The development of in-process surface roughness prediction systems in turning operation using accelerometer

dc.contributor.advisor	Joseph C. Chen
dc.contributor.author	Huang, Hanming
dc.contributor.department	Industrial Education and Technology
dc.date	2018-08-24T18:03:52.000
dc.date.accessioned	2020-07-02T05:41:47Z
dc.date.available	2020-07-02T05:41:47Z
dc.date.copyright	Mon Jan 01 00:00:00 UTC 2001
dc.date.issued	2001-01-01
dc.description.abstract	<p>Three in-process surface roughness prediction (ISRP) systems using linear multiple regression, fuzzy logic, and fuzzy nets algorisms, respectively, were developed to allow the prediction of real time surface roughness of a work piece on a turning operation. The surface roughness is predicted from feed rate, spindle speed, depth of cut, and machining vibration that is detected and collected by an accelerometer.;Two groups of data were collected for two cutters with nose radii of 0.016 and 0.031 inches, respective. A total of 162 training data sets and 54 testing data sets for each cutter were applied to train and test the system. While the multiple-regression-based system applied the linear relationships of the dependent variables and the dependent variable for the prediction, the fuzzy-logic-based and the fuzzy-nets-based systems relied on fuzzy theory for the prediction. The fuzzy rule banks employed in the fuzzy-logic-based system was generated with expert's experiences as well as observation results from the experiments. Whereas, the rule banks employed in the fuzz-nets-system were rule banks self-extracted from the training data by the fuzzy-nets self-learning algorithm.;The predicted surface roughness values were compared with corresponding measured values. The average prediction accuracy with the three algorithms, linear multiple regression, fuzzy logic, and fuzzy nets algorisms, was 92.78%, 89.06%, and 95.70%, respectively. The use of the accelerometer was found valuable in increasing the prediction The Fuzzy-nets-based In-process Surface Roughness Prediction System was considered the best among the three tested systems. This conclusion relies on not only the best average prediction accuracy achieved, but also the self-learning ability of the fuzzy nets algorism.</p>
dc.format.mimetype	application/pdf
dc.identifier	archive/lib.dr.iastate.edu/rtd/435/
dc.identifier.articleid	1434
dc.identifier.contextkey	6073779
dc.identifier.doi	https://doi.org/10.31274/rtd-180813-190
dc.identifier.s3bucket	isulib-bepress-aws-west
dc.identifier.submissionpath	rtd/435
dc.identifier.uri	https://dr.lib.iastate.edu/handle/20.500.12876/76974
dc.language.iso	en
dc.source.bitstream	archive/lib.dr.iastate.edu/rtd/435/r_3003249.pdf\|\|\|Sat Jan 15 00:16:10 UTC 2022
dc.subject.disciplines	Industrial Engineering
dc.subject.keywords	Industrial education and technology
dc.title	The development of in-process surface roughness prediction systems in turning operation using accelerometer
dc.type	article
dc.type.genre	dissertation
dspace.entity.type	Publication
thesis.degree.level	dissertation
thesis.degree.name	Doctor of Philosophy

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