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Production & Manufacturing Research
An Open Access Journal
Volume 10, 2022 - Issue 1
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Articles

Feature representation of audible sound signal in monitoring surface roughness of the grinding process

V. H. Nguyena Mechanical Engineering Department, Hanoi University of Industry, Hanoi, VietNam;b College of Mechanical Engineering, Hunan University, Changsha, Hunan, ChinaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-4345-4630
ORCID Icon,
T. H. Vuonga Mechanical Engineering Department, Hanoi University of Industry, Hanoi, VietNam
&
Q. T. Nguyena Mechanical Engineering Department, Hanoi University of Industry, Hanoi, VietNam
Pages 606-623 | Received 15 Feb 2022, Accepted 28 Jul 2022, Published online: 05 Aug 2022

Figures & data

Figure 1. A sample of AS signal is decomposed by EEMD and EMD: (a) original signal, (b) using EEMD and (c) using EMD.

Figure 1. A sample of AS signal is decomposed by EEMD and EMD: (a) original signal, (b) using EEMD and (c) using EMD.

Figure 2. Flowchart of predicting surface roughness via EEMD-IMPE-PSO-LS-SVR method.

Figure 2. Flowchart of predicting surface roughness via EEMD-IMPE-PSO-LS-SVR method.

Figure 3. Diagram of the experimental setup, a) experimental setup, b) grinding wheel.

Figure 3. Diagram of the experimental setup, a) experimental setup, b) grinding wheel.

Figure 4. Diagram of the measurement.

Figure 4. Diagram of the measurement.

Figure 5. Scatter diagram of the training data.

Figure 5. Scatter diagram of the training data.

Table 1. Surface roughness prediction results of training data.

Table 2. Surface roughness prediction results of testing data.

Figure 6. Comparison between prediction values and experimental value of test data.

Figure 6. Comparison between prediction values and experimental value of test data.

Table 3. Comparison of prediction accuracy between different predictor model.

Figure 7. Comparison diagram of the prediction accuracy (a) training data, (b) testing data.

Figure 7. Comparison diagram of the prediction accuracy (a) training data, (b) testing data.

Table 4. Comparison of prediction accuracy between different extracted feature sets.

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