Function Behavior Structure Research Articles

Research on the design of growable children’s beds based on combined hierarchical analyses

Although the market share of domestic children’s furniture is increasing annually, some potential problems limit its long-term and stable development, and there is still a gap in China compared with foreign countries. This study focused on the demand preferences for growable children’s beds and examined the design features that influence these preferences. This study introduces a combination of Hierarchical Analyses (AHP), Quality Function Development (QFD), and the Platts Conceptual Decision Matrix (PUGH) into the innovative design of a research model for children’s furniture (AHP-QFD-PUGH). This study screened and classified the decision-making indicators obtained from the research, ranked their importance by quantitative calculation, and finally proposed an optimal design solution. Additionally, to further study the structural characteristics, the function-behavior-structure (FBS) model served as a supplementary analysis tool to effectively circumvent subjective factors in product design. This integrated model accurately explored user needs and product characteristics, providing substantial guidance and new ideas for optimizing the design of growable children’s beds and enhancing growth of the children’s furniture industry.

Research on the application of AHP-FAST-FBS in the design of home entrance disinfection devices in the post-pandemic era

With the outbreak and continued spread of the COVID-19 pandemic, people's demand for daily disinfection products has increased rapidly, and its innovative design has received widespread attention. In this context, this study aims to propose a design methodology for home entrance disinfection devices based on AHP-FAST-FBS. Firstly, the design requirements of the home entrance disinfection device were collected and analyzed through in-depth interviews and the KJ method, and a hierarchical model of design demand indicators was constructed. Secondly, the Analytical Hierarchy Process (AHP) was employed to quantify these design demand indicators, and core design demands for home entrance disinfection devices were identified by weight calculations. On this basis, the Functional Analysis System Technique (FAST) method was combined to rationally transform the design demands into product functional indicators, constructing a functional system model for the home entrance disinfection device through systematic decomposition and categorization. Lastly, based on the Function-Behavior-Structure (FBS) theoretical model, the mapping of each function of the product to its structure was realized, the product structure modules were determined, and the comprehensive design and output of the innovative design scheme for the home entrance disinfection device were completed. The results of this study indicate that the design methodology combining AHP-FAST-FBS can effectively improve the scientific rigor and effectiveness of the home entrance disinfection device design, thereby generating an ideal product design scheme. This study provides systematic theoretical guidance and practical reference for designers of subsequent related disinfection products and also offers a new path for improving social health and safety.

A closed-loop human-computer interactive design method based on sequential human intention prediction and knowledge recommendation

Designers working on large-scale complex engineering projects may encounter design errors due to limited experience and expertise. Therefore, during the design process, accurately identifying the designer’s design intention and recommending relevant design knowledge can enhance design efficiency and reduce errors. In this paper, we propose a closed-loop human–computer interactive design method based on sequential human intention prediction and knowledge recommendation. This method leverages the Function-Behaviour-Structure (FBS) model to reduce the dimensionality of design action sequences, so as to facilitate the analysis of potential design patterns. The processed action sequences are used to train Transformer to predict design intentions. In different input sequences, Transformer achieved a highest prediction accuracy of 92.09%. We construct a Design Knowledge Recommendation Framework (DKRF) and its corresponding design knowledge matching algorithm. This framework accurately recommends design knowledge to designers, solving design stagnation and improving design efficiency. Finally, a case study of four types of mechanical model design is conducted to demonstrate the feasibility and wide applicability of the proposed closed-loop human–computer interactive design method.

