Design Knowledge Representation Research Articles

Opportunities and challenges of ChatGPT for design knowledge management

Recent advancements in Natural Language Processing have opened up new possibilities for the development of large language models like ChatGPT, which can facilitate knowledge management in the design process by providing designers with access to a vast array of relevant information. However, integrating ChatGPT into the design process also presents new challenges. In this paper, we provide a concise review of the classification and representation of design knowledge, and past efforts to support designers in acquiring knowledge. We analyze the opportunities and challenges that ChatGPT presents for knowledge management in design and propose promising future research directions. A case study is conducted to validate the advantages and drawbacks of ChatGPT, showing that designers can acquire targeted knowledge from various domains, but the quality of the acquired knowledge is highly dependent on the prompt.

Product Innovation Design Process Model Based on Functional Genes Extraction and Construction

The product innovation design is an innovation-intensive process that requires abundant knowledge and design experience, even across industries and disciplines, as a resource for problem solving. Therefore, designers need to have a good command of more design knowledge in this process. However, due to varied expressions of design knowledge (function, principle, structure) in different fields lead to the difficulty in representation of design knowledge, which makes designers unable to innovate by directly using design knowledge. Therefore, to solve the problem above, this paper, analogous to biological gene, proposes a product innovation process model based on functional gene extraction and construction. The proposed process model normalizes design knowledge through functional genes and help designers acquire design knowledge in different fields, which further promotes the product innovation design. The process model consists of four following steps. First, Obtain the overall function of product based on user needs and decompose it. Second, Build the functional gene model based on digital twin ones to expand the breadth of design knowledge and facilitate the retrieval. Third, Screen similar functional gene by the functional similarity algorithm. Finally, Obtain the design scheme through recombination, transcription, and translation of the functional gene. The feasibility of the research method is verified by using a shared bicycle parking device as an example.

Artificial Intelligence Tools for Better Use of Axiomatic Design

Axiomatic Design (AD) provides a powerful thinking framework for solving complex engineering problems through the concept of design domains and diligent mapping and decomposition between functional and physical domains. Despite this utility, AD is yet to be implemented for widespread use by design practitioners solving real world problems in industry and exists primarily in the realm of academia. This is due, in part, to a high level of design expertise and familiarity with its methodology required to apply the AD approach effectively. It is difficult to correctly identify, extract, and abstract top-level functional requirements (FRs) based on early-stage design research. Furthermore, guiding early-stage design by striving to maintain functional independence, the first Axiom, is difficult at a systems level without explicit methods of quantifying the relationship between high-level FRs and design parameters (DPs). To address these challenges, Artificial Intelligence (AI) methods, specifically in deep learning (DL) assisted Natural Language Processing (NLP), have been applied to represent design knowledge for machines to understand, and, following AD principles, support the practice of human designers. NLP-based question-answering is demonstrated to automate early-stage identification of FRs and to assist design decomposition by recursively mapping and traversing down along the FR-DP hierarchical structure. Functional coupling analysis could then be conducted with vectorized FRs and DPs from NLP-based language embeddings. This paper presents a framework for how AI can be applied to design based on the principles of AD, which will enable a virtual design assistant system based on both human and machine intelligence.

DESIGN KNOWLEDGE REPRESENTATION WITH TECHNOLOGY SEMANTIC NETWORK

AbstractEngineers often need to discover and learn designs from unfamiliar domains for inspiration or other particular uses. However, the complexity of the technical design descriptions and the unfamiliarity to the domain make it hard for engineers to comprehend the function, behavior, and structure of a design. To help engineers quickly understand a complex technical design description new to them, one approach is to represent it as a network graph of the design-related entities and their relations as an abstract summary of the design. While graph or network visualizations are widely adopted in the engineering design literature, the challenge remains in retrieving the design entities and deriving their relations. In this paper, we propose a network mapping method that is powered by Technology Semantic Network (TechNet). Through a case study, we showcase how TechNet’s unique characteristic of being trained on a large technology-related data source advantages itself over common-sense knowledge bases, such as WordNet and ConceptNet, for design knowledge representation.

