Combining Deep Learning with Knowledge Graph for Design Knowledge Acquisition in Conceptual Product Design

The acquisition of valuable design knowledge from massive fragmentary data is challenging for designers in conceptual product design.This study proposes a novel method for acquiring design knowledge by combining deep learning with knowledge graph.Specifically,the design knowledge acquisition method utilises the knowledge extraction model to extract design-related entities and relations from fragmentary data,and further constructs the knowledge graph to support design knowledge acquisition for conceptual product design.Moreover,the knowledge extraction model introduces ALBERT to solve memory limitation and communication overhead in the entity extraction module,and uses multi-granularity information to overcome segmentation errors and polysemy ambiguity in the relation extraction module.Experimental comparison verified the effectiveness and accuracy of the proposed knowledge extraction model.The case study demonstrated the feasibility of the knowledge graph construction with real fragmentary porcelain data and showed the capability to provide designers with interconnected and visualised design knowledge.

Linking Competitors’ Knowledge and Developing Innovative Products Using Data Mining Techniques

In this article, the relationship between the knowledge of competitors and the development of new products in the field of capital medical equipment has been investigated. In order to identify the criteria for measuring competitors’ knowledge and developing new capital medical equipment products, marketing experts were interviewed and then a researcher-made questionnaire was compiled and distributed among the statistical sample of the research. Also, in order to achieve the goals of the research, a questionnaire among 100 members of the statistical community was selected, distributed and collected. To analyze the gathered data, the structural equation modeling (SEM) method was used in the SMART PLS 2 software to estimate the model and then the K-MEAN approach was used to cluster the capital medical equipment market based on the knowledge of actual and potential competitors. The results have shown that the knowledge of potential and actual competitors has a positive and significant effect on the development of new products in the capital medical equipment market. From the point of view of the knowledge of actual competitors, the market of “MRI”, “Ultrasound” and “SPECT” is grouped in the low knowledge cluster;“Pet MRI”, “CT Scan”, “Mammography”, “Radiography, Fluoroscopy and CRM”, “Pet CT”, “SPECT CT” and “Gamma Camera” markets are clustered in the medium knowledge. Finally, “Angiography” and “CBCT” markets are located in the knowledge cluster. From the perspective of knowledge of potential competitors, the market of “angiography”, “mammography”, “SPECT” and “SPECT CT” in the low knowledge cluster, “CT scan”, “radiography, fluoroscopy and CRM”, “pet CT”, “CBCT” markets in the medium knowledge cluster and “MRI”, “pet MRI”, “ultrasound” and “gamma camera” markets in the high knowledge cluster are located.

Collecting Knowledge about Medicinal Ingredients in Northwestern Sichuan in the 1950s

This article explores China’s state-level knowledge production on medicinal ingredients in northwestern Sichuan in the 1950s.Drawing on county-level archives,published materials and interviews,this historical article traces how different levels of governments and state-owned trading companies produced knowledge about medicinal ingredients and its production.It argues that on one hand the state’s procurement standards codified the knowledge about medicinal ingredients from the marketplace and the local producers.On the other hand,direct extraction of knowledge from the medicine gatherers and cultivators verbalized and collected the previously tacit local knowledge about the production of medicinal ingredients,which would help to alleviate the shortage in the supply of traditional Chinese medicines throughout the 1950s.

Intelligent Multilevel Knowledge Acquisition System for Product Design and Its Implementation

The characteristics of design process, design object and domain knowledge of complex product are analyzed. A kind of knowledge representation schema based on integrated generalized rule is stated. An AND-OR tree based model of concept for domain knowledge is set up. The strategy of multilevel domain knowledge acquisition based on the model is presented. The intelligent multilevel knowledge acquisition system (IMKAS) for product design is developed, and it is applied in the intelligent decision support system of concept design of complex product.

A Design Method with Knowledge Modeling for Complex Product Based on Ontology

Innovation design for complex products is a difficult issue in the military manufacturing industry. Ontology may provide a feasible way to rebuild the design process for complex products via sharing and reusing of design knowledge. In this paper, a design method used in the innovation design process of the complex products with knowledge modeling is proposed. Knowledge modeling is realized through ontology construction by combining the cycling evolutional theory of constructing ontology and OWL (Ontology Web Language)-based knowledge representation. As a case study, the satellite is selected as one of the complex products. The application domain of the satellite ontology is analyzed. According to the analysis result, the knowledge structure of satellite ontology is put forward based on OWL. The application of satellite product design shows that the method can effectively organize and reuse the knowledge resources in the innovation design of complex product and help companies to create more competitive products based on the existing knowledge and experience.

