A support vector regression based prediction model of affective responses for product form design

In this paper, a state-of-the-art machine learning approach known as support vector regression (SVR) is introduced to develop a model that predicts consumers’ affective responses (CARs) for product form design. First, pairwise adjectives were used to describe the CARs toward product samples. Second, the product form features (PFFs) were examined systematically and then stored them either as continuous or discrete attributes. The adjective evaluation data of consumers were gathered from questionnaires. Finally, prediction models based on different adjectives were constructed using SVR, which trained a series of PFFs and the average CAR rating of all the respondents. The real-coded genetic algorithm (RCGA) was used to determine the optimal training parameters of SVR. The predictive performance of the SVR with RCGA (SVR–RCGA) is compared to that of SVR with 5-fold cross-validation (SVR–5FCV) and a back-propagation neural network (BPNN) with 5-fold cross-validation (BPNN–5FCV). The experimental results using the data sets on mobile phones and electronic scooters show that SVR performs better than BPNN. Moreover, the RCGA for optimizing training parameters for SVR is more convenient for practical usage in product form design than the timeconsuming CV.     

A systematic data-mining-based methodology for product family design and product configuration

Product family design and product configuration based on data mining technology is identified as an intelligent and automated means to improve the efficiency of product development. However, few of previous literatures have proposed systematic product family design method based on data mining technology. To make up for this deficiency, this research put forward a systematic data-mining-based method for product family design and product configuration. First, the customer requirement information and product engineering information in the historical order are formatted into structural data. Second, principal component analysis is performed on historical orders to extract the customers' differentiated needs. Third, association rule algorithm is introduced to mine the rules between differentiated needs and module instances in the historical orders, thus obtained the configuration knowledge between customer needs and product engineer. Forth, the mined rules are used to construct association rule-based classifier (CBA) that is employed to sort out the best product configuration schemes as popular product variants. Fifth, sequence alignment technique is employed to identify modules for popular product variants, so that the module instances are divided into optional, common and special module, respectively, thereby the product platform is generated based on common modules. Finally, according to new customer needs, the CBA classifier is used to recommend the best configuration schemes, and then popular product variants are configured based on the product platform. The feasibility of the proposed method is demonstrated by the product family design example of desktop computer hosts.     

A quantitative aesthetic measurement method for product appearance design

Product appearance is one of the crucial factors that influence consumers’ purchase decisions. The attractiveness of product appearance is mainly determined by the inherent aesthetics of the design composition related to the arrangement of visual design elements. Hence, it is critical to study and improve the arrangement of visual design elements for product appearance design. Strategies that apply aesthetic design principles to assist designers in effectively arranging visual design elements are widely acknowledged in both academia and industry. However, applying aesthetic design principles relies heavily on the designer’s perception and experience, while it is rather challenging for novice designers. Meanwhile, it is hard to measure and quantify design aesthetics in designing artefacts when designers refer to existing successful designs. In this regard, this study aims to introduce a method that assists designers in applying aesthetic design principles to improve the attractiveness of product appearance. Furthermore, formulas for aesthetic measurement based on aesthetic design principles are also developed, and it makes an early attempt to provide quantified aesthetic measurements of design artefacts. A case study on camera design was conducted to demonstrate the merits of the proposed method where the improved strategies for the camera appearance design offer insights for concept generation in product appearance design based on aesthetic design principles.     

Comparison of multi-objective evolutionary algorithms in hybrid Kansei engineering system for product form design

Understanding the affective needs of customers is crucial to the success of product design. Hybrid Kansei engineering system (HKES) is an expert system capable of generating products in accordance with the affective responses. HKES consists of two subsystems: forward Kansei engineering system (FKES) and backward Kansei engineering system (BKES). In previous studies, HKES was based primarily on single-objective optimization, such that only one optimal design was obtained in a given simulation run. The use of multi-objective evolutionary algorithm (MOEA) in HKES was only attempted using the non-dominated sorting genetic algorithm-II (NSGA-II), such that very little work has been conducted to compare different MOEAs. In this paper, we propose an approach to HKES combining the methodologies of support vector regression (SVR) and MOEAs. In BKES, we constructed predictive models using SVR. In FKES, optimal design alternatives were generated using MOEAs. Representative designs were obtained using fuzzy c-means algorithm for clustering the Pareto front into groups. To enable comparison, we employed three typical MOEAs: NSGA-II, the Pareto envelope-based selection algorithm-II (PESA-II), and the strength Pareto evolutionary algorithm-2 (SPEA2). A case study of vase form design was provided to demonstrate the proposed approach. Our results suggest that NSGA-II has good convergence performance and hybrid performance; in contrast, SPEA2 provides the strong diversity required by designers. The proposed HKES is applicable to a wide variety of product design problems, while providing creative design ideas through the exploration of numerous Pareto optimal solutions.     

