A multi-objective genetic algorithm approach to rule mining for affective product design

A novel multi-objective genetic algorithm (GA)-based rule-mining method for affective product design is proposed to discover a set of rules relating design attributes with customer evaluation based on survey data. The proposed method can generate approximate rules to consider the ambiguity of customer assessments. The generated rules can be used to determine the lower and upper limits of the affective effect of design patterns. For a rule-mining problem, the proposed multi-objective GA approach could simultaneously consider the accuracy, comprehensibility, and definability of approximate rules. In addition, the proposed approach can deal with categorical attributes and quantitative attributes, and determine the interval of quantitative attributes. Categorical and quantitative attributes in affective product design should be considered because they are commonly used to define the design profile of a product. In this paper, a two-stage rule-mining approach is proposed to generate rules with a simple chromosome design in the first stage of rule mining. In the second stage of rule mining, entire rule sets are refined to determine solutions considering rule interaction. A case study on mobile phones is used to demonstrate and validate the performance of the proposed rule-mining method. The method can discover rule sets with good support and coverage rates from the survey data.     

An integrated component design approach to the development of a design information system for customer-oriented product design

In product design, the designer must draw upon many different types of information including both product-user requirements and design developments in related fields. As each designer has his/her own subjective opinion, an appropriate link between the user requirements and design features must be carefully developed to generate the most suitable design recommendation.The objective of this research is to develop a component design approach for the generation and evaluation of feasible design alternatives to help designers make the most effective design decisions. The development of the approach is based on the concept of component design and interaction links between user requirements and design output parameter values so that the design alternatives can be generated. A confidence interval for order statistics is then used to determine if enough unique feasible design alternatives have been generated. The research is presented in conjunction with a ballpoint pen design to demonstrate the feasibility and advantages of this approach.The results should provide designers with a useful method for the development of a design information system that will efficiently generate and evaluate a set of feasible design alternatives in product design process.     

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.     

A function-oriented biologically analogical approach for constructing the design concept of smart product in Industry 4.0

The development of the Industry 4.0 paradigm and the advancement of information technology have aroused new consumer requirements for smart products that are capable of context awareness and autonomous control. Nature holds huge potential for inspiring innovative design concepts that can meet the ever-growing need for smart products since biology perceive and interact with their living environment for survival. However, to date, very few studies have explored the application of natural wisdom in building innovative design concepts for smart products. This paper proposes a function-oriented design approach for smart products, by analogizing to biological prototypes. To do so, a unified functional representation, based on the Function–behavior–structure (FBS) ontology, is proposed to abstract biological prototypes, followed by a fuzzy triangular numbers-based algorithm designed to locate appropriate biological prototypes as analogical sources for smart product development. Moreover, functional innovative strategies and a hybrid design process are formulated to develop design concepts of smart products, by integrating several existing engineering design methods. Finally, an illustrative design case of a smart natural resource collecting system is used to demonstrate the workability of the proposed method.     

Machine learning and CBR integrated mechanical product design approach

Many works based on case-based reasoning (CBR) and rule-based reasoning (RBR) have been done to obtain a product concept intelligently, but researches on automatic and intelligent methods for the detailed design process are still lacking. To achieve the intelligent design of the whole process for mechanical product development, an approach based on cases and knowledge is proposed. First, considering that the actual product cases sample is small, correlations among product features and relationships between product features and requirements are evaluated based on design knowledge. According to these correlations and relationships, a design problem is decomposed into multiple parallel small-scale sub-problems not only to increase data samples but reduce data dimensions. Moreover, a hybrid method that combined Hamming distance with Euclidean distance is generated to retrieve similar cases, which can address both numerical and encoding factors. In addition, K-Nearest-Neighbor (KNN) algorithm and Linear regression are adopted to adapt the classification and numeric parameters, respectively, so that the detailed design process can be achieved without fixed knowledge template. Finally, a tool design software is developed based on Visual studio 2015 to verify the practicability of the proposed method.     