Knowledge Acquisition and Representation for High-Performance Building Design: A Review for Defining Requirements for Developing a Design Expert System

New functions and requirements of high performance building (HPB) being added and several regulations and certification conditions being reinforced steadily make it harder for designers to decide HPB designs alone. Although many designers wish to rely on HPB consultants for advice, not all projects can afford consultants. We expect that, in the near future, computer aids such as design expert systems can help designers by providing the role of HPB consultants. The effectiveness and success or failure of the solution offered by the expert system must be affected by the quality, systemic structure, resilience, and applicability of expert knowledge. This study aims to set the problem definition and category required for existing HPB designs, and to find the knowledge acquisition and representation methods that are the most suitable to the design expert system based on the literature review. The HPB design literature from the past 10 years revealed that the greatest features of knowledge acquisition and representation are the increasing proportion of computer-based data analytics using machine learning algorithms, whereas rules, frames, and cognitive maps that are derived from heuristics are conventional representation formalisms of traditional expert systems. Moreover, data analytics are applied to not only literally raw data from observations and measurement, but also discrete processed data as the results of simulations or composite rules in order to derive latent rule, hidden pattern, and trends. Furthermore, there is a clear trend that designers prefer the method that decision support tools propose a solution directly as optimizer does. This is due to the lack of resources and time for designers to execute performance evaluation and analysis of alternatives by themselves, even if they have sufficient experience on the HPB. However, because the risk and responsibility for the final design should be taken by designers solely, they are afraid of convenient black box decision making provided by machines. If the process of using the primary knowledge in which frame to reach the solution and how the solution is derived are transparently open to the designers, the solution made by the design expert system will be able to obtain more trust from designers. This transparent decision support process would comply with the requirement specified in a recent design study that designers prefer flexible design environments that give more creative control and freedom over design options, when compared to an automated optimization approach.

Application of knowledge engineering in spacecraft overall design

This paper proposes an advanced technology in spacecraft overall design based on knowledge engineering. Knowledge engineering is a branch of artificial intelligence. Applying knowledge engineering technology to improve the efficiency and quality of overall design process and enhance the automation of overall design, moreover focusing on solving overall spacecraft design knowledge representation, knowledge evaluation, knowledge acquisition, knowledge inference and related issues. Unlike the existing methods, this paper uses ontology technology to build a pint-sized spacecraft overall design knowledge base under protégé software environment, which achieves a better performance. Finally, simulations for the designed power subsystem based on the knowledge base are performed, and the simulation results validate the effectiveness, reliability, of the power subsystem designed by the method of spacecraft overall design based on knowledge engineering.

Ontology-based representation of tolerancing and design knowledge for an automated tolerance specification of product concepts

Abstract Motivated by the high importance of the tolerance specification, numerous partially automated approaches have emerged. However, since these approaches require an extensive geometry definition, they are not applicable for an early definition of the tolerance concept and its evaluation by Robust Design aspects. Motivated by this lack, this paper presents a novel ontology-based approach combining knowledge from the product design and tolerancing domains to enable an automated tolerance specification of product concepts. This close linkage enables a more precise specification and bridges the gap to the subsequent final specification in accordance with current GPS standards. Finally, this novel approach is demonstrated by means of a crank drive mechanism.

Special Issue: Highlights From ASME CIE 2018

Special Issue: Highlights From ASME CIE 2018

Research on the knowledge representation and retrieval for mechanical product design based on ontology

Research on the knowledge representation and retrieval for mechanical product design based on ontology

Research on the knowledge representation and retrieval for mechanical product design based on ontology

Different subjects are applied in the design of mechanical products. For efficient sharing and reusing of knowledge in the diverse forms of information, this study presents the classification of mechanical design and the process of ontology construction. The knowledge of mechanical field can fully express the semantics and syntax of concept through the function of ontology. Furthermore, based on the representation of ontology knowledge, it establishes a knowledge retrieval model of mechanical products and proposes a corresponding retrieval algorithm, which implements much higher precision and recall in the mechanical field.

Web services-based knowledge sharing, reuse and integration in the design evaluation of mechanical systems

Abstract The process of design evaluation is a complicated task for designers due to the need to employ sufficient multi-disciplinary design knowledge. To aid this task, the sharing, reuse and integration of multi-disciplinary knowledge between workers, who play different roles and perform different operations in the design evaluation process, is of great importance. Web services provide an appropriate means to transfer design knowledge between individuals and teams in different disciplines, offering reductions in operation costs and enhanced efficiency, with a better, more streamlined service. This paper identifies and clarifies the knowledge needs required in the design evaluation process, models the knowledge flow among various organizations and the knowledge shared between them, and proposes a web services-based knowledge flow framework to enhance collaboration among various disciplinary experts. An illustrative case is presented to demonstrate how the proposed model may be used to represent design knowledge and integrate it into the design evaluation process. Results from this study contribute significantly to efforts in fulfilling the diverse knowledge needs of workers in the evaluation process. It also helps integrated designers to obtain a clear view of knowledge flow during design evaluation and facilitates knowledge sharing, exchange and reuse among various organizations via a web service-based method.

