Classification of the machine state in turning processes by using the acoustic emission

被引:1
|
作者
Ocampo, Daniel Diaz [1 ]
Aubart, Daniel [2 ]
Gonzalez, German [2 ]
Zanger, Frederik [2 ]
Heizmann, Michael [1 ]
机构
[1] Karlsruhe Inst Technol KIT, Inst Ind Informat Technol IIIT, Hertzstr 16, D-76187 Karlsruhe, Germany
[2] Karlsruhe Inst Technol KIT, Wbk Inst Prod Sci, Kaiserstr 12, D-76131 Karlsruhe, Germany
来源
PRODUCTION ENGINEERING-RESEARCH AND DEVELOPMENT | 2024年 / 18卷 / 02期
关键词
Classification; Machine state; Acoustic emission; Turning; CHIP FORMATION; FREQUENCY;
D O I
10.1007/s11740-024-01266-2
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Processing digital information stands as a crucial foundation of Industry 4.0, facilitating a spectrum of activities from monitoring processes to their understanding and optimization. The application of data processing techniques, including feature extraction and classification, coupled with the identification of the most suitable features for specific purposes, continues to pose a significant challenge in the manufacturing sector. This research investigates the suitability of classification methods for machine and tool state classification by employing acoustic emission (AE) sensors during the dry turning of Ti6Al4V. Features such as quantiles, Fourier coefficients, and mel-frequency cepstral coefficients are extracted from the AE signals to facilitate classification. From this features the 20 best are selected for the classification to reduce the dimension of the feature space and redundancy. Algorithms including decision tree, k-nearest-neighbors (KNN), and quadratic discriminant analysis (QDA) are tested for the classification of machine states. Of these, QDA exhibits the highest accuracy at 98.6 %. Nonetheless, an examination of the confusion matrix reveals that certain classes, influenced by imbalanced training data, exhibit a lower prediction accuracy. In summary, the study affirms the potential of AE sensors for machine state recognition and tool condition monitoring. Although QDA emerges as the most acurate classifier, there remains an avenue for refinement, particularly in training data optimization and decision-making processes, to augment accuracy.
引用
收藏
页码:289 / 297
页数:9
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