A Kansei mining system for affective design

Affective design has received much attention from both academia and industries. It aims at incorporating customers' affective needs into design elements that deliver customers' affective satisfaction. The main challenge for affective design originates from difficulties in mapping customers' subjective impressions, namely Kansei, to perceptual design elements. This paper intends to develop an explicit decision support to improve the Kansei mapping process by reusing knowledge from past sales records and product specifications. As one of the important applications of data mining, association rule mining lends itself to the discovery of useful patterns associated with the mapping of affective needs. A Kansei mining system is developed to utilize valuable affect information latent in customers' impressions of existing affective designs. The goodness of association rules is evaluated according to their achievements of customers' expectations. Conjoint analysis is applied to measure the expected and achieved utilities of a Kansei mapping relationship. Based on goodness evaluation, mapping rules are further refined to empower the system with useful inference patterns. The system architecture and implementation issues are discussed in detail. An application of Kansei mining to mobile phone affective design is presented.     

A novel architecture: Using convolutional neural networks for Kansei attributes automatic evaluation and labeling

Kansei evaluation is crucial to the process of Kansei engineering. However, traditional methods are subjective and random. In order to eliminate the differences of individual evaluation criteria in product Kansei attributes evaluation, and further improve the evaluation efficiency, a novel automatic evaluation and labeling architecture for product Kansei attributes was proposed in this paper based on Convolutional Neural Networks (CNNs). The architecture consists of two modules: (1) Target detection module (Faster R-CNN was taken as an example), (2) Fine-Grained classification module (DFL-CNN was taken as an example). A case study was provided to validate the proposed architecture. The proposed architecture transformed design evaluation tasks into the recognition and classification tasks. The experiments achieved 98.837%, 96.899%, 86.047%, and 81.008% accuracy in the binary, triple, and two five-classification tasks, respectively. Our results proved the feasibility of using computer vision to mimic human vision for the automatic evaluation of Kansei attributes.     

Data-driven generative design for mass customization: A case study

Generative design provides a promising algorithmic solution for mass customization of products, improving both product variety and design efficiency. However, the current designer-driven generative design formulates the automated program in a manual manner and has insufficient ability to satisfy the diverse needs of individuals. In this work, we propose a data-driven generative design framework by integrating multiple types of data to improve the automation level and performance of detail design to boost design efficiency and improve user satisfaction. A computational workflow including automated shape synthesis and structure design methods is established. More specifically, existing designs selected based on user preferences are utilized in the shape synthesis for creating generative models. For structural design, user-product interaction data gathered by sensors are used as inputs for controlling the spatial distributions of heterogeneous lattice structures. Finally, the proposed concept and workflow are demonstrated with a bike saddle design with a personalized shape and inner structures to be manufactured with additive manufacturing.     

Constructing a hybrid Kansei engineering system based on multiple affective responses: Application to product form design

This study proposes an expert system, which is called hybrid Kansei engineering system (HKES) based on multiple affective responses (MARs), to facilitate the development of product form design. HKES is consists of two sub-systems, namely forward Kansei engineering system (FKES) and backward Kansei engineering system (BKES). FKES is utilized to generate product alternatives and BKES is utilized to predict affective response of new product designs. Although the idea of HKES and similar hybrid systems have already been applied in various fields, such as product design, engineering design, and system optimization, most of existing methodologies are limited by searching optimal design solutions using single-objective optimization (SOO), instead of multi-objective optimization (MOO). Hence the applicability of HKES is limited while adapting to real-world problems, such as product form design discussed in this paper. To overcome this shortcoming, this study integrates the methodologies of support vector regression (SVR) and multi-objective genetic algorithm (MOGA) into the scheme of HEKS. BKES was constructed by training SVR prediction model of every single affective response (SAR). The form features of these product samples were treated as input data while the average utility scores obtained from all the consumers were used as output values. FKES generates optimal design alternatives using the MOGA-based searching method according to MARs specified by a product designer as the system supervisor. A case study of mobile phone design was given to demonstrate the analysis results. The proposed HKES based on MARs can be applied to a wide variety of product design problems, as well as other MOO problems involving with subjective human perceptions.     

Can pupil size be measured to assess design products?

