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首页> 外文期刊>Journal of Applied Polymer Science >Physical, mechanical, and sliding wear behavior of micro-sized Linz-Donawintz slag filled epoxy composites
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Physical, mechanical, and sliding wear behavior of micro-sized Linz-Donawintz slag filled epoxy composites

机译:Physical, mechanical, and sliding wear behavior of micro-sized Linz-Donawintz slag filled epoxy composites

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摘要

Linz-Donawintz (LD) slag is a solid waste produced during the basic oxygen furnace process of steel making and is more often disposed of on nearby land causing a disturbance in vegetation and human health. Hence, judicial management of LD slag is a need for an hour. For this purpose, the usability of LD slags as a reinforcement material in epoxy composites was investigated in the present work. In this investigation, the physical properties (density, void content, water absorption rate), mechanical properties (tensile properties, flexural properties, compressive strength, hardness), and tribological properties (sliding wear behavior) are reported. From the experimental investigation, it is found that the density, void content, and water intake capacity increase with LD slag loading. The maximum voids present in the composite is 1.92 for epoxy/40 wt. LD slag composite. Similarly, maximum water uptake of only 0.6 is observed for composite with maximum filler content and for maximum immersion time. Further, it is observed that the strength of composite under tensile and flexural loading was compromised slightly. At 40 wt. of LD slag, the measured tensile and flexural strength are 20.8 MPa and 28.78 MPa respectively. The strength of composites under compressive loading increases with filler content and reaches a maximum of 108 MPa for epoxy/40 wt. LD slag composite. The high hardness of filler assists in increasing the hardness of the composites. The hardness of neat epoxy is Shore-D number 75 which increases to Shore-D number 84. With increased hardness, ultimately the specific wear rate under dry sliding conditions reduces. Taguchi's L-25 orthogonal array having four factors, each at five different levels is implemented to conduct the sliding wear tests. A forecast model is built on an artificial neural network (ANN) which successfully envisages the sliding wear performance of the composite under investigation inside and outside the experimental domain.

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