Journal Papers

Shangmou Yang, Carmen Torres-Sanchez, Benoit Ter-Ovanessian, Paul P Conway

Published in the Journal of Alloys and Compounds, Volume 1010, 5 January 2025, 178081

Abstract

Bulk metallic glasses (BMGs) possess higher strength than crystalline alloys because crack propagation is halted through an amorphous structure without grain boundaries or crystal defects. Nanoinclusions can further enhance mechanical properties. Here we investigate how the formation of nanocrystals into a Ti_41.2Zr_10.6Cu_39.1Pd_9.1 BMG matrix via controlled annealing that leads to devitrification of the bulk microstructure, as well as chemical changes to the surface oxide layer, affects mechanical and biological performance. The BMG nanocrystalline composite (BMGC, 12.8 % crystallinity produced via annealing at 415 °C for 5 min, based on crystallisation kinetics studies) was compared to the fully amorphous BMG and the fully crystalline counterpart (annealed at 415 °C and 60 min). BMGC fracture strength (1374.6 MPa) was higher than that of the amorphous BMG (1303.1 MPa) and the fully crystalline specimen (644.4 MPa). Young’s moduli correlated negatively with the degree of crystallisation (78.3–66.2 GPa). The results from in vitro tests on MC3T3-E1 illustrate that the surface chemistry plays a crucial role enhancing osteoblastogenesis: the presence of Zr oxides, wettable surfaces and large values of polar component of Surface Free Energy due to the nanocrystals, and a thinner oxide layer with low concentrations of Cu_xO, positioned BMGC as the preferred substrate. Tailoring amorphicity-to-crystallinity ratio in a Ti-Zr-Cu-Pd BMG is a route to create multifunctional substrates.

M. Khalil, M. Burton, S. Hickinbotham, P. P. Conway, C. Torres-Sanchez

Published in the Engineering Modelling Analysis & Simulation – The NAFEMS Journal Vol. 2, Issue 1, 2025

Abstract

Metallic scaffolds are used as implants to help heal bones. Sheet-based Triply Periodic Minimal Surfaces (TPMS) are of interest due to their high surface-to-volume ratio (S/V) and customisable stiffness. They can be realised using Additive Manufacturing. Other studies investigate porosity and pore size of scaffolds, but they frequently overlook S/V, which is critical for cellular response. Additionally, the limitation of AM (esp. Selective Laser Melting (SLM)) resides in the discrepancies between as-designed and as-built physical and mechanical properties of those structures, and this also needs addressing. This work investigates three types of pure Titanium TPMS scaffolds, with an emphasis on as-designed vs as-built discrepancies and the significance of S/V. As-designed scaffolds reported 70-75% porosity and 25-35 cm^-1 S/V, and stiffness was measured using finite element analysis (FEA) obtaining 6.7-9.3 GPa. The as-built scaffolds had 59-70% porosity and 33-42 cm^-1 S/V. Laboratory compression testing revealed an effective Young’s modulus of 5-9 GPa, comparable to bone tissue. Image-based simulation methods were employed on the as-built samples which reported the stiffness range of 8.3-15 GPa, overestimating it by 54%. It is hypothesised that these discrepancies stem from the secondary roughness on the surfaces, cracks and entrapped voids created during the SLM process, causing reduction in porosity, yet not contributing to structure’s strength. The cyber-physical validation methods presented in this work are a good way to quantify these discrepancies, allowing feedback to the design stages for more predictable as-built structures.

Published in the Artificial Life Journal and accessible via Evolving Novel Gene Regulatory Networks for Structural Engineering Designs | Artificial Life | MIT Press and via the QUB Research Portal using the “View” panel.

Abstract: Engineering design optimization poses a significant challenge, usually requiring human expertise to discover superior solutions. Although various search techniques have been employed to generate diverse designs, their effectiveness is often limited by problem-specific parameter tuning, making them less generalizable and scalable. This article introduces a framework inspired by
evolutionary and developmental (evo-devo) concepts, aiming to automate the evolution of structural engineering designs. In biological systems, evo-devo governs the growth of single-cell organisms into multicellular organisms through the use of gene regulatory networks (GRNs). GRNs are inherently complex and highly nonlinear, and this article explores the use of neural networks and genetic programming as artificial representations of GRNs to emulate such behaviors. To evolve a wide range of Pareto fronts for artificial GRNs, this article introduces a new technique, a real value–encoded neuroevolutionary method termed real-encoded NEAT (RNEAT). The performance of RNEAT is compared with that of two well-known evolutionary search techniques across different 2-D and 3-D problems. The experimental results demonstrate two key findings. First, the proposed framework effectively generates a population of GRNs that can produce diverse structures for both 2-D and 3-D problems. Second, the proposed RNEAT algorithm outperforms its competitors on more than 50% of the problems examined. These results validate the proof of concept underlying the proposed evo-devo-based engineering design evolution.

