

Remanufacturing design is a green design model that considers remanufacturability during the design process to improve the reuse of components. However, traditional remanufacturing design scheme decision making focuses on the remanufacturability indicator and does not fully consider the carbon emissions of the remanufacturing process, which will take away the energy-saving and emission reduction benefits of remanufacturing. In addition, remanufacturing design schemes rarely consider the human ergonomics of the product, which leads to uncomfortable handling of the product by the customer. To reduce the remanufacturing carbon emission and improve customer comfort, it is necessary to select a reasonable design scheme to satisfy the carbon emission reduction and ergonomics demand; therefore, this paper proposes an integrated multi-criteria decision-making method for remanufacturing design that considers the carbon emission and human ergonomics. Firstly, an evaluation system of remanufacturing design schemes is constructed to consider the remanufacturability, cost, carbon emission, and human ergonomics of the product, and the evaluation indicators are quantified by the normalization method and the Kansei engineering (KE) method; meanwhile, the hierarchical analysis method (AHP) and entropy weight method (EW) are used for the calculation of the subjective and objective weights. Then, a multi-attribute decision-making method based on the combination of an assignment approximation of ideal solution ranking (TOPSIS) and gray correlation analysis (GRA) is proposed to complete the design scheme selection. Finally, the feasibility of the scheme is verified by taking a household coffee machine as an example. This method has been implemented as an application using Visual Studio 2022 and Microsoft SQL Server 2022. The research results indicate that this decision-making method can quickly and accurately generate reasonable remanufacturing design schemes. Full article

This study presents a data-driven, multi-objective optimization framework for user-centric product form design, integrating affective response modeling with coupled constraint satisfaction. Initially, morphological analysis and aesthetic evaluation are employed to extract critical design elements, while cluster analysis segments users based on preference data. Dominance-based rough set theory is then applied to derive group-specific affective patterns, which are subsequently modeled using Genetic Algorithm-optimized Backpropagation Neural Networks (GA-BPNN). The framework leverages Non-dominated Sorting Genetic Algorithm II (NSGA-II) to generate Pareto-optimal solutions, balancing aesthetic preferences and engineering constraints across user groups. A case study on SUV form design validates the proposed methodology, demonstrating its efficacy in delivering optimal, user-group-targeted design solutions while accommodating individual variability and constraint interdependencies. The results highlight the framework’s potential as a generalizable approach for emotion-aware, constraint-compliant product design. Full article

Service robots (SRs) are increasingly deployed in commercial settings, yet the factors influencing their acceptance, particularly emotional design elements, remain understudied. This research investigates SR acceptance factors by integrating the technology acceptance model, the Computers Are Social Actors (CASA) framework, Kansei engineering (KE), and social presence theory (SPT) to examine how design elements influence user responses to SRs. Using structural equation modeling of survey data from 318 shoppers and hotel guests in China, we tested relationships between CASA attributes, emotional perceptions, social presence, and usage intention. The results revealed that communication style produced the strongest effects across all emotional dimensions, with cuteness and coolness directly influencing usage intention, while warmth and novelty operate through social presence mediation. Multi-group analysis identified significant gender differences in response patterns: male users prioritized communication-driven perceptions while female users responded more strongly to coolness attributes. These findings extend our understanding of acceptance factors in service robot adoption, highlighting the critical roles of emotional design, communication style, and gender differences, while suggesting differentiated design approaches for diverse user segments. Full article

The evaluation of imagery style in industrial product design is inherently subjective, making it difficult for designers to accurately capture user preferences. This ambiguity often results in suboptimal market positioning and design decisions. Existing methods, primarily limited to single product categories, rely on labor-intensive user surveys and computationally expensive data processing techniques, thus failing to support cross-category collaboration. To address this, we propose an Imagery Style Evaluation (ISE) method that enables rapid, objective, and intelligent assessment of imagery styles across diverse industrial product forms, assisting designers in better capturing user preferences. By combining Kansei Engineering (KE) theory with four key visual morphological features—contour lines, edge transition angles, visual directions and visual curvature—we define six representative style paradigms: Naturalness, Technology, Toughness, Steadiness, Softness, and Dynamism (NTTSSD), enabling quantification of the mapping between product features and user preferences. A deep learning-based ISE architecture was constructed by integrating the NTTSSD paradigms into an enhanced YOLOv5 network with a Convolutional Block Attention Module (CBAM) and semantic feature fusion, enabling effective learning of morphological style features. Experimental results show the method improves mean average precision (mAP) by 1.4% over state-of-the-art baselines across 20 product categories. Validation on 40 product types confirms strong cross-category generalization with a root mean square error (RMSE) of 0.26. Visualization through feature maps and Gradient-weighted Class Activation Mapping (Grad-CAM) further verifies the accuracy and interpretability of the ISE model. This research provides a robust framework for cross-category industrial product style evaluation, enhancing design efficiency and shortening development cycles. Full article