Some studies assessed aesthetic appreciation by pupillary measurement. While design judgments are also a kind of aesthetic appreciation, design products might be suitable for assessment in pupillary measurement as well. Hence, this study explores the relationship between pupil size and user subjective opinion using forty-eight International Affective Picture System (IAPS) images and forty-eight product images as stimuli. The stimuli are composed of positive, negative, and neutral images. For each trial, participants viewed scrambled versions of image and then viewed unscrambled versions (target image). The pupil sizes of participants were measured while viewing target image. After viewing target image, participants rated immediately their emotional response to the target on a 7-point scale.     

Characterising user capabilities to support inclusive design evaluation

Designers require knowledge and data about users to effectively evaluate product accessibility during the early stages of design. This paper addresses this problem by setting out the sensory, cognitive and motor dimensions of user capability that are important for product interaction. The relationship between user capability and product demand is used as the underlying conceptual model for product design evaluations and for estimating the number of people potentially excluded from using a given product.     

Kano’s model in Kansei Engineering to evaluate subjective real estate consumer preferences

Kansei Engineering is a product development tool used to identify users’ perceptions and find quantitative relationships between their subjective responses and design features. This paper proposes the use of Kano’s model in this process to analyse the impact of different subjective attributes on consumers’ purchase decisions. A practical example of real estate promotions design is presented. In the first stage, semantic differential is used to measure the subjective component of the emotional state. In the second stage, regression analysis and Kano’s model are used to define the relative weight of each emotional attribute in the purchase decision. Besides linear attributes, Kano’s model identified two other kinds of attributes that present a non-linear performance: basic attributes and exciting attributes. Therefore linear models could underestimate the effect of such kind of attributes.

Relevance to industry

This information is very relevant for architects and designers as it enables them to determine the extent to which they must direct their efforts at improving certain attributes with the object of improving the global evaluation.     

Information management for integrated design environments

This paper identifies requirements for an engineering design information management system. Future CAD systems must support a wide range of activities — such as definition, manipulation and analyses of complex product information models. These models represent not only conventional data associated with current CAD applications, but also design information characterizing the correlations between the requirements, functions, behaviors and physical form of the product. Such functionality is important for both the individual designer and the design organization, as the need to manage information as a corporate asset is becoming a critical component of business strategy. This paper explores these needs using two design studies. The first study illustrates some major concepts relative to non-routine design activities, while the second study focuses on the routine design activities relative to organization interactions. These studies were used to elicit high level requirements which serve as the basis for the development of prototype software systems. These prototypes are briefly introduced here.     

Distributed representation learning and intelligent retrieval of knowledge concepts for conceptual design

Data-driven conceptual design is rapidly emerging as a powerful approach to generate novel and meaningful ideas by leveraging external knowledge especially in the early design phase. Currently, most existing studies focus on the identification and exploration of design knowledge by either using common-sense or building specific-domain ontology databases and semantic networks. However, the overwhelming majority of engineering knowledge is published as highly unstructured and heterogeneous texts, which presents two main challenges for modern conceptual design: (a) how to capture the highly contextual and complex knowledge relationships, (b) how to efficiently retrieve of meaningful and valuable implicit knowledge associations. To this end, in this work, we propose a new data-driven conceptual design approach to represent and retrieve cross-domain knowledge concepts for enhancing design ideation. Specifically, this methodology is divided into three parts. Firstly, engineering design knowledge from the massive body of scientific literature is efficiently learned as information-dense word embeddings, which can encode complex and diverse engineering knowledge concepts into a common distributed vector space. Secondly, we develop a novel semantic association metric to effectively quantify the strength of both explicit and implicit knowledge associations, which further guides the construction of a novel large-scale design knowledge semantic network (DKSN). The resulting DKSN can structure cross-domain engineering knowledge concepts into a weighted directed graph with interconnected nodes. Thirdly, to automatically explore both explicit and implicit knowledge associations of design queries, we further establish an intelligent retrieval framework by applying pathfinding algorithms on the DKSN. Next, the validation results on three benchmarks MTURK-771, TTR and MDEH demonstrate that our constructed DKSN can represent and associate engineering knowledge concepts better than existing state-of-the-art semantic networks. Eventually, two case studies show the effectiveness and practicality of our proposed approach in the real-world engineering conceptual design.     