A systematic approach for coupling user satisfaction with product design

A fundamental assumption in this paper is that user satisfaction depends on product design. The approach consists of 5 steps: (1) define user satisfaction, (2) decompose product design elements, (3) conduct experiments, (4) develop relationship models, and (5) analyse critical design features. In order to demonstrate the practicability of this approach, relationship models were developed based on experimental data using a total of 60 subjects (30 American and 30 Korean subjects). In addition, critical design features and their common properties were identified for audio/visual consumer products. Similarities and differences between American and Korean consumers were discussed. The resulting relationship models can be used to predict user satisfaction and provide significant remedies for design change.     

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.     

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.     

The marketing implications of affective product design

Emotions are compelling human experiences and product designers can take advantage of this by conceptualizing emotion-engendering products that sell well in the market. This study hypothesized that product attributes influence users’ emotions and that the relationship is moderated by the adherence of these product attributes to purchase criteria. It was further hypothesized that the emotional experience of the user influences purchase intention. A laboratory study was conducted to validate the hypotheses using mobile phones as test products. Sixty-two participants were asked to assess eight phones from a display of 10 phones and indicate their emotional experiences after assessment. Results suggest that some product attributes can cause intense emotional experience. The attributes relate to the phone's dimensions and the relationship between these dimensions. The study validated the notion of integrating affect in designing products that convey users’ personalities.     

A methodology for evolutionary product design

this paper describes a function-based approach for conceptual design support in the context of evolutionary product development. The main objective is to improve a designers productivity by the effective reuse of existing design information in design alternative identification, evaluation, and modification. An integrated evolutionary design methodology, EPD, is presented. The proposed methodology divides the whole process into three inter-related phases: information recovery, information management, and information reuse. The detailed steps in each phase are elaborated, and various techniques are employed to improve information reuse efficiency. A case study on commercial electrostatic air cleaner was used to illustrate the whole process of product evolutionary design. The proposed methodology will have a positive impact on the future development of the conceptual design support system.     

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.     

A generic genetic algorithm for product family design

Product family design (PFD) has been well recognized as an effective means to satisfy diverse market niches while maintaining the economies of scale and scope. PFD essentially entails a configuration problem by “combination," where combinatorial explosion always occurs and is known to be mathematically intractable or NP-hard. Although genetic algorithms (GAs) have been proven to excel in solving combinatorial optimization problems, it is difficult to adopt the traditional GA to deal with the complex data and interrelationships inherent in the PFD problem. This paper proposes a generic genetic algorithm (GGA) for PFD. A generic encoding scheme is developed to adapt to diverse PFD scenarios. A hybrid constraint-handling strategy is proposed to handle complex and distinguishing constraints at different stages along the evolutionary process. The design and implementation procedures of the GGA are discussed in detail. An application of the proposed GGA to motor family design is reported. The GGA efficiency is also tested through efficiency analysis in terms of the probability of generating feasible solutions, as well as through analysis of the GGA complexity.     

A product design method for form and color matching based on aesthetic theory

Form and color matching design are vital links in product design. Recently, many scholars have focused on quantifying visual aesthetics to improve design efficiency in form or color matching design activities. However, few scholars have considered both form and color matching. Therefore, we propose a more comprehensive and practical design model based on the aesthetics theory of form and color matching. This model includes 3 phases: preparation (Phase I), form design (Phase II), and color matching design (Phase III). In Phase I, the target product's functional system and design objectives are clarified. In Phase II, the spatial layouts of the target product are analysed, and several reasonable layouts are designed in detail. Finally, the form with the highest aesthetic measurement is selected from alternatives using the modified equilibrium formula and questionnaire. In Phase III, a group of colors conforming to the target imagery is selected from a color palette. Meanwhile, a set of appropriate observation angles are chosen using an expert questionnaire. Subsequently, three-color combinations with higher color harmony are filled into images with different observation angles. Finally, the aesthetics of color matching schemes are obtained using the formula of color matching, and the scheme with a higher average value of the aesthetic measurement from all observation angles is considered the best scheme. This paper takes the fresh food vehicle and leafless fan as the cases. The best form and color matching scheme are obtained using the formula of aesthetic measurement and verified with an expert questionnaire. The result shows consistency between the results of the quantization formula and the expert questionnaire, which confirms the effectiveness of this design model. The proposed design model could improve the design efficiency and ensure the products' visual aesthetics.     

