PCA fused NN approach for drill wear prediction in drilling mild steel specimen

Proceedings - 2009 2nd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2009

Volumn , Issue , 2009, Pages 85-89

  Panda, S S ^a   Mahapatra, S S ^b  

^a INDIAN INSTITUTE OF TECHNOLOGY PATNA   (India)

^b NATIONAL INSTITUTE OF TECHNOLOGY   (India)

Author keywords

BPNN; Design of experiment; Flank wear; Neuron; PCA; Sensor integration; Signal analysis

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BPNN; CHIP THICKNESS; COMPARATIVE ANALYSIS; CUTTING FORCES; DATA ACQUISITION SYSTEM; DRILL WEAR; FEED-RATES; FLANK WEAR; INPUT PARAMETER; MILD STEEL; PCA; PRINCIPAL COMPONENTS; PROCESS PARAMETERS; SENSOR INTEGRATION; SENSOR SIGNALS; SPINDLE SPEED;

BACKPROPAGATION; CARBON STEEL; COMPUTER SCIENCE; DESIGN OF EXPERIMENTS; EXPERIMENTS; INFORMATION TECHNOLOGY; NEURAL NETWORKS; SENSORS; SIGNAL ANALYSIS; SURFACE ROUGHNESS;

DRILLS;

EID: 70449130778     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCSIT.2009.5234475     Document Type: Conference Paper

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