Towards Controllable Generative Design: A Conceptual Design Generation Approach Leveraging the FBS Ontology and Large Language Models

Abstract Recent research in the field of design engineering is primarily focusing on using AI technologies such as Large Language Models (LLMs) to assist early-stage design. The engineer or designer can use LLMs to explore, validate and compare thousands of generated conceptual stimuli and make final choices. This was seen as a significant stride in advancing the status of the generative approach in computer-aided design. However, it is often difficult to instruct LLMs to obtain novel conceptual solutions and requirement-compliant in real design tasks, due to the lack of transparency and insufficient controllability of LLMs. This study presents an approach to leverage LLMs to infer Function-Behavior-Structure (FBS) ontology for high-quality design concepts. Prompting design based on the FBS model decomposes the design task into three sub-tasks including functional, behavioral, and structural reasoning. In each sub-task, prompting templates and specification signifiers are specified to guide the LLMs to generate concepts. User can determine the selected concepts by judging and evaluating the generated function-structure pairs. A comparative experiment has been conducted to evaluate the concept generation approach. According to the concept evaluation results, our approach achieves the highest scores in concept evaluation, and the generated concepts are more novel, useful, functional, and low-cost compared to the baseline.

DesignFusion: Integrating Generative Models for Conceptual Design Enrichment

Abstract Conceptual design is a pivotal phase of product design and development, encompassing user requirement exploration and informed solution generation. Recent generative models with their powerful content generation capabilities have been applied to conceptual design to support designers’ ideation. However, the lack of transparency in their generation process and the shallow nature of their generated solutions constrain their performance in complex conceptual design tasks. In this study, we first introduce a conceptual design generation approach that combines generative models with classic design theory. This approach decomposes the conceptual design task based on design process and design attributes, and uses the who, what, where, when, why, how (5W1H) method, function-behavior-structure model, and Kansei Engineering to guide generative models to generate conceptual design solutions through multi-step reasoning. Then we present an interactive system using a mind-map layout to visualize multi-step reasoning, called DesignFusion. This empowers designers to track the generation process and control inputs/outputs at each reasoning step. Two user studies show that our approach significantly enhances the quality of generated design solutions and enriches designer experience in human–artificial intelligence co-creation.

A constraint-driven conceptual design approach for product based on function-behavior-structure design process

Conceptual design is an important part of new product development (NPD) since it relies on performance, quality, and cost restrictions from users during the design process, and it has a direct impact on downstream design activities. However, in the function-behavior-structure (FBS) design model, constraints are only frequently used in the scheme evaluation or detailed optimization stage, ignoring the influence of various constraints in the function solution process, which leads to repeated revisions of design schemes. In addition, the expression of constraints in NPD is focused on the abstraction of qualitative requirements, and there is no regular classification and definition. Aiming at the above defects, a constraint-driven product conceptual design model based on FBS model is proposed. First, a constraint-driven FBS conceptual design model is proposed as an important theoretical basis for subsequent design. Second, FBS mapping is driven by constraints, and function-behavior association matrix under functional constraints and behavior-structure association matrix under behavioral constraints are established to obtain feasible principle solutions. Third, the principle solutions are combined according to the compatibility constraints, and the constraint evaluation criteria are integrated with the technology for order preference by similarity to an ideal solution (TOPSIS) to obtain the optimal conceptual scheme (CS). To aid the design process, a prototype solving CSs based on design constraints system is developed to assist designers to manage constraints and improve evaluation efficiency. Finally, a case study of the high-speed blender scheme is used to validate the proposed approach and the optimal CS driving by constraint is solved, then the compared results and prototype system showed the applicability of the proposed approach.

An Extended Function-Behaviour-Structure Ontology to support FMEA within a System Engineering Context

Failure Mode and Effects Analysis is a core tool for the systems engineering design assurance, facilitating the identification of design risks and their management throughout the lifecycle of the system. However, the development and management of the FMEA within a systems engineering context faces many challenges, including effectiveness of the process and integrity of knowledge capture, and the lack of a consistent integration with the systems lifecycle. The research underpinning this contribution aims to establish an intelligent FMEA environment supported by an ontology as the basis for knowledge representation of complex systems function failure underpinning the FMEA analysis and documentation. We propose to extend the Function-Behaviour-Structure (FBS) ontology of system design, with representation of function failure reasoning supported by a function failure taxonomy. This provides a consistent ontological basis underpinning the FMEA analysis of a system. We also discuss the expansion of the ontology with the levels of design decomposition of a complex system, to support the management of traceability of the FMEA across the design process. We illustrate the deployment of the proposed ontology framework, implemented in Protégé environment.

