Fuzzy regression modeling for tool performance prediction and degradation detection

International Journal of Neural Systems

Volumn 20, Issue 5, 2010, Pages 405-419

  Li, X ^a   Er, M J ^b   Lim, B S ^a   Zhou, J H ^a   Gan, O P ^a   Rutkowski, L ^c,d  

^a SINGAPORE INSTITUTE OF MANUFACTURING TECHNOLOGY   (Singapore)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^c CZESTOCHOWA UNIVERSITY OF TECHNOLOGY   (Poland)

^d UNIVERSITY OF SOCIAL SCIENCES   (Poland)

Author keywords

cutters degradation detection; Fuzzy logic; multiple regression modeling; self organizing maps; tool performance prediction

Indexed keywords

BACK-PROPAGATION NEURAL NETWORKS; BALL NOSE MILLING; COMPARATIVE STUDIES; CUTTERS DEGRADATION DETECTION; DRY MACHINING; EXPERIMENTAL DATA; FUZZY LOGIC INFERENCE; FUZZY REGRESSIONS; LEARNING SPEED; MULTI-LAYERED; MULTIPLE REGRESSION MODEL; MULTIPLE REGRESSIONS; NONLINEAR PROBLEMS; PREDICTION ACCURACY; PREDICTIVE MODELS; RATE OF CONVERGENCE; REGRESSION MODELING; REMAINING USEFUL LIVES; RULE BASED; TOOL PERFORMANCE; TOOL PERFORMANCE PREDICTION;

BALL MILLING; DEGRADATION; FORECASTING; FUZZY INFERENCE; FUZZY SYSTEMS; HYBRID SYSTEMS; LINEARIZATION; METAL ANALYSIS; MILLING (MACHINING); RADIAL BASIS FUNCTION NETWORKS; REGRESSION ANALYSIS; TOOL STEEL;

SELF ORGANIZING MAPS;

ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; FUZZY LOGIC;

ARTIFICIAL INTELLIGENCE; FUZZY LOGIC; NEURAL NETWORKS (COMPUTER);

EID: 77958062360     PISSN: 01290657     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0129065710002498     Document Type: Article

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