As a knowledge-intensive activity, the Kansei engineering (KE) process encounters numerous challenges in the design knowledge flow, primarily due to issues related to information reliability, uncertainty, and subjectivity. Bridging this gap, this study introduces an advanced KE framework integrating a cloud model with Z-numbers (CMZ) and Bayesian elastic net regression (BENR). In stage-I of this KE, data mining techniques are employed to process online user reviews, coupled with a similarity analysis of affective word clusters to identify representative emotional descriptors. During stage-II, the CMZ algorithm refines K-means clustering outcomes for market-representative product forms, enabling precise feature characterization and experimental prototype development. Stage-III addresses linguistic uncertainties in affective modeling through CMZ-augmented semantic differential questionnaires, achieving a multi-granular representation of subjective evaluations. Subsequently, stage-IV employs BENR for automated hyperparameter optimization in design knowledge inference, eliminating manual intervention. The framework’s efficacy is empirically validated through a domestic cleaning robot case study, demonstrating superior performance in resolving multiple information processing challenges via comparative experiments. Results confirm that this KE framework significantly improves uncertainty management in design knowledge flow compared to conventional implementations. Furthermore, by leveraging the intrinsic symmetry of the normal cloud model with Z-numbers distributions and the balanced ℓ₁/ℓ₂ regularization of BENR, CMZ–BENR framework embodies the principle of structural harmony. Full article

Accessible restrooms must reconcile code-based functionality with the affective expectations of disabled users. This study develops an integrated Kansei Engineering (KE)–Rough Set Theory (RST)–Support Vector Machine (SVM) workflow that converts user emotions into verifiable design guidelines. Surveys and semi-structured interviews with 50 disabled participants produced nine Kansei words; factor analysis extracted three principal emotional factors—tidiness, utility and care—capturing 75.8% of total variance. The morphological decomposition of 60 restroom samples yielded 41 design attributes, from which RST attribute reduction isolated six critical features. An SVR model with a radial-basis kernel, trained on 90% of the data and validated on the remaining 10%, achieved R² = 0.931 and RMSE = 0.085. The exhaustive prediction of 15,750 feasible design combinations pinpointed an optimal configuration; follow-up user testing confirmed the improvement in satisfaction (mean 5.1 on a seven-point scale). The KE–RST–SVM workflow thus offers a reproducible, data-driven path for harmonizing emotional and functional objectives in inclusive restroom design, and can be extended to other barrier-free facilities. Full article

With the rapid expansion of the Electric Sport Utility Vehicle (ESUV) market, capturing consumer aesthetic preferences and emotional needs through front-end styling has become a key issue in automotive design. However, traditional Kansei Engineering (KE) approaches suffer from limited timeliness, subjectivity, and low predictive accuracy when extracting affective vocabulary and modeling the nonlinear relationship between product form and Kansei imagery. To address these challenges, this study proposes an improved KE-based ESUV styling framework that integrates data mining, machine learning, and generative AI. First, real consumer reviews and front-end styling samples are collected via Python-based web scraping. Next, the Biterm Topic Model (BTM) and Analytic Hierarchy Process (AHP) are used to extract representative Kansei vocabulary. Subsequently, the Back Propagation Neural Network (BPNN) and Support Vector Regression (SVR) models are constructed and optimized using the Seagull Optimization Algorithm (SOA) and Particle Swarm Optimization (PSO). Experimental results show that SOA-BPNN achieves superior predictive accuracy. Finally, Stable Diffusion is applied to generate ESUV design schemes, and the optimal model is employed to evaluate their Kansei imagery. The proposed framework offers a systematic and data-driven approach for predicting consumer affective responses in the conceptual styling stage, effectively addressing the limitations of conventional experience-based design. Thus, this study offers both methodological innovation and practical guidance for integrating affective modeling into ESUV styling design. Full article

