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Artificial Intelligence Based Surface Roughness Prediction Modeling for Three Dimensional End Milling
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- 발행기관
- 보안공학연구지원센터(IJAST) 바로가기
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- 간행물
- International Journal of Advanced Science and Technology 바로가기
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- 통권
- Vol.45 (2012.08)바로가기
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- 페이지
- pp.1-18
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A206785
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- Surface roughness is an index which determines the quality of machined products and is influenced by the cutting parameters. In this study the average surface roughness Ra (value) for Aluminum after ball end milling operation has been measured. 84 experiments have been conducted varying cutter axis inclination angle (φ degree), spindle speed (S rpm), feed rate (fy mm/min), radial depth of cut (feed fx mm), axial depth of cut (t mm) in order to find Ra. This data has been divided into two sets on a random basis; 68 training data set and 16 testing data set. The training data set has been used to train different ANN and ANFIS models for Ra prediction. And testing data set has been used to validate the models. Better ANFIS model has been selected based on the minimum value of Root Mean Square Error (RMSE) which is constructed with three Gaussian membership functions (gaussmf) for each input variables and linear membership function for output. Similarly better ANN model has been selected based on the minimum value of Root Mean Square Error (RMSE) and Mean Absolute Percentage of Error (MAPE). The Selected ANFIS model has been compared with theoretical equation output, ANN and Response Surface Methodology (RSM). This comparison is done based on RMSE and MAPE. The comparison shows that selected ANFIS model gives better result for training and testing data. So, this ANFIS model can be used further for predicting surface roughness of Aluminum for three dimensional end milling operation.
목차
Abstract
1. Introduction
2. Literature Review
3. Methodology
3.1. Experimental Setup and Design of Experiment
3.2. Surface Roughness
3.3. ANFIS
3.4. RSM
3.5. ANN
3.6. Theoretical Equations
3.7. Pearson Correlation Coefficient
4. Results and Discussion
4.1. ANFIS Output
4.2. ANN Output
4.3. RSM Output and Output from Theoretical Equations
5. Conclusion
Acknowledgements
References
키워드
Ball end mill ANN ANFIS RSM Roughness prediction저자
간행물 정보
발행기관
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- 발행기관명
- 보안공학연구지원센터(IJAST) [Science & Engineering Research Support Center, Republic of Korea(IJAST)]
- 설립연도
- 2006
- 분야
- 공학>컴퓨터학
- 소개
- 1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업
간행물
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- 간행물명
- International Journal of Advanced Science and Technology
- 간기
- 월간
- pISSN
- 2005-4238
- 수록기간
- 2008~2016
- 십진분류
- KDC 505 DDC 605
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