Vision-based modal analysis of cutting tools. (January 2021)
- Record Type:
- Journal Article
- Title:
- Vision-based modal analysis of cutting tools. (January 2021)
- Main Title:
- Vision-based modal analysis of cutting tools
- Authors:
- Gupta, Pulkit
Rajput, Harsh Singh
Law, Mohit - Abstract:
- Graphical abstract: Highlights: Vision-based cutting tool motion registration methods are presented. Tool motion is recorded with low- and high-speed cameras with high resolutions. Motion is estimated using edge detection, optical flow, and DIC schemes. Tool motion from vision-based measurements agree with integrated accelerations. Modal parameters from vision-based response compare well with standard procedures. Abstract: This paper presents the use of vision-based methods for cutting tool motion registration and modal analysis. Motion of three illustrative tools were recorded using low- and high-speed cameras with sufficiently high resolutions. The tool's own features are used to register motion. Pixels within images from recordings of the vibrating tools are treated as non-contact motion sensors. Comparative analysis of three different methods of motion registration are presented to evaluate their suitability for the application of interest. These include variants of expanded edge detection and tracking schemes, expanded optical flow-based schemes, and established digital image correlation methods. Performance of different methods was observed to be governed by the tool's own features, illumination conditions, noise, and the image acquisition parameters. Extracted motion was benchmarked against twice integrated measured tool point accelerations, and motion was generally observed to compare well. Modal parameters extracted from vision-based measurements were also observedGraphical abstract: Highlights: Vision-based cutting tool motion registration methods are presented. Tool motion is recorded with low- and high-speed cameras with high resolutions. Motion is estimated using edge detection, optical flow, and DIC schemes. Tool motion from vision-based measurements agree with integrated accelerations. Modal parameters from vision-based response compare well with standard procedures. Abstract: This paper presents the use of vision-based methods for cutting tool motion registration and modal analysis. Motion of three illustrative tools were recorded using low- and high-speed cameras with sufficiently high resolutions. The tool's own features are used to register motion. Pixels within images from recordings of the vibrating tools are treated as non-contact motion sensors. Comparative analysis of three different methods of motion registration are presented to evaluate their suitability for the application of interest. These include variants of expanded edge detection and tracking schemes, expanded optical flow-based schemes, and established digital image correlation methods. Performance of different methods was observed to be governed by the tool's own features, illumination conditions, noise, and the image acquisition parameters. Extracted motion was benchmarked against twice integrated measured tool point accelerations, and motion was generally observed to compare well. Modal parameters extracted from vision-based measurements were also observed to agree with those extracted using more traditional experimental modal analysis procedures using a contact type accelerometer as the transducer. Since methods presented are generalized, they can suitably be adapted for other applications of interest. … (more)
- Is Part Of:
- CIRP journal of manufacturing science and technology. Volume 32(2021)
- Journal:
- CIRP journal of manufacturing science and technology
- Issue:
- Volume 32(2021)
- Issue Display:
- Volume 32, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 32
- Issue:
- 2021
- Issue Sort Value:
- 2021-0032-2021-0000
- Page Start:
- 91
- Page End:
- 107
- Publication Date:
- 2021-01
- Subjects:
- Dynamics -- Cutting tool -- Computer vision -- Vibration -- Image processing -- Digital image correlation
Manufacturing processes -- Periodicals
670.5 - Journal URLs:
- http://www.sciencedirect.com/science/journal/17555817 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.cirpj.2020.11.012 ↗
- Languages:
- English
- ISSNs:
- 1755-5817
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3267.425000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 25580.xml