Study on Injection Molding Parameters of Graphene/Polypropylene Composites

• Main Authors: PENG,WEI-CHANG, 彭偉彰
• Other Authors: YANG,CHING-BEEN
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/kp2w97

碩士 === 南亞技術學院 === 機械工程系機械與機電工程碩士班 === 106 === Polypropylene is a common thermoplastic with greater impact resistance and strong mechanical properties and is widely used in the industry. With high thermal conductivity and low resistivity, graphene ushered in a new generation of materials in electronic components. This study fabricated injection molding test specimens using a graphene/polypropylene composite. Using the Taguchi method and an L9 orthogonal array, various combinations of injection molding process parameters, namely A. injection temperature, B. packing time, C. injection pressure, and D. graphene proportion, were tested. The resulting thermal conductivity, resistivity, and tensile strength of the specimens were analyzed to identify the parameter combinations with optimal single-objective quality. Under the single objective of thermal conductivity, Taguchi analysis identified A3B1C2D3 as the optimal parameter combination with a thermal conductivity coefficient of 0.102(W/m•K), whereas the L9 orthogonal array identified A2B1C2D3 as the optimal parameter combination with a thermal conductivity coefficient of 0.081(W/m•K), thereby presenting an improvement rate of 25.9%. Under the single objective of resistivity, Taguchi analysis identified A1B2C1D3 as the optimal parameter combination with resistivity 2802(MΩ), while the L9 orthogonal array identified A3B2C1D3 as the optimal parameter combination with resistivity 2956.333(MΩ), thereby presenting an improvement rate of 5.2%. Under the single objective of tensile strength, Taguchi analysis identified A1B2C3D2 as the optimal parameter combination with tensile strength 2.84(kgf/mm2), while the L9 orthogonal array identified A1B3C3D3 as the optimal parameter combination with tensile strength 2.79(kgf/mm2), thereby presenting an improvement rate of 1.7%. The Taguchi method is generally used to find optimal parameter combinations for a single objective. However, using the Taguchi method to find optimal parameter combinations that meet multiple objectives will often result in conflicting results. To address this issue, this study applied gray relational analysis in conjunction with the Taguchi method. The gray relational analysis results revealed that under the three quality objectives of thermal conductivity, resistivity, and tensile strength, all of the parameter combinations displayed a slight decrease in one quality objective but improvement in the other two.