Computational inteligence in optimization of machining operation parameters of st-37 steel
Optimal selection of cutting parameters is one of the significant issues in achieving high quality machining. In this study, a method for the selection of optimal cutting parameters during lathe operation is presented. The present study focuses on multiple-performance optimization on machining chara...
| Main Authors: | , , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Published: |
2013
|
| Subjects: |
| _version_ | 1796859545630277632 |
|---|---|
| author | Golshan, Abolfazl Ghodsiyeh, Danial Gohari, Soheil Ayob, Amran Baharudin, B. T. Hang Tuah |
| author_facet | Golshan, Abolfazl Ghodsiyeh, Danial Gohari, Soheil Ayob, Amran Baharudin, B. T. Hang Tuah |
| author_sort | Golshan, Abolfazl |
| collection | ePrints |
| description | Optimal selection of cutting parameters is one of the significant issues in achieving high quality machining. In this study, a method for the selection of optimal cutting parameters during lathe operation is presented. The present study focuses on multiple-performance optimization on machining characteristics of St-37 steel. The cutting parameters used in this experimental study include cutting speed, feed rate, depth of cut and rake angle. Two output parameters, namely, surface roughness and tool life are considered as process performance. A statistical model based on linear polynomial equations is developed to describe different responses. For optimal conditions, the Non-dominated Sorting Genetic Algorithm (NSGA) is employed in achieving appropriate models. The optimization procedure shows that the proposed method has a high performance in problem-solving. |
| first_indexed | 2024-03-05T19:28:45Z |
| format | Conference or Workshop Item |
| id | utm.eprints-50955 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T19:28:45Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | utm.eprints-509552017-09-26T01:30:17Z http://eprints.utm.my/50955/ Computational inteligence in optimization of machining operation parameters of st-37 steel Golshan, Abolfazl Ghodsiyeh, Danial Gohari, Soheil Ayob, Amran Baharudin, B. T. Hang Tuah TJ Mechanical engineering and machinery Optimal selection of cutting parameters is one of the significant issues in achieving high quality machining. In this study, a method for the selection of optimal cutting parameters during lathe operation is presented. The present study focuses on multiple-performance optimization on machining characteristics of St-37 steel. The cutting parameters used in this experimental study include cutting speed, feed rate, depth of cut and rake angle. Two output parameters, namely, surface roughness and tool life are considered as process performance. A statistical model based on linear polynomial equations is developed to describe different responses. For optimal conditions, the Non-dominated Sorting Genetic Algorithm (NSGA) is employed in achieving appropriate models. The optimization procedure shows that the proposed method has a high performance in problem-solving. 2013 Conference or Workshop Item PeerReviewed Golshan, Abolfazl and Ghodsiyeh, Danial and Gohari, Soheil and Ayob, Amran and Baharudin, B. T. Hang Tuah (2013) Computational inteligence in optimization of machining operation parameters of st-37 steel. In: 2012 International Conference on Mechanical Materials and Manufacturing Engineering, ICMMME 2012, 5 October 2012 through 6 October 2012, Dalian; China. https://www.researchgate.net/publication/256294910_Computational_Inteligence_in_Optimization_of_Machining_Operation_Parameters_of_ST-37_Steel |
| spellingShingle | TJ Mechanical engineering and machinery Golshan, Abolfazl Ghodsiyeh, Danial Gohari, Soheil Ayob, Amran Baharudin, B. T. Hang Tuah Computational inteligence in optimization of machining operation parameters of st-37 steel |
| title | Computational inteligence in optimization of machining operation parameters of st-37 steel |
| title_full | Computational inteligence in optimization of machining operation parameters of st-37 steel |
| title_fullStr | Computational inteligence in optimization of machining operation parameters of st-37 steel |
| title_full_unstemmed | Computational inteligence in optimization of machining operation parameters of st-37 steel |
| title_short | Computational inteligence in optimization of machining operation parameters of st-37 steel |
| title_sort | computational inteligence in optimization of machining operation parameters of st 37 steel |
| topic | TJ Mechanical engineering and machinery |
| work_keys_str_mv | AT golshanabolfazl computationalinteligenceinoptimizationofmachiningoperationparametersofst37steel AT ghodsiyehdanial computationalinteligenceinoptimizationofmachiningoperationparametersofst37steel AT goharisoheil computationalinteligenceinoptimizationofmachiningoperationparametersofst37steel AT ayobamran computationalinteligenceinoptimizationofmachiningoperationparametersofst37steel AT baharudinbthangtuah computationalinteligenceinoptimizationofmachiningoperationparametersofst37steel |