Affiliations 

  • 1 Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia; Centre of Advanced Manufacturing and Material Processing (AMMP), University of Malaya, 50603 Kuala Lumpur, Malaysia. Electronic address: [email protected]
  • 2 Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia; Centre of Advanced Manufacturing and Material Processing (AMMP), University of Malaya, 50603 Kuala Lumpur, Malaysia; Department of Mechanical Engineering, Faculty of Engineering, University of UCLA, Los Angeles, CA 90032, United States; Department of Mechanical and Aerospace Engineering, University of California, CA 90095-1597, United States. Electronic address: [email protected]
  • 3 Advanced Materials Research Center, Materials Engineering Department, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran. Electronic address: [email protected]
  • 4 UM Power Energy Dedicated Advanced Centre (UMPEDAC),University of Malaya, Kuala Lumpur, Malaysia
  • 5 Institute of Plant Biotechnology and Biology, University of Münster, Schlossplatz 8, 48143 Münster, Germany
  • 6 Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
  • 7 Department of Medical Microbiology, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
  • 8 Department of Chemistry, Faculty of Science, University of Malaya, 50603 Kuala Lumpur, Malaysia
  • 9 Graduate School of Nano-bio Science, Yokohama City University, Yokohama 236-0027, Japan
J Mech Behav Biomed Mater, 2017 May;69:1-18.
PMID: 28027481 DOI: 10.1016/j.jmbbm.2016.11.019

Abstract

Recently, the robust optimization and prediction models have been highly noticed in district of surface engineering and coating techniques to obtain the highest possible output values through least trial and error experiments. Besides, due to necessity of finding the optimum value of dependent variables, the multi-objective metaheuristic models have been proposed to optimize various processes. Herein, oriented mixed oxide nanotubular arrays were grown on Ti-6Al-7Nb (Ti67) implant using physical vapor deposition magnetron sputtering (PVDMS) designed by Taguchi and following electrochemical anodization. The obtained adhesion strength and hardness of Ti67/Nb were modeled by particle swarm optimization (PSO) to predict the outputs performance. According to developed models, multi-objective PSO (MOPSO) run aimed at finding PVDMS inputs to maximize current outputs simultaneously. The provided sputtering parameters were applied as validation experiment and resulted in higher adhesion strength and hardness of interfaced layer with Ti67. The as-deposited Nb layer before and after optimization were anodized in fluoride-base electrolyte for 300min. To crystallize the coatings, the anodically grown mixed oxide TiO2-Nb2O5-Al2O3 nanotubes were annealed at 440°C for 30min. From the FESEM observations, the optimized adhesive Nb interlayer led to further homogeneity of mixed nanotube arrays. As a result of this surface modification, the anodized sample after annealing showed the highest mechanical, tribological, corrosion resistant and in-vitro bioactivity properties, where a thick bone-like apatite layer was formed on the mixed oxide nanotubes surface within 10 days immersion in simulated body fluid (SBF) after applied MOPSO. The novel results of this study can be effective in optimizing a variety of the surface properties of the nanostructured implants.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.