Effect of Caesalpinia decapetala on the Dry Sliding Wear Behavior of Epoxy Composites

Author:

Biratu Hailemariam1,Gelaw Mengistu1,Shahapurkar Kiran1ORCID,Chenrayan Venkatesh12ORCID,Soudagar Manzoore Elahi M.34,Tirth Vineet56ORCID,Algahtani Ali56ORCID,Al-Mughanam Tawfiq7

Affiliation:

1. Department of Mechanical Engineering, School of Mechanical, Chemical and Materials Engineering, Adama Science and Technology University, Adama, 1888, Ethiopia

2. Department of Mechanical Engineering, Knowledge Institute of Technology, 637504, Salem, India

3. Department of Mechanical Engineering, Graphic Era (Deemed to be University), Dehradun, Uttarakhand 248002, India

4. Institute of Sustainable Energy, Universiti Tenaga National, Jalan IKRAM-UNITEN, 43000, Kajang, Selangor, Malaysia

5. Mechanical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Asir, Saudi Arabia

6. Research Center for Advanced Materials Science (RCAMS), King Khalid University, Guraiger, Abha-61413, Asir, Saudi Arabia

7. Department of Mechanical Engineering, College of Engineering, King Faisal University, P.O. Box 380, Al-Ahsa 31982, Saudi Arabia

Abstract

The present research investigates the wear characteristics of an epoxy composite reinforced with a novel Caesalpinia decapetala (CD) shell. The CD is available abundantly worldwide, especially in Ethiopia, particularly in East and West Oromia near West Harar. The composite specimens were processed in the open mould casting technique by varying the vol.% of CD in 10, 20, and 30. EDS is used to evaluate the important elements present in the CD. The density of composites increases with the increase in the content of CD, while the void content estimations reveal good control over the composite fabrication. The wear response of composites is investigated by varying the sliding distance and load and by maintaining a fixed velocity (5 m/s). At a 5 km slide distance and 50 N load, the 30 vol.% Caesalpinia decapetala composition depicts better wear resistance and friction coefficient than other compositions. Experimental results are used to envisage the ideal wear factors and to assess the influence of parameters over the two wear objectives, wear rate and CoF. The grey relational analysis- (GRA-) coupled artificial neural network (ANN) hybrid technique was employed for the prediction and validation. It has been observed that a trivial error of 0.49% amidst GRA and ANN estimation is observed.

Funder

King Faisal University

Publisher

Hindawi Limited

Subject

Polymers and Plastics

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