Multicriteria requirement ranking based on uncertain knowledge representation and reasoning

The abundance of complex natural language descriptions related to requirements data presents major challenges for requirement analysis. Knowledge graphs (KGs), as the latest achievement in symbolic studies, are widely used in various fields due to their rich semantic expressive abilities. However, existing methods that mine resource description framework (RDF) triples in classical KGs cannot effectively capture the fuzzy semantic information in the product development system. To address this, we propose a multicriteria requirement ranking method based on uncertain knowledge representation and reasoning (KRR). First, we defined a model for representing uncertain knowledge to organize diverse data from multiple sources. This process was accomplished by constructing the requirement ontology, which is based on the function-behaviour-structure (FBS) model and requirements modelling-related documents. Next, a knowledge representation approach named the fuzzy requirement knowledge graph (FRKG) was devised by combining attribute confidence and predicate fuzziness. Then, knowledge reasoning rules were designed to enhance the edges in the FRKG, unveiling potential relationships between nodes. Utilizing the enriched FRKG (EFRKG), we proposed a multicriteria requirement ranking method based on grey relational analysis (GRA). To validate the effectiveness of the proposed approach, we conducted a case study involving unmanned aerial vehicles (UAVs). Furthermore, the semantic extension capability of FRKG was evaluated, and a comparison with traditional multicriteria requirement ranking methods was performed to demonstrate the efficiency of the proposed approach from both perspectives.

Development of an agent-based model incorporating Function–Behavior–Structure framework to enable systems engineering design process evaluation

An important step in the development of any process is evaluation. Evaluation ensures that the process affects effectiveness, efficiency, or other metrics as designed. Without evaluation, processes must stand solely on the theories, axioms, and/or heuristics that formed them. Outcome or objectives-based evaluations are especially useful in seeing if the expected impact is realized. Systems and design engineering present problems for outcome evaluations due to system design efforts being long in duration, expensive, organization/team specific, and environment/context specific. These characteristics make repeating the same effort under the same conditions using different processes difficult, if not impossible. While a comparison of processes for evaluation in a real application may not be feasible, a simulation of the processes with agents that capture system design behaviors may produce findings to support hypotheses. This paper examines the use of agent-based modeling and simulation to compare pseudo-waterfall and pseudo-agile engineering processes for a simple design problem. The Function–Behavior–Structure (FBS) model of design is used along with empirical data from FBS studies to examine the performance of a two-person design team using pseudo-waterfall and pseudo-agile engineering processes. The results of this exercise show possible advantages of agile-like processes in total time to complete the design.

K-Means Module Division Method of FDM3D Printer-Based Function–Behavior–Structure Mapping

Product performance, function, cost, and the level of module generalization are all significantly influenced by product modular design, but different goods require different division indicators and techniques. The purpose of this study is to provide a set of appropriate modular division techniques for FDM 3D printers. This research offers an ecologically friendly module division index and uses module clustering as the module division principle in accordance with the current industrial development trend and the fundamental requirements of FDM 3D printer consumers in the current market. The K-means algorithm is used to use the Jaccard similarity coefficient as the metric of similarity of the DSM clustering process to realize the module division of the FDM 3D printer after studying the function–behavior–structure mapping model of the 3D printer. Additionally, the elbow method–cluster error variance and average contour coefficient evaluation systems were built, respectively, in order to verify the viability of the FDM 3D printer module division method and obtain the best module division results. By analyzing these two systems, it was discovered that when the FDM 3D printer was divided into three modules, the in-cluster error variance diagram obviously had an inflection point, and the average profile coefficient and other modular approaches that need to be adjusted to their respective goods can use this division method as a theoretical foundation and point of reference.

