Prediction of surface roughness and cutting zone temperature in dry turning processes of AISI304 stainless steel using ANFIS with PSO learning
Abstract This paper presents an approach for modeling and prediction of both surface roughness and cutting zone temperature in turning of AISI304 austenitic stainless steel using multi-layer coated (TiCN + TiC + TiCN + TiN) tungsten carbide tools. The proposed approach is based on an adaptive neuro-...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Aydın, Mehmet [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2012 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag London 2012 |
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| Übergeordnetes Werk: |
Enthalten in: The international journal of advanced manufacturing technology - London : Springer, 1985, 67(2012), 1-4 vom: 16. Okt., Seite 957-967 |
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| Übergeordnetes Werk: |
volume:67 ; year:2012 ; number:1-4 ; day:16 ; month:10 ; pages:957-967 |
| Links: |
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| DOI / URN: |
10.1007/s00170-012-4540-2 |
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| Katalog-ID: |
SPR001763881 |
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| 245 | 1 | 0 | |a Prediction of surface roughness and cutting zone temperature in dry turning processes of AISI304 stainless steel using ANFIS with PSO learning |
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| 520 | |a Abstract This paper presents an approach for modeling and prediction of both surface roughness and cutting zone temperature in turning of AISI304 austenitic stainless steel using multi-layer coated (TiCN + TiC + TiCN + TiN) tungsten carbide tools. The proposed approach is based on an adaptive neuro-fuzzy inference system (ANFIS) with particle swarm optimization (PSO) learning. AISI304 stainless steel bars are machined at different cutting speeds and feedrates without cutting fluid while depth of cut is kept constant. ANFIS for prediction of surface roughness and cutting zone temperature has been trained using cutting speed, feedrate, and cutting force data obtained during experiments. ANFIS architecture consisting of 12 fuzzy rules has three inputs and two outputs. Gaussian membership function is used during the training process of the ANFIS. The surface roughness and cutting zone temperature values predicted by the PSO-based ANFIS model are compared with the measured values derived from testing data set. Testing results indicate that the predicted surface roughness and cutting zone temperature are in good agreement with measured roughness and temperature. | ||
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| 650 | 4 | |a Cutting temperature |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Karakuzu, Cihan |4 aut | |
| 700 | 1 | |a Uçar, Mehmet |4 aut | |
| 700 | 1 | |a Cengiz, Abdulkadir |4 aut | |
| 700 | 1 | |a Çavuşlu, Mehmet Ali |4 aut | |
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10.1007/s00170-012-4540-2 doi (DE-627)SPR001763881 (SPR)s00170-012-4540-2-e DE-627 ger DE-627 rakwb eng Aydın, Mehmet verfasserin aut Prediction of surface roughness and cutting zone temperature in dry turning processes of AISI304 stainless steel using ANFIS with PSO learning 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2012 Abstract This paper presents an approach for modeling and prediction of both surface roughness and cutting zone temperature in turning of AISI304 austenitic stainless steel using multi-layer coated (TiCN + TiC + TiCN + TiN) tungsten carbide tools. The proposed approach is based on an adaptive neuro-fuzzy inference system (ANFIS) with particle swarm optimization (PSO) learning. AISI304 stainless steel bars are machined at different cutting speeds and feedrates without cutting fluid while depth of cut is kept constant. ANFIS for prediction of surface roughness and cutting zone temperature has been trained using cutting speed, feedrate, and cutting force data obtained during experiments. ANFIS architecture consisting of 12 fuzzy rules has three inputs and two outputs. Gaussian membership function is used during the training process of the ANFIS. The surface roughness and cutting zone temperature values predicted by the PSO-based ANFIS model are compared with the measured values derived from testing data set. Testing results indicate that the predicted surface roughness and cutting zone temperature are in good agreement with measured roughness and temperature. ANFIS (dpeaa)DE-He213 PSO (dpeaa)DE-He213 Surface roughness (dpeaa)DE-He213 Cutting temperature (dpeaa)DE-He213 Karakuzu, Cihan aut Uçar, Mehmet aut Cengiz, Abdulkadir aut Çavuşlu, Mehmet Ali aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 67(2012), 1-4 vom: 16. Okt., Seite 957-967 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:67 year:2012 number:1-4 day:16 month:10 pages:957-967 https://dx.doi.org/10.1007/s00170-012-4540-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 67 2012 1-4 16 10 957-967 |
