[논문]Prediction and control of surface roughness in CNC lathe using artificial neural network

Karayel, Durmus

doi:10.1016/j.jmatprotec.2008.07.023

[해외논문] Prediction and control of surface roughness in CNC lathe using artificial neural network

Journal of materials processing technology, v.209 no.7, 2009년, pp.3125 - 3137

Karayel, Durmus (Tel.: +90 2642774001)

Abstract ▼ AI-Helper

AbstractIn this study, a neural network approach is presented for the prediction and control of surface roughness in a computer numerically controlled (CNC) lathe. Experiments have been performed on the CNC lathe to obtain the data used for the training and testing of a neural network. The parameters used in the experiment were reduced to three cutting parameters which consisted of depth of cutting, cutting speed, and feed rate. Each of the other parameters such as tool nose radius, tool overhang, approach angle, workpiece length, workpiece diameter and workpiece material was taken as constant. A feed forward multi-layered neural network was developed and the network model was trained using the scaled conjugate gradient algorithm (SCGA), which is a type of back-propagation. The adaptive learning rate was used. Therefore, the learning rate was not selected before training and it was adjusted during training to minimize training time. The number of iterations was 8000 and no smoothing factor was used. Ra, Rz and Rmax were modeled and were evaluated individually. One hidden layer was used for all models while the numbers of neurons in the hidden layer of the Ra model were five and the numbers of neurons in the hidden layers of the Rz and Rmax models were ten. The results of the neural network approach were compared with actual values. In addition, inasmuch as the control of surface roughness is proposed, a control algorithm was developed in the present investigation. The desired surface roughness was entered into the control system as a reference value and the controller determined the cutting parameters for these surface roughness values. A new surface roughness value was determined by sending the cutting parameters to the observer (ANN block). The obtained surface roughness was fed back to the comparison unit and was compared with the reference value and the difference surface roughness was then sent to the controller. The iteration was continued until the difference was reduced to a certain value of surface roughness which could be permitted for machining accuracy. When the surface roughness reached the permitted value, these cutting parameters were sent to the CNC turning system as input values. In conclusion, both the surface roughness values corresponding to the cutting parameters and suitable cutting parameters for a certain surface roughness can be determined prior to a machining operation using the ANN and control algorithm.

Keyword

참고문헌 (30)

Journal of Materials Processing Technology Abouelatta 118 1-3 269 2001 10.1016/S0924-0136(01)00959-1 Surface roughness prediction based on cutting parameters and tool vibrations in turning operations

상세보기
Journal of Materials Processing Technology Arbizu 143-144 390 2003 10.1016/S0924-0136(03)00407-2 Surface roughness prediction by factorial design of experiments in turning processes

상세보기
Robotics and Computer-Integrated Manufacturing Benardos 18 343 2002 10.1016/S0736-5845(02)00005-4 Prediction of surface roughness in CNC face milling using neural network and Taguchi's design of experiments, robotics and computer integrated manufacturing

상세보기
Computer Integrated Manufacturing Systems Brian 10 3 231 1997 10.1016/S0951-5240(97)00013-X A generic intelligent control system for grinding

상세보기
Journal of Material Processing Technology Choudhury 67 55 1997 10.1016/S0924-0136(96)02818-X Surface roughness in the turning of high-strength steel by factorial design of experiments

상세보기
Journal of Materials Processing Technology Davim 116 2-3 305 2001 10.1016/S0924-0136(01)01063-9 A note on the determination of optimal cutting conditions for surface finish obtained in turning using design of experiments

상세보기
Engineering Application of Artificial Intelligence Dimla 12 471 1999 10.1016/S0952-1976(99)00015-9 Application of perceptron neural network to tool-state classification in a metal-turning operation

상세보기
International Journal of Production Research Fang 35 179 1997 10.1080/002075497196046 A new algorithm for developing a reference model for predicting surface roughness in finish machining of steels

상세보기
Journal of Materials Processing Technology Grzesik 134 265 2003 10.1016/S0924-0136(02)01105-6 Hybrid approach to surface roughness evaluation in multistage machining process

상세보기
International Journal of Machine Tools and Manufacture Ho 42 13 1441 2002 10.1016/S0890-6955(02)00078-0 Accurate modeling and prediction of surface roughness by computer vision in turning operations using an adaptive neuro-fuzzy inference system