Optimal platform investment for product family design

Existing models for developing modular product families based on a common platform are either too engineering oriented or too marketing centric. In this paper, we propose an intermediate modeling ground that bridges this gap by simultaneously considering essential concepts from engineering and marketing to construct an alternative model for platform-based product families. In this model, each variant (in the platform-based product family) contributes a percentage to overall market coverage inside a target market segment. The extent to which a specific variant contributes to market coverage is linked to its degree of distinctiveness. On the other hand the cost of development of all variants (that constitute the product family) is also dependent on the degree of commonality between these variants. The objective of the model is to maximize market coverage subject to an available development budget. Based on a conceptual design of the product family, the proposed model suggests the optimal initial investment in the platform, the commonality level between variants, and the number of variants to be produced in order to maximize market coverage using both analytical and simulation techniques. An application example using an ice scraper product family is included to demonstrate the proposed model.     

A CSP approach for the network of product lifecycle constraints consistency in a collaborative design context

In this paper, we are considering that the design process can be modelled in the form of a constraint satisfaction problem (CSP). CSP modelling or resolution has proved its efficiency within the framework of single-designer design. We propose to extend the functions of CSP to the context of multi-concept design of the same artefact. We define CoCSP as cooperative constraint satisfaction problem including the actors of the design problem. We are presenting the operating principles of an algorithm for the real-time management of design decisions, based on a model described in the form of a CoCSP for the integration of supply-chain constraints. This algorithm enables the number of design decisions rejected at a given moment in design to be kept to a minimum. The algorithm forms the core of a prototype for an unsupervised, generic constraint-based collaborative design system. Our aim is to produce a platform centred on the notion of constraints that will enable a product design problem to be modelled and solved by integrating supply-chain constraints as far upstream as possible.     

A knowledge-based master model approach exemplified with jet engine structural design

Successful product development requires the consideration of multiple engineering disciplines and the quantification of tradeoffs among conflicting objectives from the very early design phases. The single-largest challenge to do so is the lack of detailed design information. A possible remedy of this issue is knowledge-based engineering. This paper presents a knowledge-based master model approach that enables the management of concurrent design and analysis models within different engineering disciplines in relation to the same governing product definition. The approach is exemplified on an early phase structural design of a turbo-fan jet engine. The model allows geometric-, structural mechanics- and rotor-dynamic- models to be concurrently integrated into a multi-disciplinary design and optimization loop.     

A computational framework for authoring and searching product design specifications

The development of product design specifications (PDS) is an important part of the product development process. Incompleteness, ambiguity, or inconsistency in the PDS can lead to problems during the design process and may require unnecessary design iterations. This generally results in increased design time and cost. Currently, in many organizations, PDS are written using word processors. Since documents written by different authors can be inconsistent in style and word choice, it is difficult to automatically search for specific requirements. Moreover, this approach does not allow the possibility of automated design verification and validation against the design requirements and specifications.In this paper, we present a computational framework and a software tool based on this framework for writing, annotating, and searching computer-interpretable PDS. Our approach allows authors to write requirement statements in natural language to be consistent with the existing authoring practice. However, using mathematical expressions, keywords from predefined taxonomies, and other metadata the author of PDS can then annotate different parts of the requirement statements. This approach provides unambiguous meaning to the information contained in PDS, and helps to eliminate mistakes later in the process when designers must interpret requirements. Our approach also enables users to construct a new PDS document from the results of the search for requirements of similar devices and in similar contexts. This capability speeds up the process of creating PDS and helps authors write more detailed documents by utilizing previous, well written PDS documents. Our approach also enables checking for internal inconsistencies in the requirement statements.     

