Publications
67 results found
Lee JJ, Mohammed AA, Pullen A, et al., 2023, Mechanical characterisation of 3D printed lightweight lattice structures with varying internal design alterations, Materials Today Communications, Vol: 36
The recent advances in the additive manufacturing (AM) have enhanced the development of light-weight, energy-absorbing structures in many aspects with precise, designed configurations of the internal structure. Given the range of potential materials many possible variations exist compared to the existing polymer-based foams. The advantages of rapid prototyping, enabled through AM, allows a streamlined process in obtaining a structure of desired mechanical behaviour. In this work, development, and design variation of a flexible lattice structure with core strut modification is demonstrated. The core struts are varied in terms of shape and density and are fabricated using vat photopolymerisation with Formlabs Flexible 80A resin and experimental methodology is outlined for the characterisation of the printed specimen under compressive loading. Mechanical characterisation under three different compression rates presents that the implementation of the core strut increases the elastic modulus of the lattice structure. The enhanced stiffness effects are further increased with the variations in shape, while the evaluation of the density variations shows significantly different deformation behaviour and strain rate sensitivity. The behaviour of each specimen types is discussed further in terms of their functional viability and potential applications where the design specific behaviour and small, lightweight form factor can be most effectively utilised.
Hong F, Lampret B, Myant C, et al., 2023, 5-axis multi-material 3D printing of curved electrical traces, Additive Manufacturing, Vol: 70
Prototyping three-dimensional (3D) printed electronics via material extrusion (MEX) has become popular in recent years with the increased availability of commercial conductive filaments. However, the current planar 3D printing method of layer upon layer construction shows clear challenges in extruding conductive traces for inclining surfaces. This inherent limitation of planar 3D printing restricts the design freedom of 3D printed electrically conductive objects with conductive filaments based on Polylactic Acid (PLA). To overcome this limitation of planar 3D printing, this paper describes a novel method of employing a multi-material 5-axis 3D printer to extrude conductive PLA in curved layers. The paper characterises changes in the resistivity of printed traces for angles of incline and curvatures using two commercial conductive PLA filaments. Conductive traces were printed via a custom-built desktop 5-axis 3D printer and a conventional multi-material MEX 3D printer. We found that 3D printing following a conformal approach can reduce the resistivity of the vertical conductive trace by more than 9 times. The paper concludes by successfully fabricating complex conductive patterns onto free-form doubly curved substrates.
Burge T, Jeffers J, Myant C, 2023, A computational design of experiments based method for evaluation of off-the-shelf total knee replacement implants, Computer Methods in Biomechanics and Biomedical Engineering, Vol: 26, Pages: 629-638, ISSN: 1025-5842
A methodology to explore the design space of off-the-shelf total knee replacement implant designs is outlined. Generic femur component and tibia plate designs were scaled to thousands of sizes and virtually fitted to 244 test subjects. Various implant designs and sizing requirements between genders and ethnicities were evaluated. 5 sizes optimised via the methodology produced a good global fit for most subjects. However, clinically significant over/underhang was present in 19% of subjects for tibia plates and 25% for femur components, reducing to 11/20% with 8 sizes. The analysis highlighted subtly better fit performance was obtained using sizes with unequal spacing.
Burge TA, Munford MJ, Kechagias S, et al., 2023, Automating the customization of stiffness-matched knee implants using machine learning techniques, The International Journal of Advanced Manufacturing Technology, ISSN: 0268-3768
In knee arthroplasty, implants are used to replace the articulating surfaces of the tibia and femur bones, with most constituting of solid metallic components. Consequentially, biomechanical stresses and strains are no longer adequately distributed at the joint post-surgery, preventing beneficial bone remodeling. To mitigate this studies have explored additively manufacturing implants with porous lattice structures to match the mechanical properties of bone. Authors have also outlined how such structures can be designed using computed tomography data to simulate the stiffness of individuals’ bones. Such methods however currently require substantial manual work by trained professionals to process the image files, extract the density information, and design lattice structures. This study proposes what is believed to be the first fully automatic pipeline capable of producing tibial trays with compliant structures customized specifically for individuals’ bones, achieved using machine learning methods. The novel process, combining classification, object detection, and segmentation machine learning models, used to facilitate the automated workflow, is outlined. The efficaciousness of the pipeline is then demonstrated by testing it using clinical computed tomography data and comparing the results with those obtained manually. As a proof of concept, prototype designs generated by the pipeline with differing degrees of complexity, up to and including mapping stiffness variation in 3D through the shaft of the tibia, were also fabricated.
