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Prediction and Optimization of Mechanical Properties of Polypropylene/Waste Tire Powder Blends using a Hybrid Artificial Neural Network-Genetic Algorithm (GA-ANN)

School of Polymer Science and Engineering, Gyeongsang National University, Jinju, Gyeongnam, Korea 660-700, Korea

Marissa A. Paglicawan

School of Polymer Science and Engineering, Gyeongsang National University, Jinju, Gyeongnam, Korea 660-700, Korea, Industrial Technology Development Institute, Department of Science and Technology, Bicutan, Taguig City, 1631 Philippines

Zhen-Xiu Zhang

School of Polymer Science and Engineering, Gyeongsang National University, Jinju, Gyeongnam, Korea 660-700, Korea

Sung Hyo Lee

Resource Recirculation Division, National Institute of Environmental Research, Kyungseo-dong, Seo-gu, Incheon 404-708, Korea

Zhen-Xiang Xin

Key Laboratory of Rubber and Plastics, Qingdao University of Science and Technology, Qingdao, People's Republic of China, 26604

Jin Kuk Kim

School of Polymer Science and Engineering, Gyeongsang National University, Jinju, Gyeongnam, Korea 660-700, Korea, rubber{at}gnu.ac.kr pub.com

Blends of Polypropylene (PP) and waste ground rubber tire powder are studied with respect to the effect of ethylene—propylene—diene monomer (EPDM) and polypropylene grafted maleic anhydride (PP-g-MA) compatibilizer content by using the Design of Experiments methodology, whereby the effect of the four polymers content on the final mechanical properties are predicted. Uniform design method is especially adopted for its advantages. Optimization is done using hybrid Artificial Neural Network-Genetic Algorithm technique. A rubber formulary with respect to the four ingredients are optimized having maximum tensile strength and then compared with a blend predicted to have maximum elongation at break. It is concluded that the blends show fairly good properties provided that it has a relatively higher concentration of PP-g-MA and EPDM content. SEM investigations also corroborates with the observed mechanical properties. A quantitative relationship is then shown between the material concentration and the mechanical properties as a set of contour plots, which are then tested and confirmed experimentally to conform to the optimum blend ratio.

Key Words: artificial neural network • compatibilization • design of experiments • Genetic Algorithm • recycling.

Journal of Thermoplastic Composite Materials, Vol. 21, No. 1, 51-69 (2008)
DOI: 10.1177/0892705707084543


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