Data-driven engineering design: A systematic review using scientometric approach

In the last two decades, data regarding engineering design and product development has increased rapidly. Big data exploration and mining offer numerous opportunities for engineering design; however, owing to the multitude of data sources and formats coupled with the high complexity of the design process, these techniques are yet to be utilised to the best of their full potential. In this study, a comprehensive assessment of the state-of-the-art data-driven engineering design (DDED) in the last 20 years was conducted. A scientometric approach was employed wherein first, a systematic article acquisition procedure was performed, where a dataset of 3339 articles related to engineering design and big data analytics applications were extracted from Web of Science (WoS) and Scopus. Thereafter, this dataset was reduced to a dataset of 366 articles based on concise data screening. The resulting articles were used to analyse the dynamics of research in DDED throughout the last 20 years and to detect the primary research topics related to DDED, the most influential authors, and the papers with the highest impact in the DDED domain. Furthermore, the co-occurrence network of keywords/keyphrases and co-authorship networks were constructed and analysed to reveal the interconnection of the research topics and the collaboration between the most prolific authors. Finally, an insight how big data analytics is being applied through product development activities to support decision-making in engineering design was presented.     

A computerized optimization framework for the morphological matrix applied to aircraft conceptual design

This paper presents a formal mathematical framework for the use of the morphological matrix in a computerized conceptual design framework. Within the presented framework, the matrix is quantified so that each solution principle is associated with a set of characteristics such as weight, cost, performance, etc. Selection of individual solutions is modeled with decision variables and an optimization problem is formulated. The applications are the conceptual design of subsystems for an Unmanned Aerial Vehicle and an aircraft fuel transfer system. Both the system models and the mathematical framework are implemented in MS Excel.     

Integrating aesthetic and emotional preferences in social robot design: An affective design approach with Kansei Engineering and Deep Convolutional Generative Adversarial Network

Recently, many companies have increasingly emphasized product appearance aesthetics and emotional preference-based design to enhance the competitiveness and popularity of their products. Identifying the interaction between product appearance and customer preferences and mining design information from the interacting context play essential roles in affect-related design approaches. However, due to the complexity of the aesthetic and emotional perception process, obtaining such design information from the interacting context is challenging. This paper proposes an affective design approach based on the Kansei engineering (KE) method and a deep convolutional generative adversarial network (DCGAN) following the research trend of merging KE with computer science techniques in recent years. A case study of the social robot design is conducted to verify the effectiveness of this approach. Appearance aesthetic and emotional preference evaluations are adopted by the KE method first to identify the crucial features in two categories: (1) The physical features of the outer shape, head and color for aesthetics; (2) The emotional features of intelligent, interesting and pleasant for preference perceptions. Based on a manually created social robot image dataset, the DCGAN model is trained to automatically generate novel design images. Then several professional designers are involved to fine-tune the generated images in detail. The experimental results show that the newly designed social robots tend to obtain positive aesthetic and preference evaluations. Practically, such an affective design approach can help industrial design companies identify customers’ psychological requirements and support designers in creating new products innovatively and efficiently.     

Method for identification and integration of design automation tasks in industrial contexts

Current industrial practice does not reflect the opportunities provided by state-of-the-art design automation methods. The limited application of computational methods to support the design process by automating design tasks is caused by the lack of methods for comprehensive design automation task definition. Therefore, potential design automation tasks are not recognized and already deployed solutions lack integration to design practice from a product lifecycle management (PLM) perspective. In response to these shortcomings, this work proposes a method for identification and integration of design automation tasks that features collaborative workshops and enterprise architecture modelling for comprehensive analysis of design processes including its technological environments. The method applies design automation task templates that contextualize the knowledge levels required for design automation task definition and the design process including its technological environments. Evaluation with three industrial cases shows that the method enables efficient identification and integration of potential design automation tasks in a PLM context. The application of knowledge levels in conjunction with enterprise architecture modelling support the identification and validation of the relevant sources of knowledge required for design automation task formalization. Thus, this work contributes by introducing and evaluating a novel method for design automation task definition that brings the opportunities of state-of-the-art design automation methods into line with requirements stemming from design practice and the related PLM.     

