Vladimir Mitrović, Milan Zdravković, Milan Trifunović, Miloš Madić, Predrag Janković
{"title":"C45E钢纵向车削过程中声音分量与切削力分量关系研究数据集。","authors":"Vladimir Mitrović, Milan Zdravković, Milan Trifunović, Miloš Madić, Predrag Janković","doi":"10.1016/j.dib.2025.112051","DOIUrl":null,"url":null,"abstract":"<p><p>Monitoring cutting force components (tangential, radial, and axial) in longitudinal turning helps identifying unfavourable cutting conditions. However, force sensor can be a costly investment, in addition to being technically challenging to integrate into the machine tool. The primary purpose of this data repository is to provide a way to evaluate the potential that sound could hold for estimating cutting force components, with the idea to possibly simplify the monitoring system. Data was collected by monitoring sound and cutting force components during longitudinal turning of C45E steel. In total, 100 experiment trials were carried out with different settings of cutting speed, depth of cut and feed rate. The sensory data consists of raw sound recordings and measurements of cutting force components, for each experiment trial. In addition, datasets with extracted sound and force features are provided, along with code used for this purpose. The sound features dataset is particularly extensive, including 260 extracted sound features in time and frequency domain. Both feature extraction process and initial exploratory data analysis are presented, making a base ground for further analysis. The researchers in manufacturing engineering, acoustics and other relevant fields can either use datasets with extracted features for conducting analysis or use only raw data and compile their own methodology for feature extraction and analysis.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"112051"},"PeriodicalIF":1.4000,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12493221/pdf/","citationCount":"0","resultStr":"{\"title\":\"Dataset for exploring relation between sound and cutting forces components in longitudinal turning of C45E steel.\",\"authors\":\"Vladimir Mitrović, Milan Zdravković, Milan Trifunović, Miloš Madić, Predrag Janković\",\"doi\":\"10.1016/j.dib.2025.112051\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Monitoring cutting force components (tangential, radial, and axial) in longitudinal turning helps identifying unfavourable cutting conditions. However, force sensor can be a costly investment, in addition to being technically challenging to integrate into the machine tool. The primary purpose of this data repository is to provide a way to evaluate the potential that sound could hold for estimating cutting force components, with the idea to possibly simplify the monitoring system. Data was collected by monitoring sound and cutting force components during longitudinal turning of C45E steel. In total, 100 experiment trials were carried out with different settings of cutting speed, depth of cut and feed rate. The sensory data consists of raw sound recordings and measurements of cutting force components, for each experiment trial. In addition, datasets with extracted sound and force features are provided, along with code used for this purpose. The sound features dataset is particularly extensive, including 260 extracted sound features in time and frequency domain. Both feature extraction process and initial exploratory data analysis are presented, making a base ground for further analysis. The researchers in manufacturing engineering, acoustics and other relevant fields can either use datasets with extracted features for conducting analysis or use only raw data and compile their own methodology for feature extraction and analysis.</p>\",\"PeriodicalId\":10973,\"journal\":{\"name\":\"Data in Brief\",\"volume\":\"62 \",\"pages\":\"112051\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2025-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12493221/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data in Brief\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.dib.2025.112051\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/10/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data in Brief","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.dib.2025.112051","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/10/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
Dataset for exploring relation between sound and cutting forces components in longitudinal turning of C45E steel.
Monitoring cutting force components (tangential, radial, and axial) in longitudinal turning helps identifying unfavourable cutting conditions. However, force sensor can be a costly investment, in addition to being technically challenging to integrate into the machine tool. The primary purpose of this data repository is to provide a way to evaluate the potential that sound could hold for estimating cutting force components, with the idea to possibly simplify the monitoring system. Data was collected by monitoring sound and cutting force components during longitudinal turning of C45E steel. In total, 100 experiment trials were carried out with different settings of cutting speed, depth of cut and feed rate. The sensory data consists of raw sound recordings and measurements of cutting force components, for each experiment trial. In addition, datasets with extracted sound and force features are provided, along with code used for this purpose. The sound features dataset is particularly extensive, including 260 extracted sound features in time and frequency domain. Both feature extraction process and initial exploratory data analysis are presented, making a base ground for further analysis. The researchers in manufacturing engineering, acoustics and other relevant fields can either use datasets with extracted features for conducting analysis or use only raw data and compile their own methodology for feature extraction and analysis.
期刊介绍:
Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.