MODELLING THE DESIGN OF MODELS: AN EXAMPLE USING CRISP-DM

AbstractDesign is widely understood as a domain-independent notion, comprising any activity concerned with creating artefacts. This paper shows that models can be viewed as artefacts, and that the design of models resembles the design of artefacts in other domains. The function-behaviour-structure (FBS) ontology of design is applied to models, mapping generic characteristics of models derived from literature on modelling onto basic, design-ontological categories. An example of model design, namely the CRISP-DM model for designing data mining models, is analysed and compared with models of designing in other domains (systems engineering, mechanical engineering, software engineering, and service design). The results show that there are fundamental commonalities but also differences, revealing the need for further research in developing a theory of model design.

Design and Evaluation of the Extended FBS Model Based Gaze-Control Power Wheelchair for Individuals Facing Manual Control Challenges.

This study addresses the challenges faced by individuals with upper limb disadvantages in operating power wheelchair joysticks by utilizing the extended Function-Behavior-Structure (FBS) model to identify design requirements for an alternative wheelchair control system. A gaze-controlled wheelchair system is proposed based on design requirements from the extended FBS model and prioritized using the MosCow method. This innovative system relies on the user's natural gaze and comprises three levels: perception, decision making, and execution. The perception layer senses and acquires information from the environment, including user eye movements and driving context. The decision-making layer processes this information to determine the user's intended direction, while the execution layer controls the wheelchair's movement accordingly. The system's effectiveness was validated through indoor field testing, with an average driving drift of less than 20 cm for participates. Additionally, the user experience scale revealed overall positive user experiences and perceptions of the system's usability, ease of use, and satisfaction.

A design model of FBS based on interval-valued Pythagorean fuzzy sets

The FBS (Function-Behaviour-Structure) model is a research model that stimulates creative thinking of designers in the design process. In order to reduce the influence of user requirement ambiguity on design results in the product design process and improve the accuracy of user requirements in the function-behavior-structure (FBS) design model, this paper proposes an interval-valued Pythagorean fuzzy set-based FBS model integrating AHP and HOQ methods. Firstly, the design model will use IVPF-AHP method to study user requirements and use interval-valued Pythagorean linguistic terms to replace the traditional scoring method of AHP to get the weight of each user requirement. Secondly, the conversion between user requirements and functions will be realized by IVPF-HOQ method, which converts customer requirements into functional characteristics and calculates the weights of each functional characteristic. Finally, the design focus will be filtered according to the order of importance of the functional characteristics, which will be used as functions to guide the development of the FBS model. In this paper, the feasibility and effectiveness of the proposed method will be verified by an application example of a hand-held fluorescence spectrometer. The results show that the proposed FBS model can effectively reduce the subjectivity and ambiguity in the decision-making process, improve the accuracy and information richness of user requirements, and effectively highlight the focus of the design study. The innovation of the proposed method is to provide a more objective and accurate innovative design method for user requirements through the integration of AHP, HOQ and FBS to effectively explore and analyze user requirements. The use of IVPFS to deal with fuzzy information in the design process in a more flexible manner can reduce the ambiguity of requirements when user data is small, and effectively improve the limitations of the FBS design model which is more subjective.