As published in Key Engineering Materials Volume 967

Ca-Mg-Zn bulk metallic glasses (BMGs) are promising biomaterials for orthopaedic applications because when they get reabsorbed, a retrieval surgery is not needed. In this study, Ca-Mg-Zn metallic glasses with different compositions, Ca_56.02Mg_20.26Zn_23.72 and Zn_50.72Mg_23.44Ca_25.84, were fabricated by induction melting followed by copper mould casting. Their degree of crystallinity was modified by annealing, obtaining exemplar specimens of fully amorphous, partially amorphous (i.e., a BMG composite (BMGC)) and fully crystalline alloys. The microstructure, thermodynamic and corrosion performance of these alloys were evaluated as well as their electrochemical behaviour. The results of polarisation tests demonstrate that the corrosion resistance of the Zn-rich alloy is markedly better than the Ca-rich BMG. Corrosion rates of these Ca-and Zn-rich alloys with different degrees of crystallinity illustrate that the corrosion behaviours of alloys strongly depend on their microstructure, which shows a positive correlation between the corrosion current density and the crystallised volume fraction of the alloy. This study aims to shed light on the impact of the amorphicity-to-crystallinity ratio on the multifunctional properties of BMGs/BMGCs, and to assess how feasible it is to fine-tune those properties by controlling the percentage of crystallinity.

Published in the IET Cyber-Physical Systems: Theory and Applications Journal

Within the Industry 4.0 landscape, humans collaborate with cyber and physical elements to form human-cyber-physical systems (HCPS). These environments are increasingly complex and challenging workspaces due to increasing levels of automation and data availability. An effective system design requires suitable frameworks that consider human activities and needs whilst supporting overall system efficacy.

Although several reviews of frameworks for technology were identified, none of these focused on the human in the system (moving towards Industry 5). The critical literature review presented provides a summary of HCPS frameworks, maps the considerations for a human in HCPS, and provides insight for future framework and system development. The challenges, recommendations, and areas for further research are discussed.

Published in the Biomaterials Advances Journal (Volume 153, October 2023, 213572)

Abstract

Data-enabled approaches that complement experimental testing offer new capabilities to investigate the interplay between chemical, physical and mechanical attributes of alloys and elucidate their effect on biological behaviours. Reported here, instead of physical causation, statistical correlations were used to study the factors responsible for the adhesion, proliferation and maturation of pre-osteoblasts MC3T3-E1 cultured on Titanium alloys. Eight alloys with varying wt% of Niobium, Zirconium, Tin and Tantalum (Ti— (2–22 wt%)Nb— (5–20 wt%)Zr— (0–18 wt%)Sn— (0–14 wt%)Ta) were designed to achieve exemplars of allotropes (incl., metastable-β, β + α′, α″). Following confirmation of their compositions (ICP, EDX) and their crystal structure (XRD, SEM), their compressive bulk properties were measured and their surface features characterised (XPS, SFE). Because these alloys are intended for the manufacture of implantable orthopaedic devices, the correlation focuses on the effect of surface properties on cellular behaviour. Physico-chemical attributes were paired to biological performance, and these highlight the positive interdependencies between oxide composition and proliferation (esp. Ti⁴⁺), and maturation (esp. Zr⁴⁺). The correlation reveals the negative effect of oxide thickness, esp. TiO_x and TaO_x on osteoblastogenesis. This study also shows that the characterisation of the chemical state and elemental electronic structure of the alloys’ surface is more predictive than physical properties, namely SFE and roughness.

Published in the Materials Letters Journal (Volume 343, 15 July, 134367)

Partially sintered satellite particles in scaffolds produced via Selective Laser Melting (SLM) create discrepancies between the as-designed and the as-manufactured properties (esp. porosity). These discrepancies impede direct comparison of manufactured parts performance to computer simulations. We propose anodic electrolysis using an electrolyte based on non-aqueous ethlylene-glycol TiCl4 (EthaTi) to remove the secondary roughness on titanium SLM-ed porous scaffolds. Post-processed gyroid scaffolds regained 10% porosity with respect to their as-manufactured value (65.20 ± 0.23%), which was close to the as-designed value (75.12%). Compared to other well-established electrolytes, this method is cost-effective, user-friendly and practical, as it requires shorter processing times, is temperature-stable and of gentler chemistry.

