Decision support system for tool condition monitoring in milling process using artificial neural network
Joint Authors
Source
Journal of Engineering Research
Issue
Vol. 10, Issue 4 B (31 Dec. 2022), pp.142-155, 14 p.
Publisher
Kuwait University Academic Publication Council
Publication Date
2022-12-31
Country of Publication
Kuwait
No. of Pages
14
Main Subjects
Abstract EN
This work discusses the development of tool condition monitoring system (TCMs) during milling of AISI stainless steel 304 using sound pressure and vibration signals.
response surface methodology (RSM) was used to design the experiments.
the various milling parameters and vegetable-based cutting fluids (VBCFs) were optimized to reduce the surface roughness and flank wear.
the experimental results reveal the direct relationship between the flank wear and sound and vibration signals.
the various statistical parameters were extracted from the measured signals and given as input data to train the artificial neural network (ANN).
From the developed ANN model, the flank wear was predicted with the mean squared error (MSE) of 0.0656 mm.
American Psychological Association (APA)
Mohanraj, T.& Tamilvanan, A.. 2022. Decision support system for tool condition monitoring in milling process using artificial neural network. Journal of Engineering Research،Vol. 10, no. 4 B, pp.142-155.
https://search.emarefa.net/detail/BIM-1494632
Modern Language Association (MLA)
Mohanraj, T.& Tamilvanan, A.. Decision support system for tool condition monitoring in milling process using artificial neural network. Journal of Engineering Research Vol. 10, no. 4 B (Dec. 2022), pp.142-155.
https://search.emarefa.net/detail/BIM-1494632
American Medical Association (AMA)
Mohanraj, T.& Tamilvanan, A.. Decision support system for tool condition monitoring in milling process using artificial neural network. Journal of Engineering Research. 2022. Vol. 10, no. 4 B, pp.142-155.
https://search.emarefa.net/detail/BIM-1494632
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 152-155.
Record ID
BIM-1494632