Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/100122
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dc.contributor.authorLiu, Tien-I.en
dc.contributor.authorSong, Shin-Daen
dc.contributor.authorLiu, Georgeen
dc.contributor.authorWu, Zhangen
dc.date.accessioned2013-09-20T02:37:24Zen
dc.date.accessioned2019-12-06T20:17:06Z-
dc.date.available2013-09-20T02:37:24Zen
dc.date.available2019-12-06T20:17:06Z-
dc.date.copyright2012en
dc.date.issued2012en
dc.identifier.citationLiu, T. I., Song, S. D., Liu, G., & Wu, Z. (2012). Online monitoring and measurements of tool wear for precision turning of stainless steel parts. The international journal of advanced manufacturing technology, 65(9-12), 1397-1407.en
dc.identifier.urihttps://hdl.handle.net/10356/100122-
dc.identifier.urihttp://hdl.handle.net/10220/13564en
dc.description.abstractOnline monitoring and measurements of tool wear were carried out using cutting forces for precision turning of stainless steel parts. The best combination of features was selected from 14 features extracted from force signals by using a Sequential Forward Search algorithm. Back-propagation neural networks (BPNs) used two features for online classification. When the adaptive neuro-fuzzy inference system (ANFIS) was applied, seven features were needed for the classification. For online measurements, only one feature is needed for BPN. Three features are needed for ANFIS for online measurements. For online classification of turning tool conditions, a 2 × 20 × 1 BPN can achieve a success rate of higher than 86% while a 7 × 2 ANFIS can reach a success rate of higher than 96%. For online measurements of tool wear, the estimation error can be as low as 1.37% when a 1 × 20 × 1 BPN was used while the error can be as low as 0.56% using a 3 × 3 ANFIS. Therefore, the 3 × 3 ANFIS can be used first to predict the degradation of tool conditions during the turning process. It can also be used to measure the tool wear online so as to take feedback control action to enhance accuracy of the process. Once the detected tool wear is close to the worn-out threshold, the 7 × 2 ANFIS will be then applied to classify the tool conditions in order to stop the turning operation on time automatically so as to assure the quality of products and to avoid catastrophic failure.en
dc.language.isoenen
dc.relation.ispartofseriesThe international journal of advanced manufacturing technologyen
dc.subjectDRNTU::Engineering::Mechanical engineeringen
dc.titleOnline monitoring and measurements of tool wear for precision turning of stainless steel partsen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen
dc.identifier.doihttp://dx.doi.org/10.1007/s00170-012-4265-2en
item.grantfulltextnone-
item.fulltextNo Fulltext-
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