...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Intelligent Manufacturing >Variation source identification for deep hole boring process of cutting-hard workpiece based on multi-source information fusion using evidence theory
【24h】

Variation source identification for deep hole boring process of cutting-hard workpiece based on multi-source information fusion using evidence theory

机译：基于证据理论的多源信息融合的切削硬工件深孔镗削过程变异源识别

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Variation source identification for machining process is a key issue in closed-loop quality control, and critical for quality and productivity improvement. Meanwhile, it is a challenging engineering problem, especially for deep hole boring process of cutting-hard workpiece due to its complexity and instability. In this paper, a systematic method of variation source identification for deep hole boring process based on multi-source information fusion using Dempster-Shafter (D-S) evidence theory is proposed. A logic framework for variation source identification is presented to address how this issue can be formulated in the frame of evidence theory, in terms of evidence acquisition, variation source frame of discernment, mass functions and the rules for evidence combination and decision-making. First, run charts are applied to detect the non-random variation of quality measurements of one workpiece, which are acquired at equidistant positions along the axis direction of the hole. And the unnatural run chart patterns are detected by using fuzzy support vector machine and regarded as information cues for variation source identification. Then, the frame of discernment which consists of potential variation source in case of every specific unnatural pattern is constructed. The mass functions that represent the degree of belief supported by the unnatural patterns regarding the possible causes are determined by using judgment matrixes, and treated as pieces of evidences of variation source identification. Afterwards, all of the evidences are combined by using D-S fusion rules. The rules for making reliable diagnostic decisions are also addressed. Finally, a case study is put forward to demonstrate the feasibility and effectiveness of the proposed methodology. The results indicate that the proposed method can resolve the conflicts among the evidences and improve the accuracy of variation source identification for deep hole boring process.
机译：加工过程的变化源识别是闭环质量控制中的一个关键问题，对于提高质量和生产率至关重要。同时，由于其复杂性和不稳定性，这是一个具有挑战性的工程问题，特别是对于切削硬工件的深孔镗削工艺而言。该文提出一种基于Dempster-Shafter（D-S）证据理论的多源信息融合的深孔钻孔过程变异源识别系统方法。提出了变异源识别的逻辑框架，以解决如何在证据理论框架中从证据获取、变异源辨别框架、质量函数以及证据组合和决策规则等方面表述该问题。首先，应用运行图来检测一个工件质量测量值的非随机变化，这些变化是在沿孔轴方向的等距位置获取的。利用模糊支持向量机检测出不自然的运行图模式，并将其视为变异源识别的信息线索。然后，构建由每个特定不自然模式中的潜在变化源组成的辨别框架。通过使用判断矩阵确定代表非自然模式支持的关于可能原因的信念程度的质量函数，并被视为变异源识别的证据。然后，使用D-S融合规则对所有证据进行组合。此外，还讨论了做出可靠诊断决策的规则。最后，通过算例验证了所提方法的可行性和有效性。结果表明，所提方法能够解决证据之间的矛盾，提高深孔钻孔工艺变异源识别的准确性。

著录项

相似文献

  • 外文文献
  • 中文文献
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号