14,364 research outputs found

    Ontology Building of Manufacturing Quality Knowledge for Design Decision Support

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    This work was funded by National Natural Science Foundation of China (No: 70472066, 70771091), the project of Bureau of Science, Technology and Industry for National Defence, China (No. Z142008A001), the NPU Foundation for Humanities, Social Science, and Management Science Development (No. RW200817), which are gratefully acknowledged.Manufacturing knowledge on product quality is a kind of typical knowledge for supporting design decisions. In order to clearly identify and understand design decisions and their knowledge needs on manufacturing quality, an ontology of design decisions and manufacturing quality knowledge is developed. The methodology and tool used for the development of the proposed ontology is firstly introduced. The design decisions are organized along with five main design phases ranging from planning and task clarification, conceptual design, embodiment design to detail design. The knowledge needs of different design decisions, especially on the manufacturing quality knowledge, are analyzed through competition questions. Then, the ontology is built in the form of a hierarchical structure through the proposed methodology and ontology editor. Based on the developed ontology, further instances of the classes in the ontology can be filled as detailed knowledge, and can be accumulated for further construction of knowledge base

    Multilingual manager: a new strategic role in organizations

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    Today?s knowledge management (KM) systems seldom account for language management and, especially, multilingual information processing. Document management is one of the strongest components of KM systems. If these systems do not include a multilingual knowledge management policy, intranet searches, excessive document space occupancy and redundant information slow down what are the most effective processes in a single language environment. In this paper, we model information flow from the sources of knowledge to the persons/systems searching for specific information. Within this framework, we focus on the importance of multilingual information processing, which is a hugely complex component of modern organizations

    Generative retrieval-augmented ontologic graph and multi-agent strategies for interpretive large language model-based materials design

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    Transformer neural networks show promising capabilities, in particular for uses in materials analysis, design and manufacturing, including their capacity to work effectively with both human language, symbols, code, and numerical data. Here we explore the use of large language models (LLMs) as a tool that can support engineering analysis of materials, applied to retrieving key information about subject areas, developing research hypotheses, discovery of mechanistic relationships across disparate areas of knowledge, and writing and executing simulation codes for active knowledge generation based on physical ground truths. When used as sets of AI agents with specific features, capabilities, and instructions, LLMs can provide powerful problem solution strategies for applications in analysis and design problems. Our experiments focus on using a fine-tuned model, MechGPT, developed based on training data in the mechanics of materials domain. We first affirm how finetuning endows LLMs with reasonable understanding of domain knowledge. However, when queried outside the context of learned matter, LLMs can have difficulty to recall correct information. We show how this can be addressed using retrieval-augmented Ontological Knowledge Graph strategies that discern how the model understands what concepts are important and how they are related. Illustrated for a use case of relating distinct areas of knowledge - here, music and proteins - such strategies can also provide an interpretable graph structure with rich information at the node, edge and subgraph level. We discuss nonlinear sampling strategies and agent-based modeling applied to complex question answering, code generation and execution in the context of automated force field development from actively learned Density Functional Theory (DFT) modeling, and data analysis
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