Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation Khorasani, Amirmahyar and Yazdi, Mohammad Reza Soleymani 2017, Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation, International journal of advanced manufacturing technology, vol. 93, no. 1-4, pp. 141-151, doi: 10.1007/s00170-015-7922-4. Attached Files Name Description MIMEType Size Downloads Title Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation Author(s) Khorasani, Amirmahyar Yazdi, Mohammad Reza Soleymani Journal name International journal of advanced manufacturing technology Volume number 93 Issue number 1-4 Start page 141 End page 151 Total pages 11 Publisher Springer Place of publication London, Eng. Publication date 2017-10 ISSN 0268-3768 1433-3015 Keyword(s) Science & Technology Technology Automation & Control Systems Engineering, Manufacturing Engineering Artificial neural networks Milling Process Simulation Surface Roughness FUZZY INFERENCE SYSTEM TURNING OPERATIONS CUTTING CONDITIONS GENETIC ALGORITHM PREDICTION OPTIMIZATION PARAMETERS REGRESSION FORCES MODEL Language eng DOI 10.1007/s00170-015-7922-4 Field of Research 091004 Machining 09 Engineering 08 Information And Computing Sciences 01 Mathematical Sciences Socio Economic Objective 970111 Expanding Knowledge in the Medical and Health Sciences HERDC Research category C1 Refereed article in a scholarly journal ERA Research output type C Journal article Copyright notice ©2015, Springer Persistent URL http://hdl.handle.net/10536/DRO/DU:30081044 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Engineering Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Fri, 29 Jan 2016, 16:22:07 EST Sat, 30 Jan 2016, 04:02:32 EST Sat, 30 Jan 2016, 05:10:48 EST Mon, 29 Feb 2016, 14:34:23 EST Mon, 29 Feb 2016, 14:35:20 EST Mon, 29 Feb 2016, 14:38:41 EST Wed, 09 Nov 2016, 11:41:55 EST Mon, 12 Dec 2016, 15:01:04 EST Tue, 27 Jun 2017, 12:48:24 EST Fri, 22 Sep 2017, 14:45:21 EST Mon, 25 Sep 2017, 07:53:18 EST Sat, 04 Nov 2017, 01:32:00 EST Wed, 25 Jul 2018, 11:01:11 EST Wed, 25 Jul 2018, 11:03:18 EST Filtered Full Citation counts:   Cited 35 times in TR Web of Science Cited 43 times in Scopus Search Google Scholar Access Statistics: 397 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Fri, 29 Jan 2016, 16:22:07 EST

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