Journal Papers

This paper was published in an IEEE Access journal of Advancing Technology for Humanity, Vol 14, accessible via the following link: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=11570195, DOI: 10.1109/ACCESS.2026.3705269

Abstract:

Using simple artificial evolution to assist in the design of complex engineering systems has a number of issues regarding diversity, scalability etc. A possible improvement is to look again towards biological systems and consider both evolution and development processes. Artificial evolutionary development (Evo-Devo) systems often lack intrinsic mechanisms to regulate the termination of artificial growth. Biological systems, from which the analogy is drawn, make use of internal and external states—the organism’s makeup and environmental forces. This perpetual state can act as a form of memory within the system, allowing for the build up of chemical concentrations and gradients, for example. This work explores how the introduction of memory, through the support of an LSTM-Gene Regulatory Network (LSTM-GRN), allows for the emergence of stopping criteria in an Evo-Devo design system. The results show that memory evolves such that it is able to control the development process and its termination in simpler settings, although this behaviour is less consistent in more complex environments. The LSTM-GRN is capable of performing well in simple tasks such as linear space exploration, but is constrained by its fixed topology when applied to more complex branching exploration tasks. This work sets the foundation for further exploration into how memory can impact the diversity of solutions found in Evo-Devo systems, thus providing more varied approaches to tackling more complex design challenges. Where more complex design requirements are necessary, designers can rely on more automated processes to search a design space. With more nuanced stopping criteria, this space becomes much richer, allowing for better design identification.

This paper will be included in An Open Access Journal of Production & Manufacturing Research, VOL. 14, NO. 1, 2661532, accessible via the following link: https://www.tandfonline.com/doi/full/10.1080/21693277.2026.2661532#abstract https://doi.org/10.1080/21693277.2026.2661532

Abstract

Predictive maintenance tools show promising results in the literature; however, the artificial intelligence and machine learning algorithms that underpin these approaches require large volumes of training data. This presents a challenge when sensor instrumentation is recent or limited, restricting the availability of historical data. One potential solution is generation of simulated datasets capable of representing fault conditions. This paper investigates a hybrid simulation approach that incorporates real healthy operating data from the target system to improve simulation validity without explicitly modelling every component. The approach is evaluated using motor bearing defect experiments from Case Western Reserve University. The simulation successfully reproduces key macroscopic fault features and performs well in classification tasks with real data. However, it fails to capture the full complexity of real signatures, as confirmed by a two-group t-test comparing simulated and experimental results, highlighting limitations of simulated data substitutes.

This paper will included in the Advanced Engineering Informatics Journal, Volume 73, accessible via the following links https://www.sciencedirect.com/science/article/pii/S1474034626002922 and https://doi.org/10.1016/j.aei.2026.104600

Abstract

Robotic design and optimization often require extensive simulation-based iterations, particularly for predicting static deformations under varying postures, external loads, and gravitational conditions. Conventional finite element simulations can achieve high accuracy but are computationally expensive, limiting their use in large-scale design optimization. Data-driven approaches have emerged to accelerate prediction; however, most existing methods are restricted to simplified scenarios, such as fixed geometries with varying poses or fixed poses with varying loads, and typically predict only partial deformation at points rather than the full deformation field. To enable fast and generalizable deformation prediction, a new neural operator architecture named Robot-NO is developed. A geometric–load graph based on the finite element mesh connectivity, where each node encodes spatial coordinates with local load conditions is constructed. A graph neural network is then employed to extract a unified global representation that captures both geometric structure and external force distribution. A query–decoder module combines global features with arbitrary query-coordinates to predict the full deformation
field. This query-based formulation enables accurate continuous deformation prediction across varying robot geometries, postures, and load conditions. Experimental results demonstrate that the proposed model achieves a root mean square error of 0.91 μm in deformation prediction and is 6000 times faster than conventional finite element simulations. This significant improvement enables rapid design, analysis and optimization for robotic mechanisms.

This Paper available online since 9 March 2026, was later included in the Materials Letters Scientific Journal volume 412 in June 2026, accessible via the following links https://www.sciencedirect.com/science/article/pii/S0167577X26003885?via%3Dihub and https://doi.org/10.1016/j.matlet.2026.140428

Abstract – Disinfection of medical devices is crucial to patient safety. Notably, an incorrect selection of disinfectant can damage the material surface and impact its performance. Ti-Zr-Cu-Pd bulk metallic glasses (BMGs) show considerable potential for biomedical use, however standard sterilisation protocols remain undeveloped. This study evaluates the effects of common clinical disinfection solutions, including NaOH, NaClO, and Virkon®, on Ti-Zr-Cu-Pd BMGs, to assess their impact on surface characteristics (chemical composition, surface free energy) and on biocompatibility (pre-osteoblast cells behaviour). The findings aim to inform practical guidelines for safe and effective cleaning and sterilisation of devices that contain these alloys.

