Prediction and optimization of mechanical properties of polypropylene/waste tire powder blends using a hybrid artificial neural network-genetic algorithm (GA-ANN)

Journal of Thermoplastic Composite Materials

Volumn 21, Issue 1, 2008, Pages 51-69

  Balasubramanian, Maridass ^a   Paglicawan, Marissa A ^a,b   Zhang, Zhen Xiu ^a   Lee, Sung Hyo ^c   Xin, Zhen Xiang ^d   Kim, Jin Kuk ^a  

^a Gyeongsang National University   (South Korea)

^b Industrial Technology Development Institute Philippines   (Philippines)

^c National Institute of Environmental Research   (South Korea)

^d QINGDAO UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Artificial neural network; Compatibilization; Design of experiments; Genetic Algorithm; Recycling

Indexed keywords

COMPATIBILIZERS; DESIGN OF EXPERIMENTS; GENETIC ALGORITHMS; NEURAL NETWORKS; OPTIMIZATION; RECYCLING;

ETHYLENE-PROPYLENE-DIENE MONOMER (EPDM); WASTE TIRE POWDER BLENDS;

POLYPROPYLENES;

EID: 37249081082     PISSN: 08927057     EISSN: 15307980     Source Type: Journal    
DOI: 10.1177/0892705707084543     Document Type: Article

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