Mohammed AA, Miao J, Ragaisyte I, et al., 2023, 3D printed superparamagnetic stimuli-responsive starfish-shaped hydrogels, Heliyon, Vol: 9, Pages: 1-13, ISSN: 2405-8440
Magnetic-stimuli responsive hydrogels are quickly becoming a promising class of materials across numerous fields, including biomedical devices, soft robotic actuators, and wearable electronics. Hydrogels are commonly fabricated by conventional methods that limit the potential for complex architectures normally required for rapidly changing custom configurations. Rapid prototyping using 3D printing provides a solution for this. Previous work has shown successful extrusion 3D printing of magnetic hydrogels; however, extrusion-based printing is limited by nozzle resolution and ink viscosity. VAT photopolymerization offers a higher control over resolution and build-architecture. Liquid photo-resins with magnetic nanocomposites normally suffer from nanoparticle agglomeration due to local magnetic fields. In this work, we develop an optimised method for homogenously infusing up to 2 wt % superparamagnetic iron oxide nanoparticles (SPIONs) with a 10 nm diameter into a photo-resin composed of water, acrylamide and PEGDA, with improved nanoparticle homogeneity and reduced agglomeration during printing. The 3D printed starfish hydrogels exhibited high mechanical stability and robust mechanical properties with a maximum Youngs modulus of 1.8 MPa and limited shape deformation of 10% when swollen. Each individual arm of the starfish could be magnetically actuated when a remote magnetic field is applied. The starfish could grab onto a magnet with all arms when a central magnetic field was applied. Ultimately, these hydrogels retained their shape post-printing and returned to their original formation once the magnetic field had been removed. These hydrogels can be used across a wide range of applications, including soft robotics and magnetically stimulated actuators.
Burge T, Jeffers J, Myant C, 2023, Applying machine learning methods to enable automatic customisation of knee replacement implants from CT data, Scientific Reports, Vol: 13, Pages: 1-9, ISSN: 2045-2322
The aim of this study was to develop an automated pipeline capable of designing custom total knee replacement implants from CT scans. The developed pipeline firstly utilised a series of machine learning methods including classification, object detection, and image segmentation models, to extract geometrical information from inputted DICOM files. Statistical shape models then used the information to create femur and tibia 3D surface model predictions which were ultimately used by computer aided design scripts to generate customised implant designs. The developed pipeline was trained and tested using CT scan images, along with segmented 3D models, obtained for 98 Korean Asian subjects. The performance of the pipeline was tested computationally by virtually fitting outputted implant designs with ‘ground truth’ 3D models for each test subject’s bones. This demonstrated the pipeline was capable of repeatably producing highly accurate designs, and its performance was not impacted by subject sex, height, age, or knee side. In conclusion, a robust, accurate and automatic, CT-based total knee replacement customisation pipeline was shown to be feasible and could afford significant time and cost advantages over conventional methods. The pipeline framework could also be adapted to enable customisation of other medical implants.
