基于 DBO-ELM 的 C/SiC 复合材料有序砂轮磨损状态识别研究

IF 5.3 1区 工程技术 Q1 ENGINEERING, MECHANICAL
Wear Pub Date : 2024-07-31 DOI:10.1016/j.wear.2024.205529
Ye Guo , Bing Chen , Hongyu Zeng , Guangye Qing , Bing Guo
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引用次数: 0

摘要

在有序砂轮磨削 2.5D C/SiC 复合材料的过程中,由于材料的特殊结构和有序的磨料簇,信号较为复杂,难以识别砂轮的磨损状态。为解决这一问题,本研究采用了直接和间接两种分析方法。采集了砂轮整个寿命期间的声发射信号、磨削力和磨削温度信号,并对砂轮的形貌进行了拍照。对比分析了有序砂轮与传统无序砂轮在磨损行为上的差异。深入研究了有序砂轮磨损行为与各种信号之间的对应关系。结果表明,初始磨损发生在第 1-80 次磨削过程中,稳定磨损发生在第 81-224 次磨削过程中,严重磨损发生在第 225-350 次磨削过程中。此外,本研究还进一步提取了各种信号的关键特征,并利用皮尔逊相关系数确定了与砂轮磨损高度相关的特征。随后,使用 LDA 方法降低了单个信号类型的维度。通过比较不同信号类型组合的识别效果,确定了最佳组合。最后,本研究提出了用于有序砂轮磨损状态识别和分类的 DBO-ELM 模型。与 KNN、SVM、ELM 和 BP 四种常见的机器学习模型相比,所提出的 DBO-ELM 模型的分类准确率为 94.86%,分别提高了 25.57%、16%、16% 和 7.86%。这表明本研究提出的 DBO-ELM 模型在有序砂轮磨损状态识别方面具有一定的优势和潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on wear state identification of ordered grinding wheel for C/SiC composites based on DBO-ELM

In the grinding process of 2.5D C/SiC composites by the ordered grinding wheel, the signals are more complicated due to the special structure of the material and the ordered abrasive clusters, which makes it difficult to identify the wear state of the grinding wheel. To solve this problem, this study employs the analysis methods of direct and indirect. The acoustic emission signals, grinding force and grinding temperature signals throughout the entire lifespan of the grinding wheel were collected, and the topography of the grinding wheel was photographed. The difference in wear behavior between the ordered grinding wheel and the traditional disordered grinding wheel was compared and analyzed. The corresponding relationship between the ordered grinding wheel wear behavior and various signals was studied in depth. The results showed that the initial wear occurred during the 1st-80th grinding process, the stable wear occurred during the 81st-224th grinding process, and the serious wear occurred during the 225th-350th grinding process. Additionally, this research further extracted the key features of various signals, and used the Pearson correlation coefficient to identify the features highly related to grinding wheel wear. Subsequently, LDA was used to reduce the dimension of individual signal types. By comparing the recognition effects of different signal type combinations, the optimal combination was determined. Finally, this research proposed a DBO-ELM model for the recognition and classification of ordered grinding wheel wear state. Compared to four common machine learning models, namely KNN, SVM, ELM, and BP, the proposed DBO-ELM model demonstrated a classification accuracy of 94.86 %, which was increased by 25.57 %, 16 %, 16 % and 7.86 % respectively. This demonstrates that the DBO-ELM model proposed in this research has certain advantages and potential in the wear state recognition of abrasive clusters ordered grinding wheel.

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来源期刊
Wear
Wear 工程技术-材料科学:综合
CiteScore
8.80
自引率
8.00%
发文量
280
审稿时长
47 days
期刊介绍: Wear journal is dedicated to the advancement of basic and applied knowledge concerning the nature of wear of materials. Broadly, topics of interest range from development of fundamental understanding of the mechanisms of wear to innovative solutions to practical engineering problems. Authors of experimental studies are expected to comment on the repeatability of the data, and whenever possible, conduct multiple measurements under similar testing conditions. Further, Wear embraces the highest standards of professional ethics, and the detection of matching content, either in written or graphical form, from other publications by the current authors or by others, may result in rejection.
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