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• W2213508622 abstract "Abstract It is more economic and environmental-friendly to use recycled plastics in manufacturing, especially under the context of circular economy. Since there are many which formulae exist which improve the different properties of recycled plastics, there is a need to develop decision support tools for evaluating the materials based on multiple properties. In this study, principal component analysis (PCA) was used as a decision support tool to evaluate the performance of plastic composites based on multiple properties. Cluster analysis (CA) was used for classify those properties. The base materials were polypropylene (PP), including a virgin PP (vPP) which is currently used in automobile industry and recycled PP (rPP) recovered from mixed scrap plastics. Two primary fillers were talcum powder (talc) and glass fibre (GF) which are commonly used in the composites for manufacturing industry, maleic anhydride grafted polypropylene (MAPP) was used as compatibiliser. Two vPP-based industrial composites for making automobile parts were used as comparatives. The materials were blended using a twin screw extruder, pelletized, and injected moulded into ISO standard sample specimens for ISO standard mechanical tests. The properties tested were tensile strength, yield strength, flexural modulus, flexural strength, and impact strength. The experimental results revealed that both virgin and recycled plastics are improved by the addition of fillers, and some properties of recycled plastics are better than those of industrial composites. Based on the experimental results, the multiple tested mechanical properties and the composites were classified by CA. Evaluation was performed by PCA, and the scores of some rPP based composites are higher than vPP based composites and industrial materials." @default.
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• W2213508622 date "2016-03-01" @default.
• W2213508622 modified "2023-09-29" @default.
• W2213508622 title "Performance evaluation for composites based on recycled polypropylene using principal component analysis and cluster analysis" @default.
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• W2213508622 doi "https://doi.org/10.1016/j.jclepro.2015.12.062" @default.
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