Neural network approach for estimating the residual tensile strength after drilling in uni-directional glass fiber reinforced plastic laminates

Materials and Design

Volumn 31, Issue 6, 2010, Pages 2790-2795

  Mishra, Roshan ^a   Malik, Jagannath ^a   Singh, Inderdeep ^a   Davim, João Paulo ^b  

^a INDIAN INSTITUTE OF TECHNOLOGY ROORKEE   (India)

^b UNIVERSITY OF AVEIRO   (Portugal)

Author keywords

A. Glass fiber reinforced epoxy composites; C. Drilling; E. Residual tensile strength

Indexed keywords

A. GLASS FIBER REINFORCED EPOXY COMPOSITES; ARTIFICIAL NEURAL NETWORK; C. DRILLING; COMPOSITE PRODUCTS; DRILL POINT GEOMETRY; DRILLED HOLES; FEED-RATES; GLASS FIBER REINFORCED EPOXY COMPOSITES; INDUCED DAMAGE; INPUT VARIABLES; LONG TERM PERFORMANCE; PREDICTIVE MODELS; SPINDLE SPEED; TESTING DATA;

DRILLING; FIBER REINFORCED PLASTICS; FIBERS; GLASS FIBERS; NEURAL NETWORKS; PLASTIC LAMINATES; POLYESTERS; PREDICTIVE CONTROL SYSTEMS; REINFORCED PLASTICS; STRUCTURAL PANELS;

TENSILE STRENGTH;

EID: 76949094140     PISSN: 02641275     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.matdes.2010.01.011     Document Type: Article

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