Product-design knowledge retrieval based on ontology

In order to improve the utilization ratio of knowledge retrieval, a product-design knowledge retrieval approach based on ontology is proposed. A representation model of product-design knowledge is proposed according to its characteristics. Domain ontology of product-design is estab- lished and the semantic annotation technology is used to connect the design knowledge and ontolo- gy. A new semantic annotation format is developed and semantic information of the design knowl- edge is enriched by making use of ontology. On that basis a retrieval algorithm is designed for semantic retrieval. Finally, this approach is used in a knowledge management system for military-vehi- cle design and its effectiveness and feasibility are validated. Results show that the recall ratio and the precision ratio of knowledge retrieval are improved greatly and users＇ requirements in semantic retrieval are satisfied.

Knowledge-reuse-oriented Product Modeling and Knowledge Management in Digital Design

Mostly product design knowledge and experience exists in design handbook or expert’s brain. During design process,designer has to refer to many handbooks and design materials,and design solution is hard to be reused. In order to deal with these deficiencies in digital design,this paper focuses on knowledge-reuse-oriented product modeling and knowledge management. Design knowledge is used to guide design process,and it is embedded into product model along with design process. Through analyzing design process,mechanical product knowledge is classified into four basic types that include calculation formula,design rule,engineering check and parameter spectrum etc. In order to manage and encapsulate design knowledge the concept of knowledge component(KCOM) is proposed. Design knowledge base can be organized as a hierarchy tree of KCOM. Knowledge reuse in KCOM level is the reuse of whole object in knowledge base,and knowledge reuse in item level is the reuse of basic knowledge type. Finally knowledge reuse approaches between knowledge base and CAD system is presented.

Leveraging Robust Artificial Intelligence for Mechatronic Product Development—A Literature Review

Mechatronic product development is a complex and multidisciplinary field that encompasses various domains, including, among others, mechanical engineering, electrical engineering, control theory and software engineering. The integration of artificial intelligence technologies is revolutionizing this domain, offering opportunities to enhance design processes, optimize performance, and leverage vast amounts of knowledge. However, human expertise remains essential in contextualizing information, considering trade-offs, and ensuring ethical and societal implications are taken into account. This paper therefore explores the existing literature regarding the application of artificial intelligence as a comprehensive database, decision support system, and modeling tool in mechatronic product development. It analyzes the benefits of artificial intelligence in enabling domain linking, replacing human expert knowledge, improving prediction quality, and enhancing intelligent control systems. For this purpose, a consideration of the V-cycle takes place, a standard in mechatronic product development. Along this, an initial assessment of the AI potential is shown and important categories of AI support are formed. This is followed by an examination of the literature with regard to these aspects. As a result, the integration of artificial intelligence in mechatronic product development opens new possibilities and transforms the way innovative mechatronic systems are conceived, designed, and deployed. However, the approaches are only taking place selectively, and a holistic view of the development processes and the potential for robust and context-sensitive artificial intelligence along them is still needed.

“Products-Knowledge” Two-Dimensional Increment Model of Fashion Industry Value Chain Based on Industry Analysis

Fashion industry has a complex characteristic for it spans the first, second, and third industries. In addition, the characteristic of creative industry has high value-added for its knowledge outputting, which makes the traditional value-added analysis based on supply chain not easy and good enough to interpret its industry value-added features. From the perspective of "products-knowledge" two-dimensional analysis,a fashion industry value chain increment model is built,by simulating the process of "product flow" and "information flow" value-added. The fashion industry value chain increment model provides an effective way for the enterprise strategy formulation and production strategy adjustment.

CATIA Product Knowledge Template应用实例研究

一、概述CATIA的Product Knowledge Template功能为用户提供了一种快速建模工具,它可以将已经定义好的、不管是简单的还是复杂的模型特征、装配特征等通过交互式的方法按新的条件重新再现出来.

Knowledge-Based Platform for Collaborative Innovation Development of Products

The platform of distributed design and resource sharing is important for medium-sized and small companies in developing products to improve competitiveness. As a background of creative product design, a knowledge model based on product collaborative innovation development of products (CIDP) is proposed. Characteristics of CIDP are analyzed, and the framework and key technologies of the CIDP-platform based knowledge studied. Through integration of existing system and interface designs, a development platform has been built to support the PCID within knowledge-based engineering (KBE). An example is presented, indicating that the prototype system is maneuverable and practical.