A smart conflict resolution model using multi-layer knowledge graph for conceptual design

Reducing the impact of conflicts on requirement-function-structure mapping in the early stage of product design is an important measure to achieve conceptual innovation, which relies on accurate reasoning of multi-domain knowledge. As product requirements become more personalized and diverse, traditional discrete knowledge organization and reasoning methods are difficult to adapt to the challenges of continuity and precision in conceptual solution. Knowledge graphs with complex networks have obvious advantages in association detection, knowledge visualization, and explainable reasoning of implicit knowledge, which offer innovative opportunities for conflict resolution in conceptual design. Therefore, a smart conflict resolution model using a multi-layer Knowledge Graph for Conceptual Design(mKGCD) is proposed in this study. A knowledge expression form of FBS-oriented design patent vocabulary is proposed, which is used for knowledge entity recognition and relation extraction based on natural language processing. A label mapping method based on inventive principles is used for patent classification and a four-layer semantic network for conflict resolution is constructed. Through semantic distance calculation, the designer's requirements for function/behavior/structure are smart deployed to obtain appropriate knowledge. A case study of the conceptual design of a collapsible installation and handling equipment demonstrates the feasibility of the proposed approach. The proposed method can not only meet the functional solution and innovation in the context of different design requirements, but also effectively improve the design efficiency in the iterative design process by means of multiple meanings of one graph.     

Supporting resilient conceptual design using functional decomposition and conflict resolution

Concept generation plays a critical role in product design especially in the early design phase when functional requirements are either uncertain or partially known. To cope with the uncertainty imposed by the varying work environment, resilient design has often been called upon to leverage system capacity from the perspective of functional requirements. While the resilient design has been advanced recently, how to design a resilient product structure so to accommodate more functional requirements at the conceptual design phase remains challenging. To tackle this issue, we propose a methodology to support resilient conceptual design using functional decomposition and conflict resolution. To reduce the impact imposed by potential issues and uncertain environments, the overall system functional requirements are defined based on five principles of resilient design. The redundant conceptual scheme that meets functional requirements is given resilience through functional decomposition and conflict resolution. A case study of the conceptual design of a hard rock coring structure present to demonstrate the feasibility of the proposed approach. The results yielded have suggested that the proposed approach can not only support resilient conceptual design, but also leverage design reliability and robustness under uncertainty, especially in terms of varying work environments.     

Green product design through product modularization using atomic theory

With increasing environmental consciousness and the establishment of environmental protection regulations, green product design not only plays a crucial role in a modern industry but is also becoming the main focus of the future market. In this paper, an innovative method is presented that uses the concepts of atomic theory to solve design modularization problems for green product design. With the developed method, products can be modularized based upon given green constraints, e.g., material compatibility, part recyclability, and part disassemblability. The developed method can help engineers effectively create green designs in the initial design stage, based on product lifecycle requirements. With green considerations incorporated into new modules, a new design can be created that improves upon an original design, with respect to environmental impacts. Product designers can use our method to compare differences between their original designs and the new green modules and then perform necessary design modifications. A table lamp and a motor are used as case study examples to show the effectiveness of the atomic-theory-based green product design method.     

Developing a quantitative intelligent system for implementing concurrent engineering design

This research focuses on the development of a quantitative intelligent system for implementing concurrent engineering design. The paper first discusses the task of concurrent engineering design and the basic requirements for conducting integrated concurrent engineering design. The proposed quantitative intelligent system approach combines qualitative reasoning, based upon design and manufacturing knowledge, and quantitative evaluation and optimization, conducted using design information and manufacturing data generated in the knowledge-based reasoning. The method allows considerations on non-operating principle aspects of a product to be incorporated into the design phase, such as manufacturing, maintenance, service, recycle, etc., with an emphasis on production costs. The proposed method serves as a convenient software tool for gathering information required in the concurrent engineering design process and integrates tasks from different parts of the product development life cycle, particularly function design, manufacturability analysis and production cost estimation. A prototype software system is developed based upon this method using Smalltalk-80. In the prototype system, concurrent engineering design is carried out by: (1) describing and representing design requirements; (2) generating feasible design candidates and evaluating their design functions; (3) representing design geometry; (4) finding the associated production processes and predicting the production costs of each feasible design; and (5) identifying the costeffective design that satisfies given design requirements and requires minimum production costs.     

A product configuration analysis method for emotional design using a personal construct theory

Identifying emotion-related product attributes (perceived by consumers) is no easy task in the realm of emotional design. Conventionally, this process relies heavily on the researchers who conduct the Kansei experiments selecting product attributes such as color, form, and texture for Kansei studies. However, in so doing, other product attributes that also play a vital role in product-emotion associations might be neglected by the researchers. More importantly, the identification of product attributes should be based on consumer's point of view (and feelings). Accordingly, a personal construct theory based product configuration analysis method is proposed in this work. The method develops the customer's mind map for each Kansei tag in order to capture replications of candidate products. A means-value chain is used to generate targets which are later compared with candidate products by consumers. The comparison results could suggest product attributes that are relevant to the desired Kansei. The proposed approach is presented and illustrated using a case study of Graffiti designs on notebooks. Results obtained are discussed. It appears that the proposed method is promising in identifying product attributes with desired Kansei impacts.     

