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More companies are facing challenges in extracting and utilizing knowledge in product lifecycle. To solve this problem, a product lifecycle–oriented knowledge service framework is proposed based on the status review. The proposed framework is supported by four key methods and processes, which include mechanism of knowledge service identification, m...
Contexts in source publication
Context 1
... shown in Figure 4, each knowledge transfer activity is represented by an arrow. Different activities in prod- uct lifecycle constitute a knowledge service network including market research, product development, pro- duction, marketing and product-related service, and so on. ...
Context 2
... service network contains a large number of knowledge services which can influence each other. Some methods, such as interviews, questionnaires, motivational strategies, knowledge maps, and visualiza- tion technology, can be used to resolve the problems of tacit knowledge representation and transmission (see Figure 4). ...
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Citations
... Houssin and Coulibaly (2014) analyzed the entire product lifespan and used Markov models by considering operating time, maintenance time, and preparing time after failure to evaluate product performance [14]. Wu et al. (2017) proposed a framework by analyzing product lifecycle to enhance product design performance [15]. ...
... Houssin and Coulibaly (2014) analyzed the entire product lifespan and used Markov models by considering operating time, maintenance time, and preparing time after failure to evaluate product performance [14]. Wu et al. (2017) proposed a framework by analyzing product lifecycle to enhance product design performance [15]. ...
Products often experience different failure and repair needs during their lifespan. Prediction of the type of failure is crucial to the maintenance team for various reasons, such as realizing the device performance, creating standard strategies for repair, and analyzing the trade-off between cost and profit of repair. This study aims to apply machine learning tools to forecast failure types of medical devices and help the maintenance team properly decides on repair strategies based on a limited dataset. Two types of medical devices are used as the case study. The main challenge resides in using the limited attributes of the dataset to forecast product failure type. First, a multilayer perceptron (MLP) algorithm is used as a regression model to forecast three attributes, including the time of next failure, repair time, and repair time z-scores. Then, eight classification models, including Naïve Bayes with Bernoulli (NB-Bernoulli), Gaussian (NB-Gaussian), Multinomial (NB-Multinomial) model, Support Vector Machine with linear (SVM-Linear), polynomial (SVM-Poly), sigmoid (SVM-Sigmoid), and radical basis (SVM-RBF) function, and K-Nearest Neighbors (KNN) are used to forecast the failure type. Finally, Gaussian Mixture Model (GMM) is used to identify maintenance conditions for each product. The results reveal that the classification models could forecast failure type with similar performance, although the attributes of the dataset were limited.
... Later, Song et al. (2017) propose an recommender approach by depicting the context-based timesequence. Wu et al. (2017) propose a service-based framework that contains the research status and prospect. Further, Nilashi et al. (2018) have applied collaborative filtering to address the sparsity and scalability issues in recommender systems. ...
... Knowledge classification is the first step taken to represent and model knowledge service. From Wu et al. (2017), product development knowledge is classified as the know-what, knowwhy, know-how, and know-who knowledge. Using the XTM (XML Topic Maps) technique to represent the knowledge resources, the two key technologies to realize knowledge integration and sharing are knowledge integration based on ontology and knowledge retrieval based on semantics. ...
New product development is knowledge intensive as it needs the work teams and design engineers located at various locations to constantly share, update, and re-use knowledge. As such, improving the efficiency of acquiring knowledge and coping with the challenge of frequently retrieving related knowledge have become a key factor to managing knowledge in new product development. This paper combines rough set theory and information entropy to establish a new knowledge recommender technique to address the issue of knowledge reuse for new product development. Our method enhances knowledge acquisition and reuse, as it provides a realistic framework for knowledge acquisition and reuse, encompassing the entire process from what the design and work teams need, to recommending what they should have. To validate the proposed approach, we perform experiments on a case study to demonstrate the benefit and performance.
... In the fields of mechanical manufacturing, a product generally can be categorized into design and manufacturing systems or functional modules. The product development processes require a collaboration method integrated with knowledge acquisition and sharing [2]. As such, the existed process knowledge has been seen as the most useful and valuable resource to support product development. ...
To solve the problems of high time cost caused by the low efficiency of knowledge acquisition, this paper proposes a knowledge service framework based on case set. Three knowledge retrieval methods are designed based on parts keywords, customer orders and manufacturing processes. Additionally, a VSM-based(vetor space model) knowledge recommender method is proposed by using similarity matching algorithm to improve the efficiency of knowledge acquisition and transfer. Finally, application scenarios and evaluation of the proposed approach in a mechanical product design project are given to illustrate the application effect of knowledge service system.
... As shown in Figure 2, the knowledge management process is provided to explain the knowledge lifecycle for product design. In the outer loop of Figure 2, the knowledge process contains knowledge creation, knowledge acquisition, knowledge analysis and knowledge application [10]. The inner loop of Figure 2 shows the product design knowledge process, which includes knowledge representation, integration, query, recommendation and sharing. ...