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10.1007/s00170-012-4540-2 doi (DE-627)SPR001763881 (SPR)s00170-012-4540-2-e DE-627 ger DE-627 rakwb eng Aydın, Mehmet verfasserin aut Prediction of surface roughness and cutting zone temperature in dry turning processes of AISI304 stainless steel using ANFIS with PSO learning 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2012 Abstract This paper presents an approach for modeling and prediction of both surface roughness and cutting zone temperature in turning of AISI304 austenitic stainless steel using multi-layer coated (TiCN + TiC + TiCN + TiN) tungsten carbide tools. The proposed approach is based on an adaptive neuro-fuzzy inference system (ANFIS) with particle swarm optimization (PSO) learning. AISI304 stainless steel bars are machined at different cutting speeds and feedrates without cutting fluid while depth of cut is kept constant. ANFIS for prediction of surface roughness and cutting zone temperature has been trained using cutting speed, feedrate, and cutting force data obtained during experiments. ANFIS architecture consisting of 12 fuzzy rules has three inputs and two outputs. Gaussian membership function is used during the training process of the ANFIS. The surface roughness and cutting zone temperature values predicted by the PSO-based ANFIS model are compared with the measured values derived from testing data set. Testing results indicate that the predicted surface roughness and cutting zone temperature are in good agreement with measured roughness and temperature. ANFIS (dpeaa)DE-He213 PSO (dpeaa)DE-He213 Surface roughness (dpeaa)DE-He213 Cutting temperature (dpeaa)DE-He213 Karakuzu, Cihan aut Uçar, Mehmet aut Cengiz, Abdulkadir aut Çavuşlu, Mehmet Ali aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 67(2012), 1-4 vom: 16. Okt., Seite 957-967 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:67 year:2012 number:1-4 day:16 month:10 pages:957-967 https://dx.doi.org/10.1007/s00170-012-4540-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 67 2012 1-4 16 10 957-967 |
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10.1007/s00170-012-4540-2 doi (DE-627)SPR001763881 (SPR)s00170-012-4540-2-e DE-627 ger DE-627 rakwb eng Aydın, Mehmet verfasserin aut Prediction of surface roughness and cutting zone temperature in dry turning processes of AISI304 stainless steel using ANFIS with PSO learning 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2012 Abstract This paper presents an approach for modeling and prediction of both surface roughness and cutting zone temperature in turning of AISI304 austenitic stainless steel using multi-layer coated (TiCN + TiC + TiCN + TiN) tungsten carbide tools. The proposed approach is based on an adaptive neuro-fuzzy inference system (ANFIS) with particle swarm optimization (PSO) learning. AISI304 stainless steel bars are machined at different cutting speeds and feedrates without cutting fluid while depth of cut is kept constant. ANFIS for prediction of surface roughness and cutting zone temperature has been trained using cutting speed, feedrate, and cutting force data obtained during experiments. ANFIS architecture consisting of 12 fuzzy rules has three inputs and two outputs. Gaussian membership function is used during the training process of the ANFIS. The surface roughness and cutting zone temperature values predicted by the PSO-based ANFIS model are compared with the measured values derived from testing data set. Testing results indicate that the predicted surface roughness and cutting zone temperature are in good agreement with measured roughness and temperature. ANFIS (dpeaa)DE-He213 PSO (dpeaa)DE-He213 Surface roughness (dpeaa)DE-He213 Cutting temperature (dpeaa)DE-He213 Karakuzu, Cihan aut Uçar, Mehmet aut Cengiz, Abdulkadir aut Çavuşlu, Mehmet Ali aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 67(2012), 1-4 vom: 16. Okt., Seite 957-967 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:67 year:2012 number:1-4 day:16 month:10 pages:957-967 https://dx.doi.org/10.1007/s00170-012-4540-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 67 2012 1-4 16 10 957-967 |