상세보기
Chinese Journal of Mechanical Engineering Hongxiang 15 153 2002 10.3901/CJME.2002.02.153 Surface roughness prediction model for ultra-precision turning aluminum alloy with a single crystal diamond tool
Robotic and Computer Integrated Manufacturing Hwang 13 181 1997 10.1016/S0736-5845(97)00001-X Non-circular cutting with a lathe using three-stage intelligent controller

상세보기
International Journal of Machine Tools and Manufacture Lee 41 9 1251 2001 10.1016/S0890-6955(01)00023-2 Surface roughness inspection by computer vision in turning operations

상세보기
Mechatronics Lee 14 129 2004 10.1016/S0957-4158(02)00096-X The model of surface roughness inspection by vision system in turning

상세보기
Journal of Materials Processing Technology Liao 40 2293 2000 Design of fuzzy controller for the adaptive control of WEDM process
Journal of Materials Processing Technology Lin 108 3 286 2001 10.1016/S0924-0136(00)00835-9 Modeling the surface roughness and cutting force for turning

상세보기
International Journal of Machine Tools & Manufacturing Mathews 39 723 1999 10.1016/S0890-6955(98)00061-3 Neural-network approach for predicting hole quality in reaming

상세보기
Construction and Building Materials Pala 22 3 330 2008 10.1016/j.conbuildmat.2006.08.015 Dynamic soil structure interaction analysis of neural network

상세보기
ISA Transactions Pitstra 39 191 2000 10.1016/S0019-0578(00)00008-2 Controller design for constant cutting force turning machine control

상세보기
Journal of Materials Processing Technology Pramanik 140 1-3 308 2003 10.1016/S0924-0136(03)00751-9 Cutting performance of diamond tools during ultra-precision turning of electroless-nickel plated die materials

상세보기
Journal of Materials Processing Technology Risbood 132 203 2003 10.1016/S0924-0136(02)00920-2 Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process

상세보기
Mechatronics Rodolfo 13 825 2003 10.1016/S0957-4158(03)00005-9 Intelligent process super vision for predicting tool wear in machining processes

상세보기
Journal of Engineering for Industry Shiraishi 112 78 1990 10.1115/1.2899298 Dimensional and surface roughness control in a turning operation

상세보기
Journal of Materials Processing Technology Suneel 121 77 2002 10.1016/S0924-0136(01)01213-4 A technical note on integrated product quality model using artificial neural networks

상세보기
Journal of Materials Processing Technology Tsai 39 583 1999 An in-process surface recognition system based on neural networks in end milling cutting operations
Whitehouse 1994 Handbook of Surface Metrology
Journal of Materials Processing Technology Wong 138 538 2003 10.1016/S0924-0136(03)00143-2 Machinability data representation with artificial neural network

상세보기
Chemical Engineering Science Yiming 58 3115 2003 10.1016/S0009-2509(03)00166-0 Estimation and control of surface roughness in thin film growth using kinetic Monte-Carlo models

상세보기
Journal of Engineering for Industry Zhang 113 145 1991 10.1115/1.2899671 Dynamic generation of machined surface. Part 2: Construction of surface topography

상세보기
Journal of Dynamic System, Measurement, and Control Zhang 113 273 1991 10.1115/1.2896375 Simulation of surface topography formed during the intermittent turning processes

상세보기

활용도 분석정보

상세보기

다운로드

내보내기

활용도 Top5 논문

해당 논문의 주제분야에서 활용도가 높은 상위 5개 콘텐츠를 보여줍니다.
더보기 버튼을 클릭하시면 더 많은 관련자료를 살펴볼 수 있습니다.

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

연합인증

[해외논문] Prediction and control of surface roughness in CNC lathe using artificial neural network

Abstract ▼ AI-Helper

Keyword

참고문헌 (30)

이 논문을 인용한 문헌

활용도 분석정보

활용도 Top5 논문

관련 콘텐츠

원문 URL 링크

연관된 기능

이 논문과 함께 이용한 콘텐츠

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

선택된 텍스트

연합인증

[해외논문] Prediction and control of surface roughness in CNC lathe using artificial neural network

Abstract ▼ AI-Helper

Keyword

참고문헌 (30)

이 논문을 인용한 문헌

활용도 분석정보

활용도 Top5 논문 더보기

관련 콘텐츠

원문 URL 링크

연관된 기능

이 논문과 함께 이용한 콘텐츠

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

선택된 텍스트

활용도 Top5 논문