PREDICTION OF MECHANICAL PROPERTIES OF LDPE-TPS NANOCOMPOSITES USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM

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SABETZADEH MARYAM; BAGHERI ROUH ELAH; SHAHRIARI KAHKESHI MARYAM

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Journal: Iranian Journal of Polymer Science and Technology Year:2012 | Volume:25 | Issue:3 (ISSUE NO. 119) Page(s): 172-180

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Information Journal Paper

Title

PREDICTION OF MECHANICAL PROPERTIES OF LDPE-TPS NANOCOMPOSITES USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM

Author(s)

SABETZADEH MARYAM | BAGHERI ROUH ELAH | SHAHRIARI KAHKESHI MARYAM | Issue Writer Certificate

Keywords

PREDICTIONQ2

NANOCLAYQ3

TEMPERATUREQ2

TORQUEQ2

MECHANICAL PROPERTIESQ2

Abstract

The changes in the behavior of MECHANICAL PROPERTIES of low density polyethylene-thermoplastic corn starch (LDPE-TPCS) nanocomposites were studied by an adaptive neuro-fuzzy interference system. LDPE-TPCS composites containing different quantities of NANOCLAY (Cloisite®15A, 0.5-3 wt%) were prepared by extrusion process. In practice, it is difficult to carry out several experiments to identify the relationship between the extrusion process parameters and MECHANICAL PROPERTIES of the nanocomposites. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) was used for non-linear mapping between the processing parameters and the MECHANICAL PROPERTIES of LDPE-TPCS nanocomposites. ANFIS model due to possessing inference ability of fuzzy systems and also the learning feature of neural networks, could be used as a multiple inputs-multiple outputs to predict MECHANICAL PROPERTIES (such as ultimate tensile strength, elongation-at-break, Young’s modulus and relative impact strength) of the nanocomposites. The proposed ANFIS model utilizes TEMPERATURE, TORQUE and Cloisite®15A contents as input parameters to predict the desired MECHANICAL PROPERTIES. The results obtained in this work indicated that ANFIS is an effective and intelligent method for PREDICTION of the MECHANICAL PROPERTIES of the LDPE-TPCS nanocomposites with a good accuracy. The statistical quality of the ANFIS model was significant due to its acceptable mean square error criterion and good correlation coefficient (values>0.8) between the experimental and simulated outputs.

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APA: Copy

SABETZADEH, MARYAM, BAGHERI, ROUH ELAH, & SHAHRIARI KAHKESHI, MARYAM. (2012). PREDICTION OF MECHANICAL PROPERTIES OF LDPE-TPS NANOCOMPOSITES USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM. IRANIAN JOURNAL OF POLYMER SCIENCE AND TECHNOLOGY (PERSIAN), 25(3 (ISSUE NO. 119)), 172-180. SID. https://sid.ir/paper/6646/en

Vancouver: Copy

SABETZADEH MARYAM, BAGHERI ROUH ELAH, SHAHRIARI KAHKESHI MARYAM. PREDICTION OF MECHANICAL PROPERTIES OF LDPE-TPS NANOCOMPOSITES USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM. IRANIAN JOURNAL OF POLYMER SCIENCE AND TECHNOLOGY (PERSIAN)[Internet]. 2012;25(3 (ISSUE NO. 119)):172-180. Available from: https://sid.ir/paper/6646/en

IEEE: Copy

MARYAM SABETZADEH, ROUH ELAH BAGHERI, and MARYAM SHAHRIARI KAHKESHI, “PREDICTION OF MECHANICAL PROPERTIES OF LDPE-TPS NANOCOMPOSITES USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM,” IRANIAN JOURNAL OF POLYMER SCIENCE AND TECHNOLOGY (PERSIAN), vol. 25, no. 3 (ISSUE NO. 119), pp. 172–180, 2012, [Online]. Available: https://sid.ir/paper/6646/en

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