Pattern recognition in audible sound energy emissions of AISI 52100 hardened steel turning: a MFCC-based approach
The main objective in machining processes is to produce a high-quality surface finish which, however, can be measured only at the end of the machining cycle. A more preferable method would be to monitor the quality during the cycle, what result a real-time, low-cost, and accurate monitoring method t...
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| Veröffentlicht in: | International journal of advanced manufacturing technology 2017-02, Vol.88 (5-8), p.1383-1392 |
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| container_title | International journal of advanced manufacturing technology |
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| creator | Frigieri, Edielson P. Brito, Tarcisio G. Ynoguti, Carlos A. Paiva, Anderson P. Ferreira, João R. Balestrassi, Pedro P. |
| description | The main objective in machining processes is to produce a high-quality surface finish which, however, can be measured only at the end of the machining cycle. A more preferable method would be to monitor the quality during the cycle, what result a real-time, low-cost, and accurate monitoring method that can dynamically adjust the machining parameters and keep the target surface finish. Motivated by this premise, results of investigation on the relationship between emitted sound signal and surface finish during turning process are reported in this paper. Through experiments with AISI 52100 hardened steel, this work shows that such a correlation does exist presenting strong evidences that Mel-Frequency Cepstral Coefficients, extracted from sound energy, can detect different surface roughness levels, what makes it a promising feature for real-time process quality monitoring methods. |
| doi_str_mv | 10.1007/s00170-016-8748-4 |
| format | Article |
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A more preferable method would be to monitor the quality during the cycle, what result a real-time, low-cost, and accurate monitoring method that can dynamically adjust the machining parameters and keep the target surface finish. Motivated by this premise, results of investigation on the relationship between emitted sound signal and surface finish during turning process are reported in this paper. 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| subjects | Bearing steels CAE) and Design Chromium steels Computer-Aided Engineering (CAD Engineering Industrial and Production Engineering Mechanical Engineering Media Management Monitoring Original Article Pattern recognition Process parameters Real time Signal processing Sound Surface finish Surface roughness Turning (machining) |
| title | Pattern recognition in audible sound energy emissions of AISI 52100 hardened steel turning: a MFCC-based approach |
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