An ontology-based approach to knowledge representation for Computer-Aided Control System Design

Different approaches have been used in order to represent and build control engineering concepts for the computer. Software applications for these fields are becoming more and more demanding each day, and new representation schemas are continuously being developed. This paper describes a study of the use of knowledge models represented in ontologies for building Computer Aided Control Systems Design (CACSD) tools. The use of this approach allows the construction of formal conceptual structures that can be stated independently of any software application and be used in many different ones. In order to show the advantages of this approach, an ontology and an application have been built for the domain of design of lead/lag controllers with the root locus method, presenting the results and benefits found.

Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco.

There exists a gap between visualization design guidelines and their application in visualization tools. While empirical studies can provide design guidance, we lack a formal framework for representing design knowledge, integrating results across studies, and applying this knowledge in automated design tools that promote effective encodings and facilitate visual exploration. We propose modeling visualization design knowledge as a collection of constraints, in conjunction with a method to learn weights for soft constraints from experimental data. Using constraints, we can take theoretical design knowledge and express it in a concrete, extensible, and testable form: the resulting models can recommend visualization designs and can easily be augmented with additional constraints or updated weights. We implement our approach in Draco, a constraint-based system based on Answer Set Programming (ASP). We demonstrate how to construct increasingly sophisticated automated visualization design systems, including systems based on weights learned directly from the results of graphical perception experiments.

Producing the Rigorous Design Case

Producing a rigorous design case, and producing a case that holds utility for designers, are not always one and the same act. The differences between rigor and utility in design cases are discussed in this article, as well as the position of the design case in the broader realm of naturalistic research. Drawing from naturalistic and action research, possible standards of rigor for cases emerge. These are presented and related to the representation of design knowledge. The article then presents issues observed among authors of traditional research in producing design cases of rigor.

FROM ONTOLOGIES OF DESIGNING TO COGNITIVE SEMIOTICS

Some fundamentals of lifecycle concept for Complex Technical Systems are considered. The Complex Technical Systems lifecycle is a meta-system for computer-aided design, computer-integrated manufacturing and maintenance, enabling the mapping of Complex Technical Systems state changes during the period of its existence. Main lifecycle trends are both extension and detailing that leads to non-traditional design strategies and emergence of new design phases at the crossroad of research and development, design and tests. Within the framework of ontological approach design phases, levels, aspects and tasks are viewed. Here the phase of preliminary design (also called «research design») seen as a sort of «analytical synthesis» is of primary concern. An important role of various Non-Factors (uncertainty, imprecision, contradiction, fuzziness) in preliminary design is shown. A three-dimensional space for Non-Factors modeling is introduced and their classifications are suggested. On early stages of design a model-based approach is crucial: it deals with both models and parameters. Some original techniques and tools of representing design knowledge in a modelparametric space are developed. Besides, a multi-dimensional semantic-pragmatic space of design methods and languages based on bipolar scales is outlined. Two keynote principles for design languages – compatibility principle and complementarity principle – are formulated on the basis of semantic scale for languages. On the one hand, the support of designer’s creative thinking requires the use of both illustrative and cognitive graphics. On the other hand, two variants of General Design Theory are analyzed. By General Design Theory a formal design theory is meant that tends to describe the design concepts in terms of sets, topological spaces and calculi, in particular, higher-order predicate calculus. The analysis of formal systems shortcomings from the viewpoint of making real design decisions is performed. As a result, the need in bringing semiotic models into design science is justified. In the final part of the paper, basic definitions of semiotics together with sign classifications are given. The peculiarities of the transition from semiotic modeling to Applied Semiotics and Cognitive Semiotics are discussed. Cognitive Semiotics faces the problems of both knowledge acquisition from sign systems and the synthesis of sign-based structures in the context of knowledge engineering. Professor Yury R. Valkman was one of the pioneers in Cognitive Semiotics.

