用于智能机床的系统化多层认知模型

IF 5.9 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Intelligent Manufacturing Pub Date : 2024-08-30 DOI:10.1007/s10845-024-02481-5
Tengyuan Jiang, Jingtao Zhou, Xiang Luo, Mingwei Wang, Shusheng Zhang
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引用次数: 0

摘要

作为生产系统的基础制造能力提供单元,数控机床的智能化水平将影响智能制造的实现。学术界从技术角度对数控机床进行了大量的智能化研究,但仍需要一个系统的认知模型来促进认知能力的构建,支撑数控机床的智能化实现和持续改进。因此,本文提出了基于认知信息学的三部分七层认知模型,以促进数控机床认知能力的构建和智能化改造。首先,提出了系统的多层认知模型,并介绍了各认知层对数控机床不同认知能力建设的促进作用。然后,本文介绍了多层认知模型中包含的认知分析环和认知学习环,它们可以促进数控机床自适应能力和持续学习能力的构建。给出了智能机床的评价指标,用于评价机床智能模型。此外,还介绍了智能机床多层认知模型的认知使能技术,该技术可支持分析、决策和学习等认知能力的实现。最后,通过开发的可计算数字孪生平台和智能机床实施前后的对比,验证了所提出的系统化多层认知模型的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A systematic multi-layer cognitive model for intelligent machine tool

As the basic manufacturing capabilities provide unit of the production system, the intelligent level of the CNC machine tool will affect the realization of intelligent manufacturing. Academia has carried out a lot of intelligent research on CNC machine tool from technical perspective, but there still needs a systematic cognitive model to promote the construction of cognitive abilities, to support the intelligent realization and continuous improvement of CNC machine tool. Therefore, this paper proposes a three-part, seven-layer cognitive model based on cognitive informatics to promote the construction of cognitive abilities and the intelligent transformation of CNC machine tool. Firstly, a systematic multi-layer cognitive model is proposed, and each cognitive layer is introduced to promote the different cognitive abilities construction of CNC machine tool. Then, this paper introduces the cognitive analysis loop and the cognitive learning loop contained in the multi-layer cognitive model, which can promote the construction of the adaptive and continuous learning abilities of CNC machine tool. The evaluation indicators of the intelligence machine tool are given, which is used to evaluate machine tool intelligence model. Furthermore, the cognitive enabling technologies of the multi-layer cognitive model for intelligent machine tool is presented, which supports the realization of cognitive abilities such as analysis, decision making, and learning. Finally, the feasibility of the proposed systematic multi-layer cognitive model is verified by the developed computable digital twin platform and comparison before and after implementation for intelligent machine tool.

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来源期刊
Journal of Intelligent Manufacturing
Journal of Intelligent Manufacturing 工程技术-工程:制造
CiteScore
19.30
自引率
9.60%
发文量
171
审稿时长
5.2 months
期刊介绍: The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.
期刊最新文献
Industrial vision inspection using digital twins: bridging CAD models and realistic scenarios Reliability-improved machine learning model using knowledge-embedded learning approach for smart manufacturing Smart scheduling for next generation manufacturing systems: a systematic literature review An overview of traditional and advanced methods to detect part defects in additive manufacturing processes A systematic multi-layer cognitive model for intelligent machine tool
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