Investigating the multilevel logic in design solutions: a Function Behaviour Structure (FBS) analysis

One of the reasons why Function Behaviour Structure (FBS) model did not become a pragmatic tool in industrial practice is its inability to push the designer to develop solutions beyond the dimensional scale where components, materials, and their relationships, of an artifact are defined. This paper introduces the concept of “topology” into the FBS model to support design in a multidimensional way. Though the proposed method, instead of describing a technical system with a unique FBS model, as many models as there are levels of detail in which it can be decomposed, can be exploited. Topology is crucial for linking them together in a hierarchical organization from macro to micro, so as to use the FBS ontology both to interpret and exploit physical phenomena in the design phase. Test results about the application of the proposed FBS multilevel method on a real industrial case study with engineering students showed its ability to modify their design attitude to generate solutions from an early expansion of concepts (breadth-first strategy) to the reformulation and refinement of the same (to depth-first strategy). More in detail, through the method, the students deeper analyse of the system, individuate a greater number of parameters on which to work and provide solutions exploiting the available resources more rationally.Graphical abstract

Optimal scheme of open design based on co-designer involvement and the dynamic FBSC model

With the development of Internet technologies, consumers can become involved in the process of product design to share requirements and technical knowledge, which are considered as the utilisable design resources. Function-behaviour-structure (FBS) modelling as a means of knowledge acquisition has been proposed by multitudinous technical workers in product design. However, most existing works are static and the product design resources have a fixed boundary. In this paper, a novel method of generating the optimal scheme for open design (OD) based on co-designer involvement and a dynamic FBSC model is proposed to tackle these problems. In OD, co-designers (consumers who have design experience and knowledge) can be involved in the process of product design to express requirements, share design knowledge and experience, etc. To support co-designers involvement in OD, a framework of the dynamic function-behaviour-structure cell (FBSC) model and its knowledge representation are presented. Based on the importance weights of design requirements, an approach for selecting SCs in the design scheme is proposed. Then a method for integrating design requirements and assembly complexity is put forward to generate the optimal design scheme. Finally, a case study is presented to demonstrate the feasibility of the proposed research.

Technical Trends and Competitive Situation in Respect of Metahuman—From Product Modules and Technical Topics to Patent Data

As a form of technological integration, metahuman has a significant influence on sustainable production because of its consistent technological evolution. However, few studies have provided insights into the technical assessment of metahuman by employing patents. In this paper, patent analysis is conducted to identify technological trends and the competitive situation in respect of metahuman from a product modularity perspective. First, we identify 17 highly relevant metahuman keywords by combining a literature analysis and an expert interview method and identify 42,256 patents from the Derwent Innovation Index (DII), thus improving the accuracy and validity of the data collection process. Then, metahuman product modularity is implemented using the function-behavior-structure (FBS) model, and seven technical topics are extracted from patents via latent Dirichlet allocation (LDA). Lastly, the procedure for identifying technology areas in respect of metahuman is improved by applying an optimized method to establish the connecting paths of product modules, technical topics, and patent data. The analysis results show that the development of metahuman technology can be divided into three periods. Different patent priority countries have distinctive competitive advantages and characteristics at the product module level. The findings of this study are intended to aid R&D enterprises and the government in formulating sustainable decision-making and promoting the development of the metahuman industry.

Design for additive manufacturing: A three layered conceptual framework for knowledge-based design

Designers face new challenges in the conceptual design phase because of the wide-ranging industrial application of additive manufacturing (AM). Structure simplification and function integration should be investigated during the conceptual design phase to embrace the design freedom that design for additive manufacturing (DFAM) offers. In this regard, a three-layered DFAM framework for knowledge-based design is established in this paper. The first layer utilizes a customized Gero’s function-behavior-structure (FBS) model to support the extraction of DFAM knowledge. The second layer uses a knowledge graph (KG) approach to express the DFAM knowledge. The third layer focuses on applying KG for DFAM by providing design guidance during the conceptual design phase. The framework supports learning to capture, design feature guidelines, representation of design behavior, and interaction with AM design engineers. Thereby, the framework provides a well-ordered and coherent approach to exploit the buried new design advantages of AM during the conceptual design phase. Subsequently, to verify the feasibility of the framework in the design process, a prototype software system and a throttle pedal design project are used for demonstration.