Published in The International Journal of Advanced Manufacturing Technology, accessible via the following links https://link.springer.com/journal/170 and https://doi.org/10.1007/s00170-026-17862-8

Abstract
Tolerance allocation is an important design step for determining robot accuracy and directly affecting manufacturing cost. However, existing methods typically consider dimension synthesis first before tolerances are allocated, which neglects the manufacturability constraints arising from the dependency between part size and achievable tolerance grades. This often leads to costly iterations between design and manufacturing and increasing manufacturing cost. To address this issue, an integrated tolerance allocation and dimensional synthesis method of robot design is proposed for optimizing both positioning accuracy reliability and manufacturing cost. The method simultaneously optimizes joint dimensions and corresponding tolerances by formulating a cost function that captures the relationship between dimensional parameters, robot end-effector accuracy reliability, tolerance-grade rules, and manufacturing cost. Additionally, a matrix-based Monte Carlo simulation (MCS) method is introduced to accelerate evaluation workspace-wide reliability under tolerance uncertainty. NSGA-II multi-objective optimization algorithm is employed to find the Pareto front of the optimal solutions. A case study of a surgical robot is taken to demonstrate the effectiveness of the proposed approach. Results show that the proposed method can reduce 22% of manufacturing cost while achieving better positioning accuracy reliability compared to the traditional tolerance allocation method, and the speed of matrix-based MCS method is improved by 400 times compared to the point-based MCS method.

This paper has been published in the MDPI Big Data and Cognitive Computing Journal 2025, 9(12), 305; https://doi.org/10.3390/bdcc9120305 (registering DOI) and belongs to the Special Issue Artificial Intelligence (AI) and Natural Language Processing (NLP))

Abstract

Following PRISMA-ScR guidelines, this scoping review systematically maps the landscape of Large Language Models (LLMs) in mechanical engineering. A search of four major databases (Scopus, IEEE Xplore, ACM Digital Library, Web of Science) and a rigorous screening process yielded 66 studies for final analysis. The findings reveal a nascent, rapidly accelerating field, with over 68% of publications from 2024 (representing a year-on-year growth of 150% from 2023 to 2024), and applications concentrated on front-end design processes like conceptual design and Computer-Aided Design (CAD) generation. The technological landscape is dominated by OpenAI’s GPT-4 variants. A persistent challenge identified is weak spatial and geometric reasoning, shifting the primary research bottleneck from traditional data scarcity to inherent model limitations. This, alongside reliability concerns, forms the main barrier to deeper integration into engineering workflows. A consensus on future directions points to the need for specialized datasets, multimodal inputs to ground models in engineering realities, and robust, engineering-specific benchmarks. This review concludes that LLMs are currently best positioned as powerful ‘co-pilots’ for engineers rather than autonomous designers, providing an evidence-based roadmap for researchers, practitioners, and educators.

This paper has been published in the “Rapid Prototyping Journal (2025) 31 (11): 320-332”

Purpose – The purpose of this study is to identify optimum laser settings and scanning strategies for titanium powder in a Trumpf Truprint 1000 to maximise density and minimise secondary roughness of printed parts.
Design/methodology/approach – Volumetric energy density (VED) was controlled via laser power and speed in the manufacturing of solid coupons. Relative density and surface finish (i.e. secondary roughness and its thickness) were measured. Optimum parameters were validated using porous lattices.
Findings – High density (99.6± 0.1%) and minimal secondary roughness (thickness 75 ±2 μm, Sa 2.9± 0.2 μm, Sz 30.1 ±2.8 μm) coupons were achieved using 108 W laser power, 889 mm/s laser speed, 55 μm hatch distance and 20 μm layer height to deliver VED 110 J/mm3 via a “double pass” of VED 55 J/mm3 in a conformal infill fashion. The validation lattices displayed improved strut line profiles and fewer internal voids, and open porosity closer to the design intent.
Research limitations/implications – The inherent variability present across machines and materials requires that the printing parameters have to be optimised for each pair. Initiatives to digitalise processes are needed to support our understanding of these variabilities. Our results contribute to the AM community efforts to compile experimental data that tabulates hardware performance in pursuit of that digitalisation.
Originality/value – The effectiveness of the “double pass” conformal style to increase density without compromising secondary roughness resides in the doubling of the VED experienced at the core of the component whilst leaving its edges unaffected.

This has been published in the “Engineering” on-line Journal, accessible on ScienceDirect.

Engineering | Journal | ScienceDirect.com by Elsevier

Abstract

Controlling machining deformations resulting from unbalanced stress fields inside structural components is a significant challenge in the manufacturing industry. Prediction of machining deformation fields is fundamental for deformation control and requires numerous iterations to optimize the machining process. Conventional prediction methods such as numerical analysis are tailored to a fixed geometry, making them time-consuming and inefficient for components with various geometries. In this study, a general data-driven model is proposed for predicting machining deformation fields in components with varying geometries and stress fields. This model is based on a geometry-oriented neural operator that incorporates global geometry information into the function space, modeling the relationship between the input function (stress fields) and the output function (deformation fields). Global geometric information is extracted using a graph neural network applied to a geometric graph and embedded into the input and output function space through an encoder-query framework. The proposed model achieved low root-mean-squared errors ranging from 0.001 to 0.016 mm, with maximum prediction errors between 0.003and 0.047 mm across different types of components, including beams and frames. The main contribution of this research is the significant advancement in the application of neural operators to the development of general models for predicting machining deformation. The underlying principles of the proposed model provide an important reference for wider applications related to the control of machining deformation in the context of digital and intelligent manufacturing.