Vladescu S-C, Agurto MG, Myant C, et al., 2023, Protein-induced delubrication: How plant-based and dairy proteins affect mouthfeel, FOOD HYDROCOLLOIDS, Vol: 134, ISSN: 0268-005X
Willis S, Waheed U, Coward T, et al., 2022, An automated design pipeline for transparent facial orthoses: A clinical study., J Prosthet Dent
STATEMENT OF PROBLEM: Transparent facial orthoses (TFOs) are commonly used for the treatment of craniomaxillofacial trauma and burns to prevent hypertrophic and keloid scarring. A TFO is typically customized to the patient's facial contours and relies on a precise fit to ensure good rehabilitative performance. A smart method of TFO design and manufacture is needed which does not require an experienced prosthetist, allowing for rapidly produced, well-fitting TFOs. Whether the rapid application reduces the final level of patient scarring is unclear. PURPOSE: The purpose of this clinical study was to determine whether a scalable, automated design-through-manufacture pipeline for patient specific TFO fabrication would be successful. MATERIAL AND METHODS: The automated pipeline received a 3-dimensional (3D) facial scan captured from a depth sensitive mobile phone camera. The scan was cleaned, aligned, and fit to a template mesh, with a known connectivity. The resultant fitted scan was passed into an automated design pipeline, outputting a 3D printable model of a custom TFO. The TFOs were fabricated with 3D printing and were both physically and digitally evaluated to test the fidelity of a digital fit testing system. RESULTS: A total of 10 individuals were scanned with 5 different scanning technologies (STs). All scans were passed through an automated fitting pipeline and categorized into 2 groups. Each ST was digitally fitted to a ground truth scan. In this manner, a Euclidean distance map was built to the actual facial geometry for each scan. Heatmaps of 3D Euclidean distances were made for all participant faces. CONCLUSIONS: The ability to automatically design and manufacture a custom fitted TFO using commercially available 3D scanning and 3D printing technology was successfully demonstrated. After considering equipment size and operational personnel requirements, vat polymerization (VP) technology was found to be the most promising route to TFO manufacture.
Barrak FN, Li S, Mohammed AA, et al., 2022, Anti-inflammatory properties of S53P4 bioactive glass implant material., Journal of Dentistry, Vol: 127, ISSN: 0300-5712
OBJECTIVES: To assess whether the dissolution products of S53P4 bioactive glass (BG) affect cellular response of macrophages and clinically relevant peri-implant cell populations to dental implant particles in vitro. Cells chosen were human gingival fibroblasts (HGFs), osteoblasts and bone marrow derived stromal cells (HBMSCs). METHODS: Melt-derived S53P4 bioactive glass were prepared. HGFs, Saos-2 human osteoblastic cell line, HBMSCs and macrophages, derived from THP-1 human monocytic cell line, were cultured in the presence of particles from commercially pure titanium (Ti-CP4), grade 5 titanium alloy (Ti-6Al-4V), titanium-zirconium alloy (Ti-15Zr) or zirconia (Zr) (with respective diameters of 34.1 ± 3.8, 33.3 ± 4.4, 97.8 ± 8.2 and 71.3 ± 6.1 µm) with or without S53P4 dissolution products (conditioned media contained 327.30 ± 2.01 ppm Ca, 51.34 ± 0.41 ppm P and 61.48 ± 1.17 ppm Si, pH 8.01 ± 0.21). Inflammatory and macrophage polarisation markers including TNF-ɑ, IL-1, IL-6 and CD206 were quantified using enzyme-linked immunosorbent assay (ELISA). RESULTS: The presence of Ti-6Al-4V implant particles significantly induced the expression of pro-inflammatory markers in all tested cell types. S53P4 BG dissolution products regressed the particle induced up-regulation of pro-inflammatory markers and, appeared to suppress M1 macrophage polarisation. CONCLUSIONS: Implant particles, Ti-6Al-4V in particular, resulted in significant inflammatory responses from cells. S53P4 BG may possess anti-inflammatory properties and potentially mediate macrophage polarisation behaviour. CLINICAL SIGNIFICANCE: The findings highlight that the use and benefits of BG is a promising field of study. Authors believe more collective efforts are required to fully understand the reliability, efficiency and exact mechanisms of action of BG in the search for new generation of treatme
Burge TA, Jeffers JRT, Myant CW, 2022, Performance and Sensitivity Analysis of an Automated X-Ray Based Total Knee Replacement Mass-Customization Pipeline, JOURNAL OF MEDICAL DEVICES-TRