COMPARATIVE STUDY OF ANN AND ANFIS MODELS FOR PREDICTING TEMPERATURE IN MACHINING
The Mechanism of heat generation at the cutting region (tool-workpiece interface) during machining processes is a highly complicated phenomenon and depends on many process parameters. Elevated temperature during the machining process is a root cause of residual stress on the machined part as well a...
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| Format: | Article |
| Language: | English |
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Taylor's University
2018-01-01
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| Series: | Journal of Engineering Science and Technology |
| Subjects: | |
| Online Access: | http://jestec.taylors.edu.my/Vol%2013%20issue%201%20January%202018/13_1_15.pdf |
| _version_ | 1818755577532121088 |
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| author | SOROUSH MASOUDI MOHAMMAD SIMA MAJID TOLOUEI-RAD |
| author_facet | SOROUSH MASOUDI MOHAMMAD SIMA MAJID TOLOUEI-RAD |
| author_sort | SOROUSH MASOUDI |
| collection | DOAJ |
| description | The Mechanism of heat generation at the cutting region (tool-workpiece interface) during machining processes is a highly complicated phenomenon and depends on many process parameters. Elevated temperature during the machining process is a root cause of residual stress on the machined part as well
as a cause of rapid tool wear. Although several methods have been developed to measure the temperature in machining, the in-situ application of these methods has many technical problems and restrictions. As a result, the utilization of computational methods to predict temperature in machining is very demanding.
In this paper, the artificial intelligent models known as Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) were used to model and predict the temperature in machining. Several experiments were conducted to validate these models. These experiments were carried out on thin-walled AL7075 work pieces to investigate the effect of different machining parameters on temperature in turning. A thermal imaging Infrared
(IR) camera was used to measure the temperature of the cutting area during machining. With respect to experimental data, the ANN and ANFIS models were developed and the results obtained from those models were then compared to the experimental results to evaluate the performance of the models. According to the results, the ANFIS model is superior to the ANN model in
terms the accurate and reliable prediction of temperature in machining. |
| first_indexed | 2024-12-18T05:41:22Z |
| format | Article |
| id | doaj.art-9c88eb501be148f8a7112e7f1b873a0c |
| institution | Directory Open Access Journal |
| issn | 1823-4690 |
| language | English |
| last_indexed | 2024-12-18T05:41:22Z |
| publishDate | 2018-01-01 |
| publisher | Taylor's University |
| record_format | Article |
| series | Journal of Engineering Science and Technology |
| spelling | doaj.art-9c88eb501be148f8a7112e7f1b873a0c2022-12-21T21:19:11ZengTaylor's UniversityJournal of Engineering Science and Technology1823-46902018-01-01131211225COMPARATIVE STUDY OF ANN AND ANFIS MODELS FOR PREDICTING TEMPERATURE IN MACHININGSOROUSH MASOUDI0MOHAMMAD SIMA1 MAJID TOLOUEI-RAD2Young Researchers and Elite Club, Najafabad Branch, Islamic Azad University, Najafabad, IranDepartment of industrial engineering, Lamar University, Beaumont, TX, USASchool of Engineering, Edith Cowan University, Perth, Western AustraliaThe Mechanism of heat generation at the cutting region (tool-workpiece interface) during machining processes is a highly complicated phenomenon and depends on many process parameters. Elevated temperature during the machining process is a root cause of residual stress on the machined part as well as a cause of rapid tool wear. Although several methods have been developed to measure the temperature in machining, the in-situ application of these methods has many technical problems and restrictions. As a result, the utilization of computational methods to predict temperature in machining is very demanding. In this paper, the artificial intelligent models known as Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) were used to model and predict the temperature in machining. Several experiments were conducted to validate these models. These experiments were carried out on thin-walled AL7075 work pieces to investigate the effect of different machining parameters on temperature in turning. A thermal imaging Infrared (IR) camera was used to measure the temperature of the cutting area during machining. With respect to experimental data, the ANN and ANFIS models were developed and the results obtained from those models were then compared to the experimental results to evaluate the performance of the models. According to the results, the ANFIS model is superior to the ANN model in terms the accurate and reliable prediction of temperature in machining.http://jestec.taylors.edu.my/Vol%2013%20issue%201%20January%202018/13_1_15.pdfTurningTemperatureInfrared measurementsANNANFIS |
| spellingShingle | SOROUSH MASOUDI MOHAMMAD SIMA MAJID TOLOUEI-RAD COMPARATIVE STUDY OF ANN AND ANFIS MODELS FOR PREDICTING TEMPERATURE IN MACHINING Journal of Engineering Science and Technology Turning Temperature Infrared measurements ANN ANFIS |
| title | COMPARATIVE STUDY OF ANN AND ANFIS MODELS FOR PREDICTING TEMPERATURE IN MACHINING |
| title_full | COMPARATIVE STUDY OF ANN AND ANFIS MODELS FOR PREDICTING TEMPERATURE IN MACHINING |
| title_fullStr | COMPARATIVE STUDY OF ANN AND ANFIS MODELS FOR PREDICTING TEMPERATURE IN MACHINING |
| title_full_unstemmed | COMPARATIVE STUDY OF ANN AND ANFIS MODELS FOR PREDICTING TEMPERATURE IN MACHINING |
| title_short | COMPARATIVE STUDY OF ANN AND ANFIS MODELS FOR PREDICTING TEMPERATURE IN MACHINING |
| title_sort | comparative study of ann and anfis models for predicting temperature in machining |
| topic | Turning Temperature Infrared measurements ANN ANFIS |
| url | http://jestec.taylors.edu.my/Vol%2013%20issue%201%20January%202018/13_1_15.pdf |
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