Data-Driven Modeling and Enhancement of Surface Quality in Milling Based on Sound Signals
The present study introduces an AI (Artificial Intelligence) framework for surface roughness assessment in milling operations through sound signal processing. As industrial demands escalate for in-process quality control solutions, the proposed system leverages audio data to estimate surface finish...
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| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Journal of Manufacturing and Materials Processing |
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| Online Access: | https://www.mdpi.com/2504-4494/9/7/231 |
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| author | Paschalis Charalampous |
| author_facet | Paschalis Charalampous |
| author_sort | Paschalis Charalampous |
| collection | DOAJ |
| description | The present study introduces an AI (Artificial Intelligence) framework for surface roughness assessment in milling operations through sound signal processing. As industrial demands escalate for in-process quality control solutions, the proposed system leverages audio data to estimate surface finish states without interrupting production. In order to address this, a novel classification approach was developed that maps audio waveform data into predictive indicators of surface quality. In particular, an experimental dataset was employed consisting of sound signals that were captured during milling procedures applying various machining conditions, where each signal was labeled with a corresponding roughness quality obtained via offline metrology. The formulated classification pipeline commences with audio acquisition, resampling, and normalization to ensure consistency across the dataset. These signals are then transformed into Mel-Frequency Cepstral Coefficients (MFCCs), which yield a compact time–frequency representation optimized for human auditory perception. Next, several AI algorithms were trained in order to classify these MFCCs into predefined surface roughness categories. Finally, the results of the work demonstrate that sound signals could contain sufficient discriminatory information enabling a reliable classification of surface finish quality. This approach not only facilitates in-process monitoring but also provides a foundation for intelligent manufacturing systems capable of real-time quality assurance. |
| format | Article |
| id | doaj-art-eaf2fe59742842e9b1a304494face16a |
| institution | DOAJ |
| issn | 2504-4494 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Manufacturing and Materials Processing |
| spelling | doaj-art-eaf2fe59742842e9b1a304494face16a2025-08-20T03:08:06ZengMDPI AGJournal of Manufacturing and Materials Processing2504-44942025-07-019723110.3390/jmmp9070231Data-Driven Modeling and Enhancement of Surface Quality in Milling Based on Sound SignalsPaschalis Charalampous0Centre for Research and Technology Hellas—Information Technologies Institute (CERTH/ITI), 57001 Thessaloniki, GreeceThe present study introduces an AI (Artificial Intelligence) framework for surface roughness assessment in milling operations through sound signal processing. As industrial demands escalate for in-process quality control solutions, the proposed system leverages audio data to estimate surface finish states without interrupting production. In order to address this, a novel classification approach was developed that maps audio waveform data into predictive indicators of surface quality. In particular, an experimental dataset was employed consisting of sound signals that were captured during milling procedures applying various machining conditions, where each signal was labeled with a corresponding roughness quality obtained via offline metrology. The formulated classification pipeline commences with audio acquisition, resampling, and normalization to ensure consistency across the dataset. These signals are then transformed into Mel-Frequency Cepstral Coefficients (MFCCs), which yield a compact time–frequency representation optimized for human auditory perception. Next, several AI algorithms were trained in order to classify these MFCCs into predefined surface roughness categories. Finally, the results of the work demonstrate that sound signals could contain sufficient discriminatory information enabling a reliable classification of surface finish quality. This approach not only facilitates in-process monitoring but also provides a foundation for intelligent manufacturing systems capable of real-time quality assurance.https://www.mdpi.com/2504-4494/9/7/231machiningArtificial Intelligenceroughnessmilling conditionsquality control |
| spellingShingle | Paschalis Charalampous Data-Driven Modeling and Enhancement of Surface Quality in Milling Based on Sound Signals Journal of Manufacturing and Materials Processing machining Artificial Intelligence roughness milling conditions quality control |
| title | Data-Driven Modeling and Enhancement of Surface Quality in Milling Based on Sound Signals |
| title_full | Data-Driven Modeling and Enhancement of Surface Quality in Milling Based on Sound Signals |
| title_fullStr | Data-Driven Modeling and Enhancement of Surface Quality in Milling Based on Sound Signals |
| title_full_unstemmed | Data-Driven Modeling and Enhancement of Surface Quality in Milling Based on Sound Signals |
| title_short | Data-Driven Modeling and Enhancement of Surface Quality in Milling Based on Sound Signals |
| title_sort | data driven modeling and enhancement of surface quality in milling based on sound signals |
| topic | machining Artificial Intelligence roughness milling conditions quality control |
| url | https://www.mdpi.com/2504-4494/9/7/231 |
| work_keys_str_mv | AT paschalischaralampous datadrivenmodelingandenhancementofsurfacequalityinmillingbasedonsoundsignals |