An acoustic dataset for surface roughness estimation in milling processMendeley Data
Machining process involves numerous variables that can influence the desired outcomes, with surface roughness being a critical quality index for machined products. Surface roughness is often a technical requirement for mechanical products as it can lead to chatter and impact the functional performan...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340924010709 |
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| author | N.R. Sakthivel Josmin Cherian Binoy B Nair Abburu Sahasransu L.N.V. Pratap Aratipamula Singamsetty Anish Gupta |
| author_facet | N.R. Sakthivel Josmin Cherian Binoy B Nair Abburu Sahasransu L.N.V. Pratap Aratipamula Singamsetty Anish Gupta |
| author_sort | N.R. Sakthivel |
| collection | DOAJ |
| description | Machining process involves numerous variables that can influence the desired outcomes, with surface roughness being a critical quality index for machined products. Surface roughness is often a technical requirement for mechanical products as it can lead to chatter and impact the functional performance of parts, especially those in contact with other materials. Therefore, predicting surface roughness is essential. This dataset comprises 7444 audio files containing acoustic signal samples recorded using a 44.1 kHz microphone during the milling of mild steel with a tungsten carbide tool on a BFW YF1 vertical milling machine. Various combinations of speed, feed and depth of cut were used, and surface roughness values measured using a Carl Zeiss E-35B profile-meter are provided for each combination. Additionally, an example workflow indicating the possible use of the data to estimate the surface roughness from the acoustic signals is presented. This dataset is the first publicly available resource for surface roughness measurement using sound signals in milling, offering significant potential for reuse in related research and applications. |
| format | Article |
| id | doaj-art-94c61e4c6a8b4824aea9f9e3e1f8564c |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-94c61e4c6a8b4824aea9f9e3e1f8564c2024-11-18T04:33:20ZengElsevierData in Brief2352-34092024-12-0157111108An acoustic dataset for surface roughness estimation in milling processMendeley DataN.R. Sakthivel0Josmin Cherian1Binoy B Nair2Abburu Sahasransu3L.N.V. Pratap Aratipamula4Singamsetty Anish Gupta5Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, IndiaDepartment of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, IndiaDepartment of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India; Corresponding author.Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, IndiaDepartment of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, IndiaDepartment of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, IndiaMachining process involves numerous variables that can influence the desired outcomes, with surface roughness being a critical quality index for machined products. Surface roughness is often a technical requirement for mechanical products as it can lead to chatter and impact the functional performance of parts, especially those in contact with other materials. Therefore, predicting surface roughness is essential. This dataset comprises 7444 audio files containing acoustic signal samples recorded using a 44.1 kHz microphone during the milling of mild steel with a tungsten carbide tool on a BFW YF1 vertical milling machine. Various combinations of speed, feed and depth of cut were used, and surface roughness values measured using a Carl Zeiss E-35B profile-meter are provided for each combination. Additionally, an example workflow indicating the possible use of the data to estimate the surface roughness from the acoustic signals is presented. This dataset is the first publicly available resource for surface roughness measurement using sound signals in milling, offering significant potential for reuse in related research and applications.http://www.sciencedirect.com/science/article/pii/S2352340924010709Condition monitoringMachine learningMachiningAcousticMilling |
| spellingShingle | N.R. Sakthivel Josmin Cherian Binoy B Nair Abburu Sahasransu L.N.V. Pratap Aratipamula Singamsetty Anish Gupta An acoustic dataset for surface roughness estimation in milling processMendeley Data Data in Brief Condition monitoring Machine learning Machining Acoustic Milling |
| title | An acoustic dataset for surface roughness estimation in milling processMendeley Data |
| title_full | An acoustic dataset for surface roughness estimation in milling processMendeley Data |
| title_fullStr | An acoustic dataset for surface roughness estimation in milling processMendeley Data |
| title_full_unstemmed | An acoustic dataset for surface roughness estimation in milling processMendeley Data |
| title_short | An acoustic dataset for surface roughness estimation in milling processMendeley Data |
| title_sort | acoustic dataset for surface roughness estimation in milling processmendeley data |
| topic | Condition monitoring Machine learning Machining Acoustic Milling |
| url | http://www.sciencedirect.com/science/article/pii/S2352340924010709 |
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