Online monitoring and measurements of tool wear for precision turning of stainless steel parts

Online 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 ne...

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Main Authors: Liu, Tien-I., Song, Shin-Da, Liu, George, Wu, Zhang
Other Authors: School of Mechanical and Aerospace Engineering
Format: Article
Language:English
Published: 2013
Subjects:
Online Access:https://hdl.handle.net/10356/100122
http://hdl.handle.net/10220/13564
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1001222020-03-07T13:19:27Z Online monitoring and measurements of tool wear for precision turning of stainless steel parts Liu, Tien-I. Song, Shin-Da Liu, George Wu, Zhang School of Mechanical and Aerospace Engineering DRNTU::Engineering::Mechanical engineering Online 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. 2013-09-20T02:37:24Z 2019-12-06T20:17:06Z 2013-09-20T02:37:24Z 2019-12-06T20:17:06Z 2012 2012 Journal Article Liu, 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. https://hdl.handle.net/10356/100122 http://hdl.handle.net/10220/13564 10.1007/s00170-012-4265-2 en The international journal of advanced manufacturing technology
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Engineering::Mechanical engineering
spellingShingle DRNTU::Engineering::Mechanical engineering
Liu, Tien-I.
Song, Shin-Da
Liu, George
Wu, Zhang
Online monitoring and measurements of tool wear for precision turning of stainless steel parts
description Online 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.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Liu, Tien-I.
Song, Shin-Da
Liu, George
Wu, Zhang
format Article
author Liu, Tien-I.
Song, Shin-Da
Liu, George
Wu, Zhang
author_sort Liu, Tien-I.
title Online monitoring and measurements of tool wear for precision turning of stainless steel parts
title_short Online monitoring and measurements of tool wear for precision turning of stainless steel parts
title_full Online monitoring and measurements of tool wear for precision turning of stainless steel parts
title_fullStr Online monitoring and measurements of tool wear for precision turning of stainless steel parts
title_full_unstemmed Online monitoring and measurements of tool wear for precision turning of stainless steel parts
title_sort online monitoring and measurements of tool wear for precision turning of stainless steel parts
publishDate 2013
url https://hdl.handle.net/10356/100122
http://hdl.handle.net/10220/13564
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