Knowledge component-based intelligent method for fixture design

Aiming at providing an effective mechanism for representation, organization, and reuse of fixture design knowledge, this paper puts forward an intelligent methodology for fixture design based on knowledge component technology. First, the ontology models of fixture elements, the workpiece, and the fixture design case are constructed to organize the statical design knowledge related to each of them. Then, the fixture design process-related knowledge is modeled by knowledge components corresponding to fixture layout planning process and fixture structure design process. Hence, the hierarchy structure of the fixture design knowledge reuse is formed. Under the framework of the fixture layout knowledge component, the association analysis method is used for generating reasoning rules and then the rule-based reasoning (RBR) method is used for the determination of the workpiece-locating mode and the initiative candidate of the locating datum. Further, an entropy weight fuzzy comprehensive evaluation method is employed to select the most suitable locating datum from the candidates. Under the framework of the fixture structure design knowledge component, the selection of fixture elements, element dimension drive, and auto-assemble are fulfilled by the combination of RBR and case-based reasoning (CBR). The method realizes the effective organization and efficient use of fixture design knowledge and improves the efficiency of fixture design. It has been verified by a case of airplane part machining fixture design.

Formal ontology and CAD integration with macro parametric approach

ABSTRACTThis paper discusses an ontological integration framework in the context of mereotopological formal ontology and CAD systems. Based on a comprehensive literature review of current design knowledge management and formal ontology research works, the authors propose the integration framework that can incorporate design knowledge with CAD systems. The primary role of mereotopology in this research work is the formal representation of design knowledge for the 3D solid assembly models. Most of the existing research works about design ontology present an abstract form and often require significant additional efforts to be integrated into CAD systems. Thus, this work introduces a formal ontology for CAD (OCAD) and macro-parametric approach as a practical way to integrate design knowledge with CAD systems. For the implementation, Protege, SPARQL, and commercial CAD system are employed. We also propose the enhancement of the developed framework for the future use to aid dynamic modeling.

Feature-based integrated product model for low-carbon conceptual design

ABSTRACTConceptual design has essential impacts on the carbon footprint of a product. Existing product models for conceptual design always focus on knowledge modelling and knowledge organisation, etc. However, the traditional approach is lack of better comprehending representation and reuse of low-carbon design knowledge for low-carbon conceptual design. This paper is devoted to presenting a feature-based integrated modelling approach of product model for low-carbon conceptual design to make the knowledge of functional design be transferred efficiently to the principle conceptual design and structure conceptual design, and even subsequent design process. The feature-based integrated representation model with carbon footprint information about function, effect and structure concept are put forward step by step, and then an integrated principle solution model for low-carbon conceptual design is presented based on the generalised assembly model. The knowledge representation and organisation method of principle solutions in the low-carbon conceptual design are also proposed based on the complex nested knowledge relationships among these stages in the product life cycle. Low-carbon conceptual design process is then discussed in detail. The conceptual design of a new damping device is given as an example, which demonstrates that the methodology is helpful to low-carbon conceptual design.

Digital Representation in Multicopter Design Along the Product Life-cycle

This paper describes the formal representation approach used in the scope of a large-scale research project aiming at the holistic, completely digital representation and automated computability of the product life-cycle (PLC). The distinctive approach consists in the novel methodology of visual, graph-based design languages expressed in UML (Unified Modeling Language), which allow both the creation of new engineering models as well as the reuse of pre-existing engineering models and know-how. The tool-independent representation of the engineering design knowledge via the abstraction level in UML was originally developed by one of the project partners in prior work. The paper explains the methodology, modeling method and computational means as well as their integration along the product life-cycle including requirements, design, simulation as well as cost and energy analysis. The explanation is illustrated through a design language implementation for the automated design of novel light-weight multicopters.

An extended hierarchical graph-based building model for design and engineering problems

Abstract This paper deals with research into advanced hierarchical graph structures which have been developed for knowledge representation in computer-aided design and robotics. Complex real-life engineering problems still need useful graph-based models. A new approach to represent models preserving the intrinsic nature of problem structures using graphs with many hierarchies is proposed. Different types of hierarchical dependencies (geometrical, functional, etc.) between design parts can be expressed in one structure. Moreover, the possibility of selecting only problem-related hierarchies reduces the number of data to be analysed in the process of reasoning about considered designs. This approach is illustrated by applying multi-hierarchical graph structures to represent complex data used for robot control algorithms.