The product development knowledge stored in manufacturing firms can be obtained and re-used to assist the development engineers in new product development and facilitate the use of knowledge resources, which in turn enhances the quality and reduces the time-cost of product innovation. This study presents a knowledge service integrated framework for product development, which is refined from the literature and supported by practice needs. The proposed integrated framework is supported by four pillars of work, namely, how to represent knowledge exactly, the model of knowledge integration, the method of knowledge recommendation, and the mechanism and performance evaluation of knowledge service. A demonstration of a gantry crane design is provided to show the effectiveness and benefit of the proposed integrated framework and technology solutions. Finally, we offer some pointers for future research.
... Many firms have used the knowledge service platform to model and manage their knowledge resources, to share and reuse knowledge for better and faster product development. However, the efficiency of knowledge capture and the capacity of knowledge service need to improve (Wu et al., 2017). It is a challenge to obtain the required knowledge quickly and accurately for product development, without increasing the product design time or cost. ...
... Song et al. (2017) used Gantt charts to depict the time-sequence relationship in a knowledge recommendation process. Wu et al. (2017) developed a product lifecycle-oriented knowledge service framework containing status reviews and technology trends. Zammit et al. (2018) proposed a knowledge capturing and sharing framework to improve the testing processes in global product development using storytelling and video sharing. ...
Knowledge recommendation has become a trend to improve the efficiency of acquiring knowledge during product development. As such, coping with the challenge of constantly discovering new knowledge is critical for knowledge reuse. This article proposes a knowledge recommendation framework based on design tasks, to facilitate the capturing and sharing of knowledge in manufacturing firms. An ontology-based knowledge representation model is designed and developed to organize the knowledge resources and a vector space model-based knowledge recommendation model is built to hasten the knowledge reuse process. A case study demonstrates the effectiveness of the proposed method.
... Nowadays time for design and production need to be decreased to increase the enterprise's rapid response capabilities and competitiveness. Mining implicit knowledge from history design data become more and more important [1]. Meanwhile, with the increasing of knowledge amount, how to use appropriate knowledge to assist given designer to accomplish specific task becomes a new challenge in knowledge management [2]. ...
Manufacturing firms today co-exist in a complex collaboration network with their partners, who often seek help from external knowledge services to co-develop more competitive products. Thus, knowing how to accurately and rapidly acquire such knowledge to sustainably improve the quality of knowledge services are critical to maintaining the competitiveness of such firms. There is a need to develop reliable frameworks and tools to support the knowledge services of these firms. This paper proposes a service-oriented knowledge recommender framework focusing on the mechanism of knowledge service transfer and the knowledge recommendation model, which comprises the knowledge service demanders, knowledge service providers, and platform operators. The matching processes and algorithm of the knowledge service demanders and knowledge service providers are designed based on the product development tasks. A fuzzy synthetic evaluation method is proposed, using multi-factor, fuzzy decision making techniques, to improve the quality of the knowledge service. To validate the approach, a case study on gantry crane development is provided to show how to apply the proposed framework and system. An experimental evaluation is designed to demonstrate the benefit and performance of the framework.
Past, present and future, to realize the aim of product CTQS (i.e., lower cost, faster time to market, higher quality and better service) with manufacturing intelligence, few manufacturers have no longer engaged in product related production decision support problem (P-DSP). However, P-DSP solving (P-DSPS) is a multi-criteria decision-making problem, which is context sensitive in solution objects-attributes and chaos in the decision process of manufacturing knowledge collaboration and reuse. To alleviate these limitations, this paper presents a novel triple deep workflow model for P-DSPS. Driven by a wicked task query, the proposed workflow of P-DSPS (WP-DSPS) has the function to retrieve similarity-based alternatives from domain knowledge driven solution flow (KSF) and to evaluate with expert knowledge collaboration from knowledge driven decision flow (KDF) based on utility theory under the task event driven control flow (ECF) strategy and operation logic. In the view of alternative adaption, a domain knowledge ontology-based degree of similarity (DoS) determines the P-DSPS alternatives width, a utility function-based degree of decision (DoD) determines alternatives quality, and a belief-based knowledge fusion technique is used to synthesize decision conflicts with a consensus degree (CD). To support the proposed models, a workflow-based system prototype is proposed and validated in two case studies.
The digital processing of content resources has subverted the traditional paper content processing model and has also spread widely. The digital resources processed by text structure need to be structured and processed by professional knowledge, which can be saved as a professional digital content resource of knowledge base and provide basic metadata for intelligent knowledge service platform. The professional domain-based knowledge system construction system platform explored in this study is designed based on natural language processing. Natural language processing is an important branch of artificial intelligence, which is the application of artificial intelligence technology in linguistics. The system first extracts the professional thesaurus and domain ontology in the digital resources and then uses the new word discovery algorithm based on the label weight designed by artificial intelligence technology to intelligently extract and clean the new words of the basic thesaurus. At the same time, the relationship system between knowledge points and elements is established to realize the association extraction of targeted knowledge points, and finally the output content is enriched from knowledge points into related knowledge systems. In order to improve the scalability and universality of the system, the extended architecture of the thesaurus, algorithms, computational capabilities, tags, and exception thesaurus was taken into account when designing. At the same time, the implementation of "artificial intelligence + manual assistance" was adopted. On the basis of improving the system availability, the experimental basis of the optimization algorithm is provided. The results of this research will bring an artificial intelligence innovation after the digitization to the publishing industry and will transform the content service into an intelligent service based on the knowledge system.