Consumer attitude shift and green transport advocacy in the sharing economy highlight shared two-wheeled electric scooters (STWESs) for short-distance commuting. Current designs often overlook user emotions and aesthetic alignment with product characteristics. A product design methodology is proposed in this study, constructing optimization models from both the functional experiential and perceptual visual layers. Utilizing context analysis theory (CAT) and the KANO model, an STWES contextual requirements optimization model is formulated. The expert method is then applied to identify five key design elements, generating a category diagram based on typical samples, followed by Kansei evaluation. Using quantitation theory type I (QT-1), regression equations are fitted to determine the impact of different design categories on Kansei evaluation. Illustrated in a campus setting, this approach optimizes the shared mobility experience, meeting college students’ aesthetic preferences. This method serves as a valuable reference for product design in diverse contexts. Full article

The field of complex product design evaluation can attract high ambiguity due to difficulties in establishing indicators and the subjectivity of expert evaluation scoring. Indeed, traditional Kansei Engineering (KE) relies on user requirements and feedback for design evaluation, which may not fully and effectively validate the design evaluation results, let alone determine whether they apply to complex products with more evaluation index systems. To overcome these drawbacks, this study proposes an evaluation method based on Hybrid Kansei Engineering (HKE) modeling for complex product design evaluation. HKE modeling consists of two parts, namely Forward Kansei Engineering (FKE) and Backward Kansei Engineering (BKE). In this study, four electric forklift designs are used as an example. The FKE system adopts the multi-attribute decision evaluation method; obtains the evaluation indexes of the forklift product imagery and then establishes the perceptual word collection; constructs the evaluation index system of the forklift via the Analytic Hierarchy Process (AHP); calculates the weights of the evaluation indexes of each level and their rankings; and calculates the final rankings of the four electric forklift design solutions by adopting the Fuzzy Comprehensive Evaluation (FCE) method. The BKE system adopts eye tracking (ET) to extract the attention time, visual attention hotspot, and other eye movement index data, and the Gray Relation Analysis (GRA) method was used to validate the model to derive the ranking, which verifies the effectiveness and scientific validity of the evaluation method. The results of this study show that the two-way evaluation of HKE modeling can effectively avoid subjective factors in product design, improve the scientific nature of the design, and guarantee the logical rigor of the perceptual design procedure. Full article

This study proposes an optimization method based on Rough Set Theory (RST) and Particle Swarm Optimization–Support Vector Regression (PSO-SVR), aimed at enhancing the emotional dimension of outdoor micro-space (OMS) design, thereby improving users’ outdoor activity duration preferences and emotional experiences. OMS, as a key element in modern urban design, significantly enhances residents’ quality of life and promotes public health. Accurately understanding and predicting users’ emotional needs is the core challenge in optimizing OMS. In this study, the Kansei Engineering (KE) framework is applied, using fuzzy clustering to reduce the dimensionality of emotional descriptors, while RST is employed for attribute reduction to select five key design features that influence users’ emotions. Subsequently, the PSO-SVR model is applied to establish the nonlinear mapping relationship between these design features and users’ emotions, predicting the optimal configuration of OMS design. The results indicate that the optimized OMS design significantly enhances users’ intention to stay in the space, as reflected by higher ratings for emotional descriptors and increased preferences for longer outdoor activity duration, all exceeding the median score of the scale. Additionally, comparative analysis shows that the PSO-SVR model outperforms traditional methods (e.g., BPNN, RF, and SVR) in terms of accuracy and generalization for predictions. These findings demonstrate that the proposed method effectively improves the emotional performance of OMS design and offers a solid optimization framework along with practical guidance for future urban public space design. The innovative contribution of this study lies in the proposed data-driven optimization method that integrates machine learning and KE. This method not only offers a new theoretical perspective for OMS design but also establishes a scientific framework to accurately incorporate users’ emotional needs into the design process. The method contributes new knowledge to the field of urban design, promotes public health and well-being, and provides a solid foundation for future applications in different urban environments. Full article