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10.1007/s00170-012-4540-2 doi (DE-627)SPR001763881 (SPR)s00170-012-4540-2-e DE-627 ger DE-627 rakwb eng Aydın, Mehmet verfasserin aut Prediction of surface roughness and cutting zone temperature in dry turning processes of AISI304 stainless steel using ANFIS with PSO learning 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2012 Abstract This paper presents an approach for modeling and prediction of both surface roughness and cutting zone temperature in turning of AISI304 austenitic stainless steel using multi-layer coated (TiCN + TiC + TiCN + TiN) tungsten carbide tools. The proposed approach is based on an adaptive neuro-fuzzy inference system (ANFIS) with particle swarm optimization (PSO) learning. AISI304 stainless steel bars are machined at different cutting speeds and feedrates without cutting fluid while depth of cut is kept constant. ANFIS for prediction of surface roughness and cutting zone temperature has been trained using cutting speed, feedrate, and cutting force data obtained during experiments. ANFIS architecture consisting of 12 fuzzy rules has three inputs and two outputs. Gaussian membership function is used during the training process of the ANFIS. The surface roughness and cutting zone temperature values predicted by the PSO-based ANFIS model are compared with the measured values derived from testing data set. Testing results indicate that the predicted surface roughness and cutting zone temperature are in good agreement with measured roughness and temperature. ANFIS (dpeaa)DE-He213 PSO (dpeaa)DE-He213 Surface roughness (dpeaa)DE-He213 Cutting temperature (dpeaa)DE-He213 Karakuzu, Cihan aut Uçar, Mehmet aut Cengiz, Abdulkadir aut Çavuşlu, Mehmet Ali aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 67(2012), 1-4 vom: 16. Okt., Seite 957-967 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:67 year:2012 number:1-4 day:16 month:10 pages:957-967 https://dx.doi.org/10.1007/s00170-012-4540-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 67 2012 1-4 16 10 957-967 |
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10.1007/s00170-012-4540-2 doi (DE-627)SPR001763881 (SPR)s00170-012-4540-2-e DE-627 ger DE-627 rakwb eng Aydın, Mehmet verfasserin aut Prediction of surface roughness and cutting zone temperature in dry turning processes of AISI304 stainless steel using ANFIS with PSO learning 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2012 Abstract This paper presents an approach for modeling and prediction of both surface roughness and cutting zone temperature in turning of AISI304 austenitic stainless steel using multi-layer coated (TiCN + TiC + TiCN + TiN) tungsten carbide tools. The proposed approach is based on an adaptive neuro-fuzzy inference system (ANFIS) with particle swarm optimization (PSO) learning. AISI304 stainless steel bars are machined at different cutting speeds and feedrates without cutting fluid while depth of cut is kept constant. ANFIS for prediction of surface roughness and cutting zone temperature has been trained using cutting speed, feedrate, and cutting force data obtained during experiments. ANFIS architecture consisting of 12 fuzzy rules has three inputs and two outputs. Gaussian membership function is used during the training process of the ANFIS. The surface roughness and cutting zone temperature values predicted by the PSO-based ANFIS model are compared with the measured values derived from testing data set. Testing results indicate that the predicted surface roughness and cutting zone temperature are in good agreement with measured roughness and temperature. ANFIS (dpeaa)DE-He213 PSO (dpeaa)DE-He213 Surface roughness (dpeaa)DE-He213 Cutting temperature (dpeaa)DE-He213 Karakuzu, Cihan aut Uçar, Mehmet aut Cengiz, Abdulkadir aut Çavuşlu, Mehmet Ali aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 67(2012), 1-4 vom: 16. Okt., Seite 957-967 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:67 year:2012 number:1-4 day:16 month:10 pages:957-967 https://dx.doi.org/10.1007/s00170-012-4540-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 67 2012 1-4 16 10 957-967 |
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Aydın, Mehmet @@aut@@ Karakuzu, Cihan @@aut@@ Uçar, Mehmet @@aut@@ Cengiz, Abdulkadir @@aut@@ Çavuşlu, Mehmet Ali @@aut@@ |
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Aydın, Mehmet |
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Aydın, Mehmet misc ANFIS misc PSO misc Surface roughness misc Cutting temperature Prediction of surface roughness and cutting zone temperature in dry turning processes of AISI304 stainless steel using ANFIS with PSO learning |
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Prediction of surface roughness and cutting zone temperature in dry turning processes of AISI304 stainless steel using ANFIS with PSO learning ANFIS (dpeaa)DE-He213 PSO (dpeaa)DE-He213 Surface roughness (dpeaa)DE-He213 Cutting temperature (dpeaa)DE-He213 |
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Prediction of surface roughness and cutting zone temperature in dry turning processes of AISI304 stainless steel using ANFIS with PSO learning |
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Prediction of surface roughness and cutting zone temperature in dry turning processes of AISI304 stainless steel using ANFIS with PSO learning |
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Aydın, Mehmet Karakuzu, Cihan Uçar, Mehmet Cengiz, Abdulkadir Çavuşlu, Mehmet Ali |