Published in the Computers In Industry Journal (Volume 150, September 2023, 103943)

Understanding electrical energy consumption of machines and processes is of increasing importance to (i) minimise costs and environmental impact of production activities and (ii) provide an additional information stream to inform condition monitoring systems (i.e. digital twins) about a machine’s status and health. The research outlined in this paper develops a Virtual Machining Energy Toolkit (V_MET) to predict the electrical power consumption of a Computer Numeric Control (CNC) milling machine cutting a particular part program from preparatory codes (i.e. G code). In this way the evaluation of the energy impact of manufacturing part programs prior to implementation and real-time monitoring of the process can become a routine activity at part of a total manufacturing system optimisation. The novelty of this work lies in the inclusion of a virtual CNC process model to determine cutting geometry (i.e. width and depth of cut) to enable the prediction of relatively complex part program geometry.

V_MET consists of three components: (i) the NC interpreter to extract key parameters (e.g. spindle speed, feed rate, tool path) from G-code instructions, (ii) a virtual CNC process model to determine instantaneous cutting geometry (i.e. width and depth of cut) and the material removal from the resulting machining by simulating the motion of the tool path to predict the interaction between the tool tip and workpiece and (iii) an energy model to predict the electrical power consumption for a given set of conditions, developed using regression analysis of data collected under real manufacturing conditions.

Validation of V_MET has been conducted by physical machining of different product features to evaluate the validity over a range of different cutting parameters, NC operations (i.e. linear, clockwise interpolations) and repasses over previously cut regions. Overall good accuracy has been observed for the predicted energy requirements as a function of the cutting regimes, with 4.3% error in total energy and Mean Average Percentage Error (MAPE) of 5.6% when compared with measurements taken during physical cutting trials.

Published as an IEEE Access Research Article, January 2023

Genetic algorithms (GAs) have been used to evolve optimal/sub-optimal solutions of many problems. When using GAs for evolving solutions, often fitness evaluation is the most computationally expensive, and this discourages researchers from applying GAs for computationally challenging problems. This paper presents an approach for generating offspring based on a local fitness landscape exploration to increase the speed of the search for optimal/sub-optimal solutions and to evolve better fitness solutions. The proposed algorithm, “Fitness Landscape Exploration based Genetic Algorithm” (FLEX-GA) can be applied to single and multi-objective optimization problems. Experiments were conducted on several single and multi-objective benchmark problems with and without constraints. The performance of the FLEX-based algorithm on single-objective problems is compared with a canonical GA and other algorithms. For multi-objective benchmark problems, the comparison is made with NSGA-II, and other multi-objective optimization algorithms. Lastly, Pareto solutions are evolved on eight real-world multi-objective optimization problems, and a comparative performance is presented with NSGA-II. Experimental results show that using FLEX on most of the single and multi-objective problems, the speed of the search improves up to 50% and the quality of solutions also improves. These results provide sufficient evidence of the applicability of fitness landscape approximation-based algorithms for solving real-world optimization problems.

Published in the Journal of the Mechanical Behaviour of Biomedical Materials 124 (2021) 104858

Journal of the Mechanical Behavior of Biomedical Materials | ScienceDirect.com by Elsevier

Published in Celebrating Excellence in Advanced Engineering Materials: Women in Engineering Materials: Advanced Engineering Materials (wiley.com)

Triply periodic minimal surface and trabecular-like structures are common approaches in tissue engineering. There are few comparative studies assessing the impact of topology on biological and mechanical performance independent of porosity and surface area. Herein, these two features are controlled, despite design-to-manufacture disparities intrinsic to selective laser melting. Smoothed trabecular scaffolds, with more accessible throats lined with microporosity, enhance osteoblastogenesis.

Published in the Journal of Computational Design and Engineering

This work presents a novel bottom-up methodology to generate designs that can be tightly integrated with the additive manufacturing environment and that can respond flexibly to changes in that environment….The method is bio-inspired, based on strategies observed in natural systems, particularly in biological growth and development. The design geometry is grown in a computer-aided design-based, bio-inspired generative design system called ‘Biohaviour’.