A comparative study of the ANN and RSM modeling approaches for predicting burr size in drilling

International Journal of Advanced Manufacturing Technology

Volumn 38, Issue 9-10, 2008, Pages 868-883

  Karnik, S R ^a   Gaitonde, V N ^a   Davim, J P ^b  

^a KLE TECHNOLOGICAL UNIVERSITY   (India)

^b UNIVERSITY OF AVEIRO   (Portugal)

Author keywords

Artificial neural networks; Burr size; Drilling; Response surface methodology

Indexed keywords

ARTIFICIAL INTELLIGENCE; BACKPROPAGATION; BACKPROPAGATION ALGORITHMS; COMPUTER NETWORKS; DESIGN OF EXPERIMENTS; FORECASTING; LEARNING ALGORITHMS; LEARNING SYSTEMS; PREDICTIVE CONTROL SYSTEMS; PROCESS ENGINEERING; STAINLESS STEEL; VEGETATION;

ANN MODELING; ARTIFICIAL NEURAL NETWORKS; BURR SIZE; DRILLING; RESPONSE SURFACE METHODOLOGY;

MATHEMATICAL MODELS;

EID: 50849135518     PISSN: 02683768     EISSN: 14333015     Source Type: Journal    
DOI: 10.1007/s00170-007-1140-7     Document Type: Article

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