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prediction of surface roughness and cutting zone temperature in dry turning processes of aisi304 stainless steel using anfis with pso learning |
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Prediction of surface roughness and cutting zone temperature in dry turning processes of AISI304 stainless steel using ANFIS with PSO learning |
| abstract |
Abstract This paper presents an approach for modeling and prediction of both surface roughness and cutting zone temperature in turning of AISI304 austenitic stainless steel using multi-layer coated (TiCN + TiC + TiCN + TiN) tungsten carbide tools. The proposed approach is based on an adaptive neuro-fuzzy inference system (ANFIS) with particle swarm optimization (PSO) learning. AISI304 stainless steel bars are machined at different cutting speeds and feedrates without cutting fluid while depth of cut is kept constant. ANFIS for prediction of surface roughness and cutting zone temperature has been trained using cutting speed, feedrate, and cutting force data obtained during experiments. ANFIS architecture consisting of 12 fuzzy rules has three inputs and two outputs. Gaussian membership function is used during the training process of the ANFIS. The surface roughness and cutting zone temperature values predicted by the PSO-based ANFIS model are compared with the measured values derived from testing data set. Testing results indicate that the predicted surface roughness and cutting zone temperature are in good agreement with measured roughness and temperature. © Springer-Verlag London 2012 |
| abstractGer |
Abstract This paper presents an approach for modeling and prediction of both surface roughness and cutting zone temperature in turning of AISI304 austenitic stainless steel using multi-layer coated (TiCN + TiC + TiCN + TiN) tungsten carbide tools. The proposed approach is based on an adaptive neuro-fuzzy inference system (ANFIS) with particle swarm optimization (PSO) learning. AISI304 stainless steel bars are machined at different cutting speeds and feedrates without cutting fluid while depth of cut is kept constant. ANFIS for prediction of surface roughness and cutting zone temperature has been trained using cutting speed, feedrate, and cutting force data obtained during experiments. ANFIS architecture consisting of 12 fuzzy rules has three inputs and two outputs. Gaussian membership function is used during the training process of the ANFIS. The surface roughness and cutting zone temperature values predicted by the PSO-based ANFIS model are compared with the measured values derived from testing data set. Testing results indicate that the predicted surface roughness and cutting zone temperature are in good agreement with measured roughness and temperature. © Springer-Verlag London 2012 |
| abstract_unstemmed |
Abstract This paper presents an approach for modeling and prediction of both surface roughness and cutting zone temperature in turning of AISI304 austenitic stainless steel using multi-layer coated (TiCN + TiC + TiCN + TiN) tungsten carbide tools. The proposed approach is based on an adaptive neuro-fuzzy inference system (ANFIS) with particle swarm optimization (PSO) learning. AISI304 stainless steel bars are machined at different cutting speeds and feedrates without cutting fluid while depth of cut is kept constant. ANFIS for prediction of surface roughness and cutting zone temperature has been trained using cutting speed, feedrate, and cutting force data obtained during experiments. ANFIS architecture consisting of 12 fuzzy rules has three inputs and two outputs. Gaussian membership function is used during the training process of the ANFIS. The surface roughness and cutting zone temperature values predicted by the PSO-based ANFIS model are compared with the measured values derived from testing data set. Testing results indicate that the predicted surface roughness and cutting zone temperature are in good agreement with measured roughness and temperature. © Springer-Verlag London 2012 |
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Prediction of surface roughness and cutting zone temperature in dry turning processes of AISI304 stainless steel using ANFIS with PSO learning |
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https://dx.doi.org/10.1007/s00170-012-4540-2 |
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Karakuzu, Cihan Uçar, Mehmet Cengiz, Abdulkadir Çavuşlu, Mehmet Ali |
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Karakuzu, Cihan Uçar, Mehmet Cengiz, Abdulkadir Çavuşlu, Mehmet Ali |
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10.1007/s00170-012-4540-2 |
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2024-07-04T00:17:53.642Z |
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|
| score |
7.399865 |