Knowledge acquisition method for case-knowledge-based product family design

Knowledge acquisition has always been the bottleneck of artificial intelligence. It is the critical point in product family design. Here a knowledge acquisition method was introduced based on scenario model and repository grid and attribute ordering table technology. This method acquired knowledge through providing product design cases to expert, and recording the means and knowledge used by the expert to describe and resolve problems. It used object to express design entity, used scenario to describe the design process, used Event-Condition-Action(ECA) rule to drive design process, and with the help of repository grid and attribute ordering table technology to acquire design knowledge. It’s a good way to capture explicit and implicit knowledge. And its validity is proved with respective examples.

Using Rapid Prototyping Data to Enhance a Knowledge-Based Framework for Product Redesign

The particular characteristics of Rapid Prototyping technologies, both in terms of constrains and opportunities, often require the reconfiguration of the product model to obtain the best compliance with the product functionalities and performances. Within this field of research, a knowledge-based tool named Design GuideLines Collaborative Framework (DGLs-CF) was developed to support both the designers defining the product consistently with the manufacturing technologies and the manufacturers defining the building setup consistently with the product requirements. Present work is focused on enhancing the DGLs-CF knowledge base and on updating the DGLs-CF knowledge management by using the information gathered on some RP technologies. The added-value of this research is represented by an improvement in the Redesign/Reconfig- uration Package, the final result of the DGLs-CF adoption. This is a list of actions to be performed on the product model and on the process parameters to avoid the limitations of the technology and to exploit at best its opportunities.

The Relationship between Customer Knowledge Management and Performance of Agricultural Product Innovation

This paper takes an overview of the CKM and the performance of agricultural product innovation from contents of agricultural product innovation and customer knowledge management (CKM) ,the relation between CKM and agricultural product innovation. On the basis of the overview, it builds the theoretical framework of CKM and agricultural product innovation. It points out that enterprises can satisfy demands of customers through acquisition,share,utilization and innovation of customer knowledge,and improve performance of agricultural product innovation through speeding up agricultural product innovation.

Potentials and Challenges of Carbon Knowledge Graph in Sustainable Textile Production for Carbon Traceability:A Review

Textile production has received considerable attention owing to its significance in production value,the complexity of its manufacturing processes and the extensive reach of its supply chains.However,textile industry consumes substantial energy and materials and emits greenhouse gases that severely harm the environment.In addressing this challenge,the concept of sustainable production offers crucial guidance for the sustainable development of the textile industry.Low-carbon manufacturing technologies provide robust technical support for the textile industry to transition to a low-carbon model by optimizing production processes,enhancing energy efficiency and minimizing material waste.Consequently,low-carbon manufacturing technologies have gradually been implemented in sustainable textile production scenarios.However,while research on low-carbon manufacturing technologies for textile production has advanced,these studies predominantly concentrate on theoretical methods,with relatively limited exploration of practical applications.To address this gap,a thorough overview of carbon emission management methods and tools in textile production,as well as the characteristics and influencing factors of carbon emissions in key textile manufacturing processes is presented to identify common issues.Additionally,two new concepts,carbon knowledge graph and carbon traceability,are introduced,offering strategic recommendations and application directions for the low-carbon development of sustainable textile production.Beginning with seven key aspects of sustainable textile production,the characteristics of carbon emissions and their influencing factors in key textile manufacturing process are systematically summarized.The aim is to provide guidance and optimization strategies for future emission reduction efforts by exploring the carbon emission situations and influencing factors at each stage.Furthermore,the potential and challenges of carbon knowledge graph technology are summarized in achieving carbon traceability,and several research ideas and suggestions are proposed.

Constructing the Ontology for Modeling the Fish Production in Pearl River Basin

This paper puts forward a construction method based on ontology for the Pearl River Basin fish production, to facilitate the domain knowledge analysis and information retrieval. By converting the concepts and terms in domain ordinally, the fish production ontology was constructed with the definition of classes, properties, instances, and relationships. The developed ontology model of the fish production knowledge is proposed and applied in the system of fish diseases diagnosis primarily. The research lays the semantic foundation for the further efficient knowledge management and practical application.