Product form design using customer perception evaluation by a combined superellipse fitting and ANN approach

Product aesthetics plays an important role in new product design and development. Product form can deliver product images and affect customer’s impression to a product. However, it is usually difficult to apply conventional approaches to represent the product form precisely and effectively for modeling the relationship between product image and customer perception. The objective of this work is to develop a computational technique for product aesthetics design so that customer perception can be taken into product form design in a more systematic and intelligent manner. To achieve this aim, a novel parametric approach is proposed to introduce design parameters such as line, size, and ratio into product design model and the technique of generalized superellipse fitting is adopted to describe the outline pattern of a product. Since customer perception on a product is highly non-linear and very difficult to be described by any traditional mathematical approaches, an artificial neural network (ANN) model is therefore established to relate the design parameters and the perceptual values for the design of a new product. A case study of mobile phone design, in which twelve numerical parameters are defined for the conceptual model, has been conducted to explain the implementation of the proposed approach. A three-layered perceptron ANN model is developed to predict the perceptual values of stylishness based on a survey using 32 mobile phone samples. The results of the case study illustrate that the proposed approach can successfully generate an optimum design of a mobile phone by applying a genetic algorithm (GA) on the trained ANN model.     

Creative design through knowledge clustering and case-based reasoning

New product design is inspired by the existing design. The clustering of similar design cases therefore enhances new product development (NPD). At the beginning of NPD, the success of creative design highly depends on the designers’ subjective judgments and try-and-error attempts due to its very obscure prospect. To facilitate an efficient approach for generating creative ideas, this paper proposes a new design method by integrating fuzzy relational analysis, case-based reasoning (CBR) and C-K theory. The proposed design method involves four specific sections: design criteria importance ranking; similarity measurement for design knowledge; knowledge clustering method for innovation and a step-by-step design algorithm. Moreover, a new battery buckling machinery is used as a empirical study to verify the workability of the proposed method. The contributed method shows its advantages to cultivate the inspirations from the existing design and generate creative design concepts from knowledge combination.     

Constraint-based functional design verification for conceptual design

In the early stages of mechanical product design, designers not only need to determine the physical structure of the design, but also need to verify that the design functions properly with the allowable values or ranges of values of the relevant design attributes. Existing work on design verification is either aimed at specific design problems, which are generally carried out at the downstream design stages, or aimed at deriving design behavior using a behavioral simulation approach. Functional design verification has largely been neglected by the research society. To tackle this problem, we propose a generic constraint-based approach that is based on a comprehensive functional design model. A number of strategies are proposed for the approach, including strategies for design variables reduction, variable dependency graph development, constraint propagation, and dynamic verification of a design over an assigned set of attributes (variables). The approach is implemented as part of a functional modeling design environment. A simple design verification case is presented to illustrate our approach.     

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.     

Sequential optimization and reliability assessment for multidisciplinary systems design

With higher reliability and safety requirements, reliability-based design has been increasingly applied in multidisciplinary design optimization (MDO). A direct integration of reliability-based design and MDO may present tremendous implementation and numerical difficulties. In this work, a methodology of sequential optimization and reliability assessment for MDO is proposed to improve the efficiency of reliability-based MDO. The central idea is to decouple the reliability analysis from MDO with sequential cycles of reliability analysis and deterministic MDO. The reliability analysis is based on the first-order reliability method (FORM). In the proposed method, the reliability analysis and the deterministic MDO use two MDO strategies, the multidisciplinary feasible approach and the individual disciplinary feasible approach. The effectiveness of the proposed method is illustrated with two example problems.     