An Iterative Conceptual Design Process for Modular Product Based on Sustainable Analysis and Creative Template Method

The iterative design process of mechanical products has evolved towards a more sustainable direction driven by the concept of sustainable development. Modular design emphasizes the realization of user requirements under the condition of high efficiency and low cost, which is conducive to the diversification of product design. The sustainable innovative design methods are considered in modular design processes, which can improve the design capabilities of enterprises and enhance competitiveness. However, although the existing methods used in the iterative design of a product scheme contributes to satisfy the user requirements and generate the innovative scheme of the product, the sustainable design concepts such as the impact on the utilization of resources and environment during the iteration of the conceptual scheme and sustainability evaluation of product schemes have not been given sufficient attention. To resolve this, in this work, an iterative conceptual design process is proposed for modular products based on the sustainable analysis and creative template method. In this process, firstly, the KANO model is used to analyze the sustainable requirements of users. Secondly, a function behavior structure (FBS) model and creative template method are used to form sustainable innovative modular products conceptual design scheme. Finally, fuzzy analytic hierarchy process (FAHP) is used to evaluate the product scheme. The iterative conceptual design process proposed in this paper can be abbreviated as S-KFCF. The prototype conceptual scheme design of a novel low-temperature plasma deposition device is conducted. The results suggest that the proposed process could effectively reduce the total cost, shorten design cycles, increase product part recovery rates, and improve the environmental friendliness of the design schemes produced via sustainable analysis.

Kano-FBS model: a data-driven innovative design approach for smart product-service system development

Smart product-service system (Smart PSS), as an emerging digital paradigm, should meet user expectations and optimize their experience with a higher degree of sustainability. As a complex solution bundle with digital technology, its innovative design method differs from the existing method for traditional products. For a reason that massive user-generated data can be collected in the usage stage, which can be utilized to obtain new user requirements and calculate the user satisfaction degree. According to the real-time user preference information, some modules can be upgraded or adjusted in order to extend the lifecycle of the sustainable Smart PSS. Nevertheless, few studies focus on conducting innovative design by concerning massive information collected in the usage stage in a sustainable Smart PSS environment, let alone a novel approach to support the information re-use in product development of other fields. Aiming to fill this gap, a new innovative design methodology is proposed by combining Function-Behavior-Structure (FBS) and Kano model to guide Smart PSS development. User requirements and behaviors are predicted and analysed by forecasting and collecting information from the perspective of information. As an illustrative case study, a self-service medicine vending system is described to explain the proposed approach. This research provides guidance for Smart PSS in the medical field.

A conceptual design decision approach by integrating rough Bayesian network and game theory under uncertain behavior selections

Conceptual design decision plays a vital role in the new product development as it affects the direction of subsequent design activities. However, pertinent literature advocates the uncertainty assessment of the terminal scheme, but ignores the function interactions and the uncertain behavior selections derived from user’s preferences in the function-behavior-structure (FBS) design process. Besides, the decision-makers (DM)’s fuzzy judgments for behavior selections in the FBS model are not been addressed. To fill this gap, a conceptual design decision approach by integrating rough Bayesian Network (BN) and game theory under uncertain behavior selections is proposed, which could provide a graphic probabilistic model-based reasoning for the uncertain design process. In this approach, firstly, a sub-function importance model is constructed to achieve the extraction of the core sub-function module. Then, BN approach is developed to analyze the effect of uncertain behavior on the solution of sub-functions, and then support to predict whether to adopt the optimal scheme. And sub-function BN model is constructed based on the FBS model, and an initial BN model is updated by rough set technology. Finally, the probability distribution of uncertain behavior in interactive sub-functions is obtained from BN model, which is used to transform the uncertain behavior solving problem among sub-functions into a non-cooperative game process based on behavioral probabilities, and the optimal scheme is selected. A case study of tree climbing and trimming machine is used to validate the proposed approach and five principle solutions are selected, then the comparison results showed that the sub-function BN is able to provide a valuable design recommendation in new product development.