A hybrid M5'-genetic programming approach for ensuring greater trustworthiness of prediction ability in modelling of FDM process

Journal of Intelligent Manufacturing

Volumn 25, Issue 6, 2014, Pages 1349-1365

  Garg, A ^a   Tai, K ^a   Lee, C H ^a   Savalani, M M ^b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

Artificial neural network; Fused deposition modelling; Genetic programming; M5'; Rapid prototyping; Support vector regression; Trustworthiness

Indexed keywords

DEPOSITION; ERROR COMPENSATION; FUSED DEPOSITION MODELING; FUZZY INFERENCE; FUZZY NEURAL NETWORKS; FUZZY SYSTEMS; GENETIC ALGORITHMS; GENETIC PROGRAMMING; NEURAL NETWORKS; RAPID PROTOTYPING; SOFTWARE PROTOTYPING; SUPPORT VECTOR REGRESSION; SURFACE ROUGHNESS;

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; DIMENSIONAL ACCURACY; EMPIRICAL MODELLING; FUSED DEPOSITION MODELLING; IN-DEPTH UNDERSTANDING; PHYSICS-BASED MODELS; SUPPORT VECTOR REGRESSION (SVR); TRUSTWORTHINESS;

PREDICTIVE ANALYTICS;

EID: 84911997374     PISSN: 09565515     EISSN: 15728145     Source Type: Journal    
DOI: 10.1007/s10845-013-0734-1     Document Type: Article

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