Design as integration of axiomatic design and design structure matrix

Axiomatic design and design structure matrix (DSM) are two popular design methods at the moment, while most related researches only apply the basic ideas of axiomatic design or DSM to some use cases. This paper analyses the disadvantages of both axiomatic design and DSM. The axiomatic design method guides the designer finding suitable design parameters to meet the needs of function requirements. But axiomatic design cannot support the designer to know the interactions amongst the design parameters, including geometry, spatial layout, interfaces (e.g. logical and physical connectivity), which will decide the quality of the design. DSM has the advantages at recording and analysing the interaction relationship between existing product elements. However, at the conceptual design stage or for a new product that has never been designed before, it is difficult to make the DSM. After deep investigations, it has been found that there are strong complementarities between axiomatic design and DSM, and integration of both sides is advocated for better-quality design. The main outcome of this work is the formal interpretation of the integration logic between axiomatic design and DSM. Under such integration context, the conceptual design process can be seen as a recursive interaction of axiomatic design's design matrix (DM) and corresponding DSM. In this way, axiomatic design and DSM can benefit from each other. A computer-aided conceptual design system has been developed to realize the proposed integration model of axiomatic design and DSM.     

Semantic Web based innovative design knowledge modeling for collaborative design

This paper describes a study about how to use the Semantic Web technologies for innovative design knowledge modeling in a multi-agent distributed design environment. Semantic Web based knowledge modeling for innovative design is proposed as prelude to the meaningful agent communication and knowledge reuse for collaborative work among multidisciplinary organizations. A model for innovative design is proposed at first, based on which a knowledge schema is brought forward. For sharing the design knowledge among an internet-based or distributed work team, even globally, A RDF-based knowledge model is presented to realize its representation on Semantic Web. A Semantic Web based repository for innovative design and its API for topper Semantic Web applications have been also constructed. The proposed knowledge modeling extends traditional product modeling with capabilities of innovative design, knowledge sharing and distributed problem solving, and is employed as a content language within the messages in the proposed multi-agent system architecture. The proposed approach is viewed as a promising knowledge management method that facilitates the implementation of computer supported cooperative work in innovative design of Semantic Web applications.     

A generative design technique for exploring shape variations

Because innovative and creative design is essential to a successful product, this work brings the benefits of generative design in the conceptual phase of the product development process so that designers/engineers can effectively explore and create ingenious designs and make better design decisions. We proposed a state-of-the-art generative design technique (GDT), called Space-filling-GDT (Sf-GDT), for the creation of innovative designs. The proposed Sf-GDT has the ability to create variant optimal design alternatives for a given computer-aided design (CAD) model. An effective GDT should generate design alternatives that cover the entire design space. Toward that end, the criterion of space-filling is utilized, which uniformly distribute designs in the design space thereby giving a designer a better understanding of possible design options. To avoid creating similar designs, a weighted-grid-search approach is developed and integrated into the Sf-GDT. One of the core contributions of this work lies in the ability of Sf-GDT to explore hybrid design spaces consisting of both continuous and discrete parameters either with or without geometric constraints. A parameter-free optimization technique, called Jaya algorithm, is integrated into the Sf-GDT to generate optimal designs. Three different design parameterization and space formulation strategies; explicit, interactive, and autonomous, are proposed to set up a promising search region(s) for optimization. Two user interfaces; a web-based and a Windows-based, are also developed to utilize Sf-GDT with the existing CAD software having parametric design abilities. Based on the experiments in this study, Sf-GDT can generate creative design alternatives for a given model and outperforms existing state-of-the-art techniques.     

Dynamical mining of ever-changing user requirements: A product design and improvement perspective

Previous studies carried out customer surveys by questionnaires to collect data for analyzing consumer requirements. In recent years, a large and growing body of literature has investigated the extraction of customer requirements and preferences from online reviews. However, since customer requirements change dynamically over time, traditional studies failed to obtain the change data of customer requirements and opinions based on sentiments expressed in reviews. In this paper, a new method for dynamically mining user requirements is proposed, which is used to analyze the changing behavior of product attributes and improve product design. Dynamic mining differs from the traditional need acquisition mainly in three aspects: (1) it involves dynamically mining user requirements over time (2) it adds changes in manufacturers’ opinions to the analysis (3) it allows for product improvement strategies based on the changing behavior of product attributes. First, text mining is adopted to collect customer and manufacturer review data for different time periods and extract product attributes. A Natural Language Processing tool is used to measure the importance weight and sentiment score of product attributes. Second, an approach for dynamically mining user requirements is introduced to classify product attributes and analyze the changes of attribute data in three categories over time. Finally, an improvement strategy for next-generation product design is developed based on the changing behavior of attributes. Moreover, a case study on vehicles based on online reviews was conducted to illustrate the proposed methodology. Our research suggests that the proposed approach can accurately mine customer requirements and lead to successful product improvement strategies for next-generation products.