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Development of Roughness Estimation Model for Plunge Grinding of Valve Parts Using Neural Network

뉴럴 네트워크를 이용한 밸브 부품 생산용 플런지 연삭의 거칠기 예측모델 개발

  • Choi, Jeong-Ju (Technical Center for High-Performance Valves, Dong-a University) ;
  • Park, Joon-Hong (Center for Advanced Net Shape Manufacturing and Clean Processes, Dong-a University)
  • 최정주 (동아대학교 고기능성밸브기술지원센터) ;
  • 박준홍 (동아대학교 신소형재가공청정공정개발연구센터)
  • Received : 2010.11.15
  • Accepted : 2011.01.13
  • Published : 2011.01.31

Abstract

Grinding process is executed in the final machining stage to meet the quality requirements. In generally the ground surface of workpiece is affected by dressing condition as well as grinding condition. In order to estimate the roughness of workpiece, the several roughness models have been researched. These models defined the specific parameters and considered the several parameters which affect to roughness as multiply relationship among them. However, the multiply relationship among parameters is not enough to show the complicated grinding mechanism. Therefore, the neural network algorithm is used in this paper to predict the ground roughness for the plunge grinding. The proposed structure is composed of the initial roughness as well as final roughness model. The input parameters of proposed neural network are referred with the existing roughness model's. The performance of the proposed model is verified through experiments.

본 연삭공정은 제품의 품질 만족도를 충족시키기 위해 기계가공의 마지막단계에서 이루어지는 공정이다. 일반적으로 연삭된 공작물의 가공 표면은 연삭 조건 및 드레싱 조건에 의해 그 정도가 결정된다. 이러게 연삭된 공작물의 표면 거칠기를 예측하기 위해서 몇 가지 거칠기 모델들이 연구되었다. 기존의 모델들은 몇 가지 주요 파라미터를 선정하고 이들 파라미터를 곱의 형태로 모델을 만들어 그 파라미터의 영향도를 고려하였다. 그러나 이러한 파라미터간 곱의 형태는 복잡한 연삭 메커니즘을 나타내기에는 충분하지가 않다. 따라서 본 연구에서는 뉴럴네트워크 알고리즘을 이용하여 플런지 연삭의 연삭 모델을 제안하였다. 제안된 구조는 초기 거칠기 모델과 최종 거칠기 모델로 구성되어 있다. 제안된 뉴럴네트워크의 입력 파라미터는 기존의 거칠기 모델들의 것을 이용하였다. 제안된 모델의 거칠기 예측 성능은 실험을 통해 검증하였다.

Keywords

References

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