The correct treatment of waste from agriculture has become a serious challenge on a global magnitude. As agro-wastes are often incinerated, they may be toughened and used in composite materials to produce bioplastics. The current research paper explores the impact of incorporating corn-husk filler-based thermoset composite, focusing on its tribological effects. Different weight content of corn-husk particulates (ranging from 0, 2, 4, 6, and 8%) are added to epoxy to create the specimen. The wear tests involve a certain sliding length ranging from 1 to 3 km, applied forces in between 10 and 20 N and a constant velocity of 1 m/s. The specimen with less amount of particles percentage has delivered a satisfactory tribological performance. A scanning electron microscope is employed for precise assessment of the specimen’s tested layers. The data presented in this study are validated by applying artificial neural network (ANN) model for diverse tribological features.

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Experimental and ANN Evaluation on Tribological Performance of Corn-Husk Filler-Based Composites

  • Vijay Kumar Mahakur,
  • Biki Prasad Hazam,
  • Nabojyoti Chauhan,
  • Sumit Bhowmik,
  • Santosh Kumar

摘要

The correct treatment of waste from agriculture has become a serious challenge on a global magnitude. As agro-wastes are often incinerated, they may be toughened and used in composite materials to produce bioplastics. The current research paper explores the impact of incorporating corn-husk filler-based thermoset composite, focusing on its tribological effects. Different weight content of corn-husk particulates (ranging from 0, 2, 4, 6, and 8%) are added to epoxy to create the specimen. The wear tests involve a certain sliding length ranging from 1 to 3 km, applied forces in between 10 and 20 N and a constant velocity of 1 m/s. The specimen with less amount of particles percentage has delivered a satisfactory tribological performance. A scanning electron microscope is employed for precise assessment of the specimen’s tested layers. The data presented in this study are validated by applying artificial neural network (ANN) model for diverse tribological features.