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      KCI등재 SCIE SCOPUS

      An improved surface roughness prediction model using Box-Cox transformation with RSM in end milling of EN 353

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      https://www.riss.kr/link?id=A103788660

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      다국어 초록 (Multilingual Abstract)

      In the present work, an attempt has been made to use Box-Cox transformation with response surface methodology to develop improvesurface roughness prediction model in end milling of EN 353 steel using carbide inserts. The analysis has been carried out ...

      In the present work, an attempt has been made to use Box-Cox transformation with response surface methodology to develop improvesurface roughness prediction model in end milling of EN 353 steel using carbide inserts. The analysis has been carried out in two stages.

      In the first stage quadratic model has been developed in terms of feed, speed, depth of cut and nose radius using response surface methodology(RSM) based on center composite rotatable design (CCRD). The quadratic model, thus developed predicts the surface roughnesswith 92% accuracy. In the second stage, the improved quadratic model has been developed using Box-Cox transformation with RSMbased on CCRD. The prediction ability of this develop model has been found more accurate (mean absolute error 4.7%) than previousone. An attempt has also been made to investigate the influence of cutting parameters on surface roughness. The result shows that themachining speed is the main influencing factor on the surface roughness while the depth of cut has no significant influence.

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      참고문헌 (Reference)

      1 I. A. Choudhary, "Surface roughness prediction in turning of high-strength steel by factorial design of experiments" 67 : 51-61, 1997

      2 A. Mansour, "Surface roughness model for end milling: a semi-free cutting carbon case hardening steel (EN32) in dry condition" 124 : 183-191, 2002

      3 C. Chang, "Study on the prediction model of surface roughness for side milling operations" 29 : 867-878, 2006

      4 K. Bouacha, "Statistical analysis of surface roughness and cutting forces using response surface methodology in hard turning of AISI 52100 bearing steel with CBN tool" 28 : 349-361, 2010

      5 S. Reddy, "Selection of an optimal parametric combination for achieving a better surface finish in dry milling using genetic algorithms" 28 : 463-473, 2006

      6 H. Oktem, "Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm" 27 : 735-744, 2006

      7 M. R. Razfar, "Optimum surface roughness prediction in face milling by using neural network and harmony search algorithm" 52 : 487-495, 2010

      8 C. Y. Nian, "Optimization of turning operations with multiple performance characteristics" 95 : 90-96, 1999

      9 M. Alauddin, "Optimization of surface finish in end milling Inconel 718" 56 : 54-65, 1996

      10 Ramezan Ali Mahdavinejad, "Optimization of milling parameters using artificial neural network and artificial immune system" 대한기계학회 26 (26): 4097-4104, 2012

      1 I. A. Choudhary, "Surface roughness prediction in turning of high-strength steel by factorial design of experiments" 67 : 51-61, 1997

      2 A. Mansour, "Surface roughness model for end milling: a semi-free cutting carbon case hardening steel (EN32) in dry condition" 124 : 183-191, 2002

      3 C. Chang, "Study on the prediction model of surface roughness for side milling operations" 29 : 867-878, 2006

      4 K. Bouacha, "Statistical analysis of surface roughness and cutting forces using response surface methodology in hard turning of AISI 52100 bearing steel with CBN tool" 28 : 349-361, 2010

      5 S. Reddy, "Selection of an optimal parametric combination for achieving a better surface finish in dry milling using genetic algorithms" 28 : 463-473, 2006

      6 H. Oktem, "Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm" 27 : 735-744, 2006

      7 M. R. Razfar, "Optimum surface roughness prediction in face milling by using neural network and harmony search algorithm" 52 : 487-495, 2010

      8 C. Y. Nian, "Optimization of turning operations with multiple performance characteristics" 95 : 90-96, 1999

      9 M. Alauddin, "Optimization of surface finish in end milling Inconel 718" 56 : 54-65, 1996

      10 Ramezan Ali Mahdavinejad, "Optimization of milling parameters using artificial neural network and artificial immune system" 대한기계학회 26 (26): 4097-4104, 2012

      11 B. M. Gopalsamy, "Optimisation of machining parameters for hard machining: grey relational theory approach and ANOVA" 45 : 1068-1086, 2009

      12 A. Iqbal, "Modeling the effects of cutting parameters in MQL employed finish hard-milling process using D-optimal method" 199 : 370-390, 2007

      13 V. Upadhyay, "In-process prediction of surface roughness in turning of Ti-6Al-4V alloy using cutting parameters and vibration signals" 46 (46): 154-160, 2012

      14 J. W. Osborne, "Improving your data transformations: Applying the Box-Cox transformation" 15 : 1-9, 2010

      15 C. C. Tsao, "Grey-Taguchi method to optimize the milling parameters of aluminum alloy" 40 : 41-48, 2009

      16 M. Y. Wang, "Experimental study of surface roughness in slot end milling AL2014-T6" 44 : 51-57, 2004

      17 T. L. Ginta, "Development of surface roughness models in end milling titanium alloy Ti-6Al-4V using uncoated tungsten carbide inserts" 28 (28): 542-551, 2009

      18 W. H. Yang, "Design optimization of cutting parameters for turning operations based on the Taguchi method" 84 : 122-129, 1998

      19 M. Alauddin, "Computer-aided analysis of a surface-roughness model for end milling" 55 (55): 123-127, 1995

      20 B. Fnides, "Application of response surface methodology for determining cutting force model in turning hardened AISI H11 hot work tool steel" 36 (36): 109-123, 2011

      21 J. A. Ghani, "Application of Taguchi method in the optimization of end milling parameters" 145 : 84-92, 2003

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2023 평가예정 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
      2020-01-01 평가 등재학술지 유지 (해외등재 학술지 평가) KCI등재
      2012-11-05 학술지명변경 한글명 : 대한기계학회 영문 논문집 -> Journal of Mechanical Science and Technology KCI등재
      2010-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2008-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2006-01-19 학술지명변경 한글명 : KSME International Journal -> 대한기계학회 영문 논문집
      외국어명 : KSME International Journal -> Journal of Mechanical Science and Technology
      KCI등재
      2006-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2004-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2001-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      1998-07-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 1.04 0.51 0.84
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.74 0.66 0.369 0.12
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