International Journal of Advanced Multidisciplinary Application

Machine Learning-Guided Design of Biodegradable Mg-Zn-Ca Alloy Implants for Orthopaedic Fixation

Author(s):Rohan Vaidya

Affiliation: Department of Biomedical Engineering and Centre for Computational Materials Design, Indian Institute of Science, Bengaluru, India

Page No: 14-20

Volume issue & Publishing Year: Volume 3, Issue 7, July 2026

published on: 2026/07/01

Journal: International Journal of Advanced Multidisciplinary Application.(IJAMA)

ISSN NO: 3048-9350

DOI:

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Abstract:
Biodegradable magnesium-based alloys offer a fundamentally attractive alternative to permanent titanium and stainless-steel orthopaedic fixation hardware by eliminating the need for implant-removal surgery, but clinical translation has been constrained by the difficulty of simultaneously satisfying two competing design objectives: degradation must proceed slowly enough to preserve mechanical fixation strength through the bone-healing period, yet completely enough to avoid long-term subcutaneous gas cavities from hydrogen evolution. This study addresses the resulting multidisciplinary materials-design problem by coupling systematic in vitro corrosion and cytocompatibility characterisation of magnesium-zinc-calcium (Mg-Zn-Ca) alloy compositions with a machine learning framework trained to predict degradation rate and mechanical strength retention directly from composition and processing parameters, enabling rapid in silico screening of the compositional design space ahead of resource-intensive physical testing.
Six alloy compositions spanning 0-6 wt% zinc and 0-1.5 wt% calcium were fabricated by induction melting and hot extrusion, then characterised through 90-day immersion testing in simulated body fluid (SBF) at 37°C with mass-loss gravimetry, hydrogen evolution volumetry, and electrochemical impedance spectroscopy; mechanical strength retention was tracked over a 180-day simulated physiological loading protocol; and cytocompatibility was assessed via MTT assay on MC3T3-E1 pre-osteoblast cells exposed to alloy extracts per ISO 10993-5. A gradient-boosted regression tree model was trained on 142 composition-processing-property records (combining this study's experimental data with curated literature values) to predict degradation rate and 90-day strength retention from ten composition and processing features, then used to perform multi-objective optimisation across the full compositional design space.
The Mg-4Zn-0.5Ca composition emerged as the optimal candidate from both experimental characterisation and ML-guided optimisation, achieving a degradation rate of 0.17 mm/year (59% reduction versus pure magnesium's 0.42 mm/year), 90-day compressive strength retention of 78% against human cortical bone's reference strength of approximately 150 MPa, and cell viability of 95% at 12.5% extract concentration, remaining above the ISO 10993-5 cytotoxicity threshold of 70% viability even at full-strength (100%) extract concentration where pure magnesium fell to 31% viability. The gradient-boosted model achieved a root-mean-square error of 0.021 mm/year against held-out experimental degradation-rate measurements, with feature importance analysis identifying zinc content, calcium content, and grain size as the three dominant predictors of degradation behaviour, jointly accounting for over half of total model decision weight. These findings demonstrate that coupling a structured experimental corrosion-biocompatibility dataset with machine learning-based composition screening substantially accelerates identification of clinically viable biodegradable implant alloy compositions relative to combinatorial physical testing alone.

Keywords: biodegradable magnesium alloy, orthopaedic implant, machine learning, degradation kinetics, corrosion modelling, Mg-Zn-Ca, cytocompatibility, gradient boosting, multi-objective optimisation, biomedical materials

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