Published in the “Metallurgical and Material Transactions A” – a journal focused on the latest research in physical metallurgy and materials science.

Abstract

The prediction of the formation of bulk metallic glasses (BMG) is challenging due to their complex thermodynamic and kinetic behavior. This study proposes the P_HSS parameter, a thermodynamic model which captures the concomitant effects of enthalpy of chemical mixing, mismatch entropy, and configurational entropy, as a predictor of glass-forming ability (GFA) in the Ti–Zr–Cu–Pd alloy system. Our results reveal a good linear correlation between P_HSS values and critical casting diameters, with more negative P_HSS values corresponding to higher GFA. In addition, the P_HSS values corresponding to a glass-forming range in Ti–Zr–Cu–Pd alloys were identified according to the fitted data supported by experimental verification. To do that, five (plus one extra for validation) compositions were manufactured and characterized using thermo-physical techniques. Crystallization kinetics and fragility of the supercooled liquids were studied to quantify the GFA of these alloys. We reveal that activation energies for glass transition and crystallization increase with GFA and correlate negatively with P_HSS values. A new relationship linking activation energy and thermostability was also explored. This integrated numerico-experimental approach establishes a direct link between thermodynamic parameters and GFA that contributes as a cost-effective tool for flexible exploration of new Ti–Zr–Cu–Pd BMGs, suitable for structural and biomedical applications.

This journal paper is published in MDPI Algorithms (Volume 18, Issue 8) and was also selected as the actual Issue cover too accessible via https://www.mdpi.com/1999-4893/18/8.

The paper belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes: 2nd Edition).

Abstract: Automated generative design is increasingly used across engineering disciplines to accelerate innovation and reduce costs. Generative design offers the prospect of simplifying manual design tasks by exploring the efficacy of solutions automatically. However, existing generative design frameworks rely heavily on expensive optimisation procedures and often produce customised solutions, lacking reusable generative rules that transfer across different problems. This work presents a bioinspired generative design algorithm utilizing the concept of evolutionary development (EvoDevo). This evolves a set of developmental rules that can be applied to different engineering problems to rapidly develop designs without the need to run full optimisation procedures. In this approach, an initial design is decomposed into simple entities called cells, which independently control their local growth over a development cycle. In biology, the growth of cells is governed by a gene regulatory network (GRN), but there is no single widely accepted model for this in artificial systems. The GRN responds to the state of the cell induced by external stimuli in its environment, which, in this application, is the loading regime on a bridge truss structure (but can be generalized to any engineering structure). Two GRN models are investigated: graph neural network (GNN) and graph-based Cartesian genetic programming (CGP) models. Both GRN models are evolved using a novel genetic search algorithm for parameter search, which can be re-used for other design problems. It is revealed that the CGP-based method produces results similar to those obtained using the GNN-based methods while offering more interpretability. In this work, it is shown that this EvoDevo approach is able to produce near-optimal truss structures via growth mechanisms such as moving vertices or changing edge features. The technique can be set up to provide design automation for a range of engineering design tasks.

Published in “Transactions on Additive Manufacturing Meets Medicine Trans. AMMM, 2025, Vol. 7, No 1, Article ID 2086”

https://doi.org/10.18416/AMMM.2025.25062086

Abstract: Bioengineered scaffolds with optimized osteoconductive and osteoinductive properties are highly desirable in bone tissue regeneration. Stochastic porous structures, resembling human trabecular bones, have gained increasing attention due to their suitability and superior performance in bone healing compared to regular porous architectures. In this study, we designed six trabecular-like porous scaffolds with varying porosity and surface area-to-volume ratios. The scaffolds were fabricated using pure titanium via selective laser melting, and their morphological characteristics were analyzed via micro computed tomography. Quasi-static compression testing was conducted to assess mechanical properties. The results showed that the as-built scaffolds exhibited a porosityrange67–71%, an average pore diameter ranging440–565 μm, a quasi-elastic gradient between2.6–3.5 GPa, and a yield strength of 44–58MPa. These values closely match those of the cortical bone, indicating potential for orthopeadic applications by mitigating stress shielding and enhancing implant longevity. Additionally, the permeability and wall shear stress were measured to predict cell growth performance in the scaffolds. The as-built models have a satisfactory permeability range of 6×10−9to 15×10−9m2, which is higher than that of cancellous bone, benefitting prospects for nutrient flow and by-product removal that encourage osteoblastic mineralization

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 “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’.