A neural network based approach for background noise reduction in airborne acoustic emission of a machining process
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| Publicado en: | Journal of Mechanical Science and Technology vol. 31, no. 7 (Jul 2017), p. 3171 |
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| Autor Principal: | |
| Outros autores: | , , , , |
| Publicado: |
Springer Nature B.V.
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| Acceso en liña: | Citation/Abstract Full Text - PDF |
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MARC
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| 001 | 2001518258 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1738-494X | ||
| 022 | |a 1976-3824 | ||
| 022 | |a 1226-4865 | ||
| 024 | 7 | |a 10.1007/s12206-017-0606-2 |2 doi | |
| 035 | |a 2001518258 | ||
| 045 | 2 | |b d20170701 |b d20170731 | |
| 084 | |a 137836 |2 nlm | ||
| 100 | 1 | |a Zafar, T |u National University of Sciences and Technology, Islamabad, Pakistan | |
| 245 | 1 | |a A neural network based approach for background noise reduction in airborne acoustic emission of a machining process | |
| 260 | |b Springer Nature B.V. |c Jul 2017 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Tool wear prediction has become an indispensable technique to prevent downtime in manufacturing and production processes. Airborne emission from a machining process using a low-cost microphone may provide a vital signal of tool health. However, the effect of background noise results in anomaly in data that may lead to wrong prediction of tool health. The paper presents an adaptive approach using neural networks for background noise filtration in acoustic signal for a turning process. Acoustic signal of a turning process is mixed with background noise from four different machines and introduced at different RPMs and feed-rate at a constant depth of cut. A comparison of Backpropagation neural network (BPNN), Self-organizing map and k-means clustering algorithm for noise filtration is investigated in this paper. In this regard, back-propagation neural network showed better performance with an average accuracy for all the four sources. It shows 100 % accuracy for grinding machine signal, 94.78 % accuracy for background signal from 3-axis milling machine, 45.57 % and 12.69 % for motor and 4-axis milling machine, respectively. Signal reconstruction is then done using Discrete cosine transform (DCT). The proposed technique shows a promising future for noise filtration in airborne acoustic data of a machining process. | |
| 653 | |a Comminution | ||
| 653 | |a Accuracy | ||
| 653 | |a Grinding mills | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Turning (machining) | ||
| 653 | |a Back propagation networks | ||
| 653 | |a Noise | ||
| 653 | |a Signal reconstruction | ||
| 653 | |a Noise prediction (aircraft) | ||
| 653 | |a Tool wear | ||
| 653 | |a Discrete cosine transform | ||
| 653 | |a Background noise | ||
| 653 | |a Acoustic noise | ||
| 653 | |a Signal processing | ||
| 653 | |a Neural networks | ||
| 653 | |a Clustering | ||
| 653 | |a Noise propagation | ||
| 653 | |a Milling (machining) | ||
| 653 | |a Noise reduction | ||
| 653 | |a Self organizing maps | ||
| 653 | |a Acoustic emission | ||
| 653 | |a Acoustics | ||
| 653 | |a Filtration | ||
| 653 | |a Downtime | ||
| 653 | |a Vector quantization | ||
| 700 | 1 | |a Kamal, K |u National University of Sciences and Technology, Islamabad, Pakistan | |
| 700 | 1 | |a Sheikh, Z |u PAEC, Islamabad, Pakistan | |
| 700 | 1 | |a Mathavan, S |u Nottingham Trent University, Nottingham, UK | |
| 700 | 1 | |a Ali, U |u National University of Sciences and Technology, Islamabad, Pakistan | |
| 700 | 1 | |a Hashmi, H |u National University of Sciences and Technology, Islamabad, Pakistan | |
| 773 | 0 | |t Journal of Mechanical Science and Technology |g vol. 31, no. 7 (Jul 2017), p. 3171 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2001518258/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2001518258/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |