Hierarchical Clustering of Surface Roughness Using Acoustic Emission Signals

Zvikomborero Hweju; Khaled Abou-El-Hossein

doi:10.4028/p-045wyf

Paper Titles

Deformation, Strain and Stress Concentration of Slab Narrow Side Induced by Different Reduction Amount
p.79

Study on Cracks of Last Freeze in Al-Mg Alloy Flat Ingots
p.85

Effect of Heat Treatment Temperatures on Microstructure and Mechanical Properties of Continuous Casting and Rolling Al-Mg Alloy
p.91

Effect of Natural Surface Defects on Flaking Failure of 13Cr-2Ni-2Mo and SUS 440C Stainless Steel Ball Bearings
p.97

Hierarchical Clustering of Surface Roughness Using Acoustic Emission Signals
p.105

Numerical Analysis Growth Kinetics of Dendrite Tip during Laser Welding Nickel-Based Single-Crystal Superalloy Part II: Expedient Favorite of Undercooling-Limited Epitaxy
p.113

Preparation of Film-Forming Amine Emulsion as Boiler Water Treatment Agent
p.125

Influence of a Micro-Arc Oxidation/Poly-Lactic Acid Composite Coating on Corrosion Resistance of Extruded Mg-2Zn-0.5Zr-1.5Dy (Mass%) Alloy
p.131

Thermal Spraying of MCrAlY Overlay Coating Using New Ethanol-Fueled HVOF Gun
p.139

HomeSolid State PhenomenaSolid State Phenomena Vol. 331Hierarchical Clustering of Surface Roughness Using...

Hierarchical Clustering of Surface Roughness Using Acoustic Emission Signals

Abstract:

The interaction among cutting parameters during the turning process is complex and non-linear, hence making linear predicting methods unsuitable for use. This study is a presentation of hierarchical clustering of surface roughness using acoustic emission signals during single point diamond turning of RSA-443. The intention of the study is to establish if clusters generated by portioning algorithms can be reliably used to understand the internal structure of data. Acquisition of acoustic emission signals has been achieved by a Kistler Piezotron sensor. AE root mean square, prominent frequency and peak rate are extracted from the processed captured AE signals while surface roughness is physically measured using a Kistler Taylor Hobson Profilometer. Validation of the generated clusters has been achieved by using the Purity measure. The computed purity is 1, which is an exhibition of the high quality of the hierarchical clustering result. Hence, clustering can be reliably utilized to understand the internal structure of surface roughness data during single point diamond turning.

You might also be interested in these eBooks

Mechanical Engineering, Materials Science and Civil Engineering

View Preview

View Preview

Info:

Periodical:

Solid State Phenomena (Volume 331)

Pages:

105-111

DOI:

https://doi.org/10.4028/p-045wyf

Citation:

Cite this paper

Online since:

April 2022

Authors:

Zvikomborero Hweju, Khaled Abou-El-Hossein*

Keywords:

Acoustic Emission Signals, Clusters, Purity, Surface Roughness

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] I. Abu-Mahfouz, A. Banerjee, and E. Rahman, Evaluation of Clustering Techniques to Predict Surface Roughness during Turning of Stainless-Steel Using Vibration Signals,, Materials, vol. 14, (2021).

DOI: 10.3390/ma14175050

Google Scholar

[2] H. Zhang, J. Liu, L. Chen, N. Chen, and X. Yang, Fuzzy Clustering Algorithm with Non-Neighborhood Spatial Information for Surface Roughness Measurement Based on the Reflected Aliasing Images,, Sensors, vol. 19, (2019).

DOI: 10.3390/s19153285

Google Scholar

[3] J. M. Griffin, F. Diaz, E. Geerling, M. Clasing, V. Ponce, ChrisTaylor, et al., Control of deviations and prediction of surface roughness from micro machining of THz waveguides using acoustic emission signals,, Mechanical Systems and Signal Processing, vol. 85, pp.1020-1034, (2017).

DOI: 10.1016/j.ymssp.2016.09.016

Google Scholar

[4] J. H. Oh and S. H. Lee, Prediction of surface roughness in magnetic abrasive finishing using acoustic emission and force sensor data fusion,, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, vol. 225, (2011).

DOI: 10.1177/09544054jem2055

Google Scholar

[5] C. Beggan, M. Woulfe, P. Young, and G. Byrne, Using Acoustic Emission to Predict Surface Quality,, International Journal of Advanced Manufacturing Technology, vol. 15, p.737–742, (1999).

DOI: 10.1007/s001700050126

Google Scholar

[6] Z. Hweju and K. Abou-El-Hossein, Surface Roughness Prediction Based on Acoustic Emission Signals in High-Precision Diamond Turning of Rapidly Solidified Optical Aluminum Grade (RSA443),, Key Engineering Materials, vol. 841, pp.363-368, (2020).

DOI: 10.4028/www.scientific.net/kem.841.363

Google Scholar

[7] J. Bhaskaran, M. Murugan, N. Balashanmugam, and M. Chellamalai, Monitoring of hard turning using acoustic emission signal,, Journal of Mechanical Science and Technology, vol. 26, p.609–615, (2012).

DOI: 10.1007/s12206-011-1036-1

Google Scholar

[8] P. Garikapati, K. Balamurugan, T. P. Latchoumi, and R. Malkapuram, A Cluster-Profile Comparative Study on Machining AlSi7/63% of SiC Hybrid Composite Using Agglomerative Hierarchical Clustering and K-Means,, Silicon, vol. 13, p.961–972, (2021).

DOI: 10.1007/s12633-020-00447-9

Google Scholar

[9] A. J. Torabi, M. J. Er, X. Li, B. S. Lim, and G. O. Peen, Application of Clustering Methods for Online Tool Condition Monitoring and Fault Diagnosis in High-Speed Milling Processes,, IEEE Systems Journal, vol. 10, pp.721-732, (2016).

DOI: 10.1109/jsyst.2015.2425793

Google Scholar

[10] K. Choi and C. Jun, A systematic approach to the Kansei factors of tactile sense regarding the surface roughness,, Applied Ergonomics, vol. 38, pp.53-63, (2007).

DOI: 10.1016/j.apergo.2006.01.003

Google Scholar

[11] R. Zhou, Y. Zhang, S. Feng, and N. Luktarhan, A Novel Hierarchical Clustering Algorithm Based on Density Peaks for Complex Datasets,, Complexity, vol. 2018, p.2032461, 2018/07/18 (2018).

DOI: 10.1155/2018/2032461

Google Scholar