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Investigations on the combined effects of Thiobacillus Novellus microorganism and process parameters on the bio-machining of NiTi.

Authors :
Pradeep, M
Rajesh, S
Uthayakumar, M
Sivaranjana, P
Syath Abuthakeer, S
Ravichandran, M
Thiagamani, Senthil Muthu Kumar
Mavinkere Rangappa, Sanjay
Siengchin, Suchart
Source :
Biomass Conversion & Biorefinery; Jul2024, Vol. 14 Issue 14, p15419-15428, 10p
Publication Year :
2024

Abstract

This paper discusses the importance and effect of novel microorganisms used in the bio-machining process. The NiTi is used as a work specimen, and Thiobacillus Novellus is used as a microorganism to perform machining operations. The microstructure of the fabricated specimen is more suitable for biomedical applications. The experiment is designed based on the design of experiment (DoE); three different bio-machining parameters are taken as input parameters: shaking speed, pH, and temperature. In addition, the surface roughness (R<subscript>a</subscript>) and specific metal removal rate (SMRR) are taken as performance measures. The cell concentration of the bio-machining process is kept constant for the designed experiment. Finally, for various process conditions, the effectiveness of Thiobacillus Novellus in the machining of NiTi material is presented. The novel Thiobacillus Novellus microorganism is capable removing more material from the specimen compared to Thiooxidans. The experiment results demonstrated that pH and shaking speed both have a role in achieving a higher SMRR and better R<subscript>a</subscript>. Scanning electron microscope (SEM) images are used to understand the type of machining mechanism. The Grey Wolf Algorithm (GWA) optimization method is used to determine the importance of process parameters in achieving a greater SMRR and a better R<subscript>a</subscript>. It has been observed that 95 as shaking speed, 25℃ as temperature, and 4.4 as pH and 80 as shaking speed, 25℃ as temperature, and 2.5 as pH are the best combinations for getting a greater SMRR and better R<subscript>a</subscript>. The developed model can predict the SMRR and R<subscript>a</subscript> with a minimum error of 3.59%. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
21906815
Volume :
14
Issue :
14
Database :
Complementary Index
Journal :
Biomass Conversion & Biorefinery
Publication Type :
Academic Journal
Accession number :
178995549
Full Text :
https://doi.org/10.1007/s13399-022-03616-5