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Modelling Techniques to Analyse the Functionality of Metallic Glass Alloys as Biomedical Implants: A Mini-review

  • Shubhra Shitole , S. Vincent , Abdel-Hamid Ismail Mourad and Priya Sudha
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Smart Materials
This chapter is in the book Smart Materials

Abstract

Metallic glass alloysmetallic glass alloys have emerged as a promising class of materials for orthopaedic knee implants due to their unique combination of mechanical properties, including high strength, excellent wear resistance, and biocompatibility. Understanding the mechanical behaviour and performance of metallic glass under physiological stress circumstances is essential for their design and optimization. The modelling methods used to examine the mechanical reaction and long-term functionality of metallic glass biomedical applications are reviewed in this chapter. We explore in depth several modelling techniques, such as finite element analysis, molecular dynamic simulations, statistical modelling, and machine learningmachine learning. The review addresses issues such as stress distribution prediction, wear behaviour, and assessment of fatigue life. Also discussed are the drawbacks and difficulties of modelling these intricate materials. This review intends to promote the development of advanced metallic glass with improved mechanical properties, increased lifespan, and improved patient outcomes by offering a thorough examination of modelling approaches.

Abstract

Metallic glass alloysmetallic glass alloys have emerged as a promising class of materials for orthopaedic knee implants due to their unique combination of mechanical properties, including high strength, excellent wear resistance, and biocompatibility. Understanding the mechanical behaviour and performance of metallic glass under physiological stress circumstances is essential for their design and optimization. The modelling methods used to examine the mechanical reaction and long-term functionality of metallic glass biomedical applications are reviewed in this chapter. We explore in depth several modelling techniques, such as finite element analysis, molecular dynamic simulations, statistical modelling, and machine learningmachine learning. The review addresses issues such as stress distribution prediction, wear behaviour, and assessment of fatigue life. Also discussed are the drawbacks and difficulties of modelling these intricate materials. This review intends to promote the development of advanced metallic glass with improved mechanical properties, increased lifespan, and improved patient outcomes by offering a thorough examination of modelling approaches.

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