A Novel Peak Detection Algorithm for Use in the Study of Machining Chip Segmentation

Published

November 7, 2007

Author(s)

Eric P. Whitenton, Robert W. Ivester, Jarred C. Heigel

Abstract

The study of how metal deforms and flows as parts are machined yields important insights into the metal cutting process. Improvements in high-speed digital imaging and image processing software promise to improve our understanding of the tool-workpiece interface and verify the accuracy of finite element modeling simulations. This will ultimately enable industry to improve machining processes and make parts faster at less cost. This report describes the design and results of an automated system to estimate chip segmentation frequency. High-speed images of machining chips are combined with displacement vector mapping and processing. As part of the displacement vector map processing, a novel peak detection algorithm using an inflection list was developed which minimizes a priori assumptions and yields information used in sensitivity analysis. However, further work is needed before an uncertainty analysis may be completed.

Proceedings Title

ISCA Proceedings of the 20th International Conference on Computers and Their Applications in Industry and Engineering (CAINE-2007), November 7-9, 2007, San Francisco, CA

Report Number

823018

Conference Dates

November 7-9, 2007

Conference Location

San Francisco, CA

Conference Title

ISCA 20th International Conference on Computers and Their Applications in Industry and Engineering

Pub Type

Conferences

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Keywords

high-speed video, image processing, machining chip segmentation, peak detection, vector map

Citation

Whitenton, E. , Ivester, R. and Heigel, J. (2007), A Novel Peak Detection Algorithm for Use in the Study of Machining Chip Segmentation, ISCA Proceedings of the 20th International Conference on Computers and Their Applications in Industry and Engineering (CAINE-2007), November 7-9, 2007, San Francisco, CA, San Francisco, CA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=823018 (Accessed April 19, 2024)