Red sour soup (RSS) is a traditional food with rich cultural connotations and nutritional value, unique to the Kaili region of Guizhou Province, China. However, the existing packaging design lacks cultural characteristics and visual appeal, which greatly limits its market potential. This study proposes a systematic research framework for RSS packaging design by integrating NLP, Kansei engineering, and semiotics. First, we mined consumers’ dual requirements by analyzing online reviews from e-commerce and tourism platforms using NLP and LDA. Second, we used Kansei engineering to construct a mapping model between consumers’ requirements and design elements. Notably, in the semantic space, we innovatively introduced the concept of a semantic network, considering the relationships between Kansei words for the first time. Finally, we proposed integrating the four dimensions of semiotics into the four stages of design, guiding the application of batik culture in packaging design. Based on this framework, we created a set of RSS packaging designs that integrate modern design concepts and traditional Miao cultural elements. The new design showed significant attractiveness in testing, with 100% of subjects preferring it, fully demonstrating the effectiveness and consumer acceptance of our approach. This study provides new methods and ideas for packaging in the food industry, which has positive significance for the modernized marketing of traditional foods. Full article

In the recent consumer market, eyewear has gone beyond the improvement of visual function and is used for the individual style and the symbolization of social standing. Consequently, the aesthetic design of eyewear influences consumer purchasing decisions. Thus, it is necessary to investigate how eyewear design incorporating elements with aesthetic appeal can enhance the sensory experiences of consumers, thereby intensifying their preference for products and fostering their intent to purchase. Utilizing the Evaluation Grid Method (EGM), the design characteristics of eyewear products in the market were explored to assess how these characteristics affect consumer selections. A quantitative analysis of the key quality attributes in eyewear design was conducted using the Kano Model. The results demonstrated a nonlinear relationship between design attributes and consumer satisfaction, confirming the relevance of the Kano Model’s classification. By providing a multi-dimensional quality, the Kano Model elucidated variations in consumer quality requirements for eyewear design, allowing designers and manufacturers to strategically enhance key product design elements, thus creating items with greater market appeal. The results provide recommendations for the improvement of product design aesthetics to increase visual allure for consumers and strengthen market competitiveness. Full article

Accurately grasping users’ Kansei needs and rapidly transforming them into product design solutions are key factors in enhancing product competitiveness and sustainability. This paper proposes a product appearance design method based on Kansei engineering and AI image generation technology, integrating other approaches, with household indoor hydroponics as the research subject. First, the web crawler is used to obtain product image samples and user online reviews, and factor analysis (FA) is applied to quickly extract users’ Kansei needs. Second, product morphology is used to deconstruct and encode product appearances. Partial least squares regression (PLSR) is then employed to map and quantify the relationships between Kansei needs and design elements, yielding optimal design solutions and one-dimensional sketches. These sketches are subsequently used as controlled conditions in Stable Diffusion (SD), combined with a team-trained Lora model, to generate two-dimensional colored sketches in batches. Finally, evaluations verify that the generated design solutions are satisfactory and meet users’ Kansei needs. The results indicate that the proposed product appearance design method not only holds significant implications for the sustainable development of Kansei engineering in product design but also greatly enhances the efficiency of the design process, providing new insights into integrating new technologies and scientific research methods in the field of product design. Full article

With people’s concern for health and the development of medical technology, medical products for children are gradually appearing in pharmacies and online stores. However, the appearance of most children’s household medical products tends to meet the needs of adults, which leads to low acceptance of medical products. This study aimed to explore 10- to 16-year-old children’s visual and tactile perception of different materials by researching the relationship between the psychological quantities of visual and tactile perception and the physical quantities of the material surface. Based on the theory of kansei engineering, we measured the physical quantities of nine materials used in insulin syringe products and administered a perceptual questionnaire test for children. By correlating subjective perceptions with the physical attributes of the materials’ surfaces, we determined a strong correlation between the visual and tactile psychological properties and the properties of the materials’ surfaces. Children clearly perceive materials, and materials with lower roughness can elicit calmness, while materials with higher gloss elicit negative emotions. This paper establishes an evaluation model and provides a scientific selection method for surface materials in different children’s household medical products. Full article