Recent Research Progress and Future Prospects in the Additive Manufacturing of Biomedical Magnesium and Titanium Implants

Elgazzar, Haytham; Abdelghany, Khalid

doi:10.1201/9781003327394-8

Cited by 2 publications

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“…The evolution of additive manufacturing (AM) gives access to fabricate complex structures with excellent dimensionality and tailored mechanical properties (Gibson et al , 2015; Elgazzar and Abdelghany, 2022). Selective laser sintering (SLS) is a powder-based AM process that fabricates parts using a laser source by sintering polymer powders layer by layer.…”

Section: Introductionmentioning

confidence: 99%

Quasi-static compression and energy absorption behaviour of polymeric selective laser sintered open cell lattices under varying relative densities

2024

RPJ

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Purpose The purpose of this paper is to compare the influence of relative density (RD) and strain rates on failure mechanism and specific energy absorption (SEA) of polyamide lattices ranging from bending to stretch-dominated structures using selective laser sintering (SLS). Design/methodology/approach Three bending and two stretch-dominated unit cells were selected based on the Maxwell stability criterion. Lattices were designed with three RD and fabricated by SLS technique using PA12 material. Quasi-static compression tests with three strain rates were carried out using Taguchi's L9 experiments. The lattice compressive behaviour was verified with the Gibson–Ashby analytical model. Findings It has been observed that RD and strain rates played a vital role in lattice compressive properties by controlling failure mechanisms, resulting in distinct post-yielding responses as fluctuating and stable hardening in the plateau region. Analysis of variance (ANOVA) displayed the significant impact of RD and emphasised dissimilar influences of strain rate that vary to cell topology. Bending-dominated lattices showed better compressive properties than stretch-dominated lattices. The interesting observation is that stretch-dominated lattices with over-stiff topology exhibited less compressive properties contrary to the Maxwell stability criterion, whereas strain rate has less influence on the SEA of face-centered and body-centered cubic unit cells with vertical and horizontal struts (FBCCXYZ). Practical implications This comparative study is expected to provide new prospects for designing end-user parts that undergo various impact conditions like automotive bumpers and evolving techniques like hybrid and functionally graded lattices. Originality/value To the best of the authors' knowledge, this is the first work that relates the strain rate with compressive properties and also highlights the lattice behaviour transformation from ductile to brittle while the increase of RD and strain rate analytically using the Gibson–Ashby analytical model.

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Section: Introductionmentioning

confidence: 99%

Quasi-static compression and energy absorption behaviour of polymeric selective laser sintered open cell lattices under varying relative densities

2024

RPJ

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A data-driven ensemble machine learning approach for predicting the mechanical strength of 3D printed orthopaedic bone screws

Agarwal,

Singh,

Gupta

2023

Proceedings of the Institution of Mechanical Engineers, Part E:

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The mechanical strength of three-dimensional (3D) printed orthopaedic bone screws plays a major role in load-bearing bone fractures and deformities. Orthopaedic screws need to have enough mechanical strength to support and rehabilitate fracture sites under loads. Several process parameters are used in 3D printing technology for part fabrication; monitoring the mechanical strength of fabricated parts is a difficult and tedious task. The prediction of mechanical strength by a data-driven machine learning (ML) approach can be the solution. The present work leverages ensemble ML algorithms for predicting the mechanical strength of 3D-printed orthopaedic screws. Fused deposition modelling-based technology is utilised for orthopaedic cortical screw fabrication. Different process parameters were varied at different levels for the fabrication of orthopaedic screws. Ensemble ML techniques such as XGBoost, AdaBoost and GradientBoost are employed. The robustness and performance of predictive models were compared at different error metrics to offer an adequate predictive ML model. The XGBoost ensemble model was observed to be the most accurate with the least error metrics. Honeycomb-patterned with 100% infill percentage, layer height of 0.06 mm, and wall thickness of 1 mm may be selected for the maximum strength of a 3D-printed cortical screw. Moreover, the ensemble ML model's predictive performance and adequacy were higher than the base learning models.

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Recent Research Progress and Future Prospects in the Additive Manufacturing of Biomedical Magnesium and Titanium Implants

Cited by 2 publications

References 0 publications

Quasi-static compression and energy absorption behaviour of polymeric selective laser sintered open cell lattices under varying relative densities

Quasi-static compression and energy absorption behaviour of polymeric selective laser sintered open cell lattices under varying relative densities

A data-driven ensemble machine learning approach for predicting the mechanical strength of 3D printed orthopaedic bone screws

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