Open Access

ARTICLE

Process Modelling and Experimental Analysis of Optimal Specimen Selection in Organic CMCs

by P. V. Rajesh¹, Kanak Kalita^2,*, Xiao-Zhi Gao³

1 Faculty of Mechanical Engineering, Saranathan College of Engineering, Trichy, 620012, India
2 Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, 600062, India
3 School of Computing, University of Eastern Finland, Kuopio, FI-70211, Finland

* Corresponding Author: Kanak Kalita. Email:

Computers, Materials & Continua 2022, 70(2), 2415-2433. https://doi.org/10.32604/cmc.2022.018247

Received 02 March 2021; Accepted 06 June 2021; Issue published 27 September 2021

Abstract

Bone grafting is a surgical restructuring procedure of replacing broken bones and reconstructing missing bone pieces so that complex bone fractures can be repaired to avoid any serious health risk as well as permanent bone disfiguration. Normally, human bones tend to regenerate and heal completely from fracture. But it needs a small scaffold to provide the necessary space to grow. Bone implants allow a broken bone to grow seamlessly. Traditionally, non-corrosive metal alloys are used for fixing broken bones. A metal plate is fastened between two ends of broken bones to join them. However, issues like high weight, high cost, low wear resistance, etc. led to the emergence of ceramics and ceramic-based composites in surgical engineering. Recent trends indicate the usage of organic ceramics and their associated composites as biomimetic materials for prostheses and other biomedical applications. This research paper deals with the fabrication of one such type of ceramic matrix composite (CMC) specimen with sea sponge and cuttlebone using powder metallurgy process by varying composition of cuttlebone, the particle size of the ceramics and sintering temperature of green compacts. Evaluation of thermo-mechanical properties and optimization of process parameters is carried out using the preferential selection index (PSI) method. The results obtained from this technique are further validated using Multi-Level General Factorial Design (MLGFD).

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Keywords

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