Cutting parameters identification using multi adaptive network based fuzzy inference system: an artificial intelligence approach
The influences of the machine parameters on machined parts are not always precisely known and hence, it becomes difficult to recommend the optimum machinability data for machine process. This paper proposes a method for cutting parameters identification using Multi adaptive Network based Fuzzy Infer...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Academic Journals
2011
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| Online Access: | http://psasir.upm.edu.my/id/eprint/22867/1/22867.pdf |
| _version_ | 1825925240896618496 |
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| author | Suhail, Adeel H. Ismail, Napsiah Wong, Shaw Voon Abdul Jalil, Nawal Aswan |
| author_facet | Suhail, Adeel H. Ismail, Napsiah Wong, Shaw Voon Abdul Jalil, Nawal Aswan |
| author_sort | Suhail, Adeel H. |
| collection | UPM |
| description | The influences of the machine parameters on machined parts are not always precisely known and hence, it becomes difficult to recommend the optimum machinability data for machine process. This paper proposes a method for cutting parameters identification using Multi adaptive Network based Fuzzy Inference System (MANFIS). Three Adaptive Network based Fuzzy Inference System (ANFIS) models were used in the first step to identify the initial values for the cutting parameters (cutting speed, feed rate, and depth of cut) using surface roughness as a single input, in the next step these parameters were modified and verified using another set of ANFIS models. Then, workpiece surface temperature is used as input for another set of ANFIS models to amend the final values of the cutting parameters. In this way, multi-input-multi-output ANFIS structure presented, which can identify the cutting parameters accurately once the desired surface roughness and in-process measured surface temperature were entered to the system. The test results showed that the proposed MANFIS model can be used successfully for machinability data selection. |
| first_indexed | 2024-03-06T07:55:13Z |
| format | Article |
| id | upm.eprints-22867 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T07:55:13Z |
| publishDate | 2011 |
| publisher | Academic Journals |
| record_format | dspace |
| spelling | upm.eprints-228672020-04-15T16:21:47Z http://psasir.upm.edu.my/id/eprint/22867/ Cutting parameters identification using multi adaptive network based fuzzy inference system: an artificial intelligence approach Suhail, Adeel H. Ismail, Napsiah Wong, Shaw Voon Abdul Jalil, Nawal Aswan The influences of the machine parameters on machined parts are not always precisely known and hence, it becomes difficult to recommend the optimum machinability data for machine process. This paper proposes a method for cutting parameters identification using Multi adaptive Network based Fuzzy Inference System (MANFIS). Three Adaptive Network based Fuzzy Inference System (ANFIS) models were used in the first step to identify the initial values for the cutting parameters (cutting speed, feed rate, and depth of cut) using surface roughness as a single input, in the next step these parameters were modified and verified using another set of ANFIS models. Then, workpiece surface temperature is used as input for another set of ANFIS models to amend the final values of the cutting parameters. In this way, multi-input-multi-output ANFIS structure presented, which can identify the cutting parameters accurately once the desired surface roughness and in-process measured surface temperature were entered to the system. The test results showed that the proposed MANFIS model can be used successfully for machinability data selection. Academic Journals 2011 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/22867/1/22867.pdf Suhail, Adeel H. and Ismail, Napsiah and Wong, Shaw Voon and Abdul Jalil, Nawal Aswan (2011) Cutting parameters identification using multi adaptive network based fuzzy inference system: an artificial intelligence approach. Scientific Research and Essays, 6 (1). art. no. 6E1102E17546. pp. 187-195. ISSN 1992-2248 https://academicjournals.org/journal/SRE/article-abstract/6E1102E17546 10.5897/SRE10.477 |
| spellingShingle | Suhail, Adeel H. Ismail, Napsiah Wong, Shaw Voon Abdul Jalil, Nawal Aswan Cutting parameters identification using multi adaptive network based fuzzy inference system: an artificial intelligence approach |
| title | Cutting parameters identification using multi adaptive network based fuzzy inference system: an artificial intelligence approach |
| title_full | Cutting parameters identification using multi adaptive network based fuzzy inference system: an artificial intelligence approach |
| title_fullStr | Cutting parameters identification using multi adaptive network based fuzzy inference system: an artificial intelligence approach |
| title_full_unstemmed | Cutting parameters identification using multi adaptive network based fuzzy inference system: an artificial intelligence approach |
| title_short | Cutting parameters identification using multi adaptive network based fuzzy inference system: an artificial intelligence approach |
| title_sort | cutting parameters identification using multi adaptive network based fuzzy inference system an artificial intelligence approach |
| url | http://psasir.upm.edu.my/id/eprint/22867/1/22867.pdf |
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