Economical green product design based on simplified computer-aided product structure variation

Environmental issues have become an imperative concern for most companies in relation to modern product development. Special procedures have to be taken during the product development process to comply with recent green directives. Product structure is recognized as a critical factor that provides effective means for reducing environmental impact in product end-of-life. However, most previous studies failed to leverage the vast latitude at the design stage due to the assumption of a fixed product structure. To overcome this deficiency, we propose a CAD-based approach that allows automatic variation of 3D product structure by means of changing the combination of parts, selecting the assembly method, and rearranging the assembly sequence. A computing scheme uses Genetic Algorithm (GA) techniques to produce an optimal product structure from the design alternatives generated by the approach. This corresponds to lower assembly/disassembly costs, while complying with specified recycling and recovering rates. The scheme also chooses a smaller set of parts that needs to be disassembled and determines an economical disassembly process. Implemented in a commercial CAD system, the test results demonstrated the effectiveness of this scheme in green product design in a cost-effective manner.     

A digital twin-enhanced system for engineering product family design and optimization

Engineering product family design and optimization in complex environments has been a major bottleneck in today’s industrial transformation towards smart manufacturing. Digital twin (DT), as a core part of cyber-physical system (CPS), can provide decision support to enhance engineering product lifecycle management workflows via remote monitoring and control, high-fidelity simulation, and solution generation functionalities. Although many studies have proven DT to be highly suited for industry needs, little has been reported on the product family design and optimization capabilities specifically with context awareness, which could be leaving many enterprises ambivalent on its adoption. To fill this gap, a reusable and transparent DT capable of situational recognition and self-correction is essentially required. This paper develops a generic DT architecture reference model to enable the context-aware product family design optimization process in a cost-effective manner. A case study featuring asset re-/configuration within a dynamic environment is further described to demonstrate its in-context decision-aiding capabilities. The authors hope this study can provide valuable insights to both academia and industry in improving their engineering product family management process.     

A constraint-based operation sequencing for a knowledge-based process planning

Process planning is a decision-making process. Decisions on machining operations for a particular feature have to be made on various independent conditions such as which operation should be performed with which tools and under what cutting parameters. An integrated knowledge-based CAPP system called ProPlanner has been developed. The system has five modules namely information acquisition, feature recognition, machining operation planning and tool selection, set-up planning, and operation sequencing. Most process-planning systems do not produce alternative process plans. Usually, a fixed sequence created by a process plan is not necessarily the best possible sequence. Therefore, the aim should be to generate all possible operation sequences and use some optimality criteria to obtain the best sequence for the given operating environment. This paper presents an efficient heuristic algorithm, belongs to the system's operation sequencing module, for finding near-optimal operation sequences from all available process plans in a machining set-up. The costs of the various machining schemes are calculated and the machining scheme with the lowest cost is chosen. All feasible cutting tools are identified for each particular feature and the corresponding machining operations. This process is repeated for all the features in the machining set-up. All possible feature sequence combinations allowed by the current feature constraints are then generated. Appropriate cutting tools are identified and assigned to different operations. The feature sequence with the smallest number of tool changes is adopted.     

A fuzzy-based decision-making method for evaluating product discontinuity at the product transition point

Companies have been striving to achieve product innovation and cost savings to keep up with a rapidly changing manufacturing environment, as well as to deliver new products just in time and earlier than their competitors. Their strategic goals include effective decision making with regard to product life cycles, predicting market demand, and timing new product launches through the optimization of demand and supply of products. However, most products that enter a decline stage experience a decline in sales, which in turn causes an increase in warehouse costs, and a decline in competitiveness, owing to a delay in the release of new products. In this paper, we propose a fuzzy-based decision-making method to effectively evaluate products to be discontinued at the product transition point after taking into account market uncertainties and characteristics of businesses in companies. Therefore, we conduct the matrix analysis and the Pareto analysis for quantitative evaluation of four stages of the product life cycle based on market demand information for target product groups, apply the results of this analysis to the fuzzy-based qualitative evaluation model, and subsequently infer the discontinuity priority for product models that are expected to be replaced by new products. Furthermore, we develop the PTP portfolio system to identify a list of product models to be discontinued, as well as to review their percentage of sales contribution with respect to the overall product sales of the company.