Engineering change management in individual and mass production

The ability to manage engineering changes (ECs) efficiently reflects the agility of an enterprise. A large majority of products become gradually improved and perfected through the developmental-design process, during which the set design requirements are met or even upgraded, thus prolonging the product life cycle. The concept of product improvement was based on the activation and tracking of (ECs) through the developmental-design phase and the manufacturing phase. A special method was used to recognize activities within the process and the degree of involvement of individual participants. The individuals involved in the process were provided with appropriate information and the required communication channels with others were ensured. The EC process was generalized and applied to different types of production. A product's complexity and design level were analyzed first, and key factors such as CE methods, process definition, information system, communication and organization were used as a tool for optimizing the EC process. The method was tested and successfully applied into industrial practice.     

Comparing a knowledge-based and a data-driven method in querying data streams for system fault detection: A hydraulic drive system application

The field of fault detection and diagnosis has been the subject of considerable interest in industry. Fault detection may increase the availability of products, thereby improving their quality. Fault detection and diagnosis methods can be classified in three categories: data-driven, analytically based, and knowledge-based methods.     

A knowledge support approach for the preliminary design of platform-based products in Engineering-To-Order manufacturing

A product platform is a design approach for meeting the demand for customizable products. Traditional knowledge-based technologies or systems lack flexibility in supporting both configuration and parameter design of platform-based products. In many cases, customers’ requirements and knowledge models both contain incomplete information, and there are complex relations among various solutions, functions and solution parameters in Engineering-To-Order (ETO) products. A knowledge model for the preliminary design of ETO products is presented in this paper, and linkages are established between configuration design knowledge and parameter design procedures. The basis of the knowledge model is the Extended Function-Solutions (EFS) tree, from which design case trees, design modules, constraint checking rules, and module interface templates derive. A corresponding knowledge retrieval and reuse strategy is also presented. It uses an improved fuzzy information axiom to search for the optimal configuration with incomplete information. The parameter design process model of new products then can be generated based on the optimal configuration. The case study demonstrates the knowledge modeling, retrieval and reuse for the preliminary design of open-type crank presses. Moreover, the effectiveness of the methodology is discussed by analyzing the verification approach and the satisfaction of customers’ requirements.     

Data mining in design of products and production systems

Data mining is acquiring its own identity by refining concepts from other disciplines, developing generic algorithms, and entering new application areas. Engineering design and manufacturing have been affected by the data mining pursuit. This paper outlines areas of product and manufacturing system design that are particularly suitable for data-mining applications. One of the emerging areas is innovation. The key challenges of data mining in the domains discussed in the paper are outlined.     

A framework to integrate design knowledge reuse and requirements management in engineering design

This paper presents a framework to integrate requirements management and design knowledge reuse. The research approach begins with a literature review in design reuse and requirements management to identify appropriate methods within each domain. A framework is proposed based on the identified requirements. The framework is then demonstrated using a case study example: vacuum pump design. Requirements are presented as a component of the integrated design knowledge framework. The proposed framework enables the application of requirements management as a dynamic process, including capture, analysis and recording of requirements. It takes account of the evolving requirements and the dynamic nature of the interaction between requirements and product structure through the various stages of product development.     

Automatic digital mood boards to connect users and designers with kansei engineering

One challenge for designers is how to express emotions to clients when helping them analyze ideas for the development of products. Mood boards, which comprise a set of images and words, are one of the most common tools for synthesizing a client's perception and instructing the designer about visual communication. The creation of these boards is time-consuming and becomes static before the end of the design process. This article investigates the possibility of building a kansei engineering system that is based on rough set probability statistics and is capable of linking kansei words obtained from clients with images that can be continuously collected online. The result is a proposal for a new kansei engineering procedure that contains five cycles and captures users' opinions in all phases of the design process. A subset of real data is used in the application of this procedure to a consumer product, which demonstrates the feasibility of this kind of application. The article presents a complete theoretical model of this system and its procedures and algorithms, which enables the creation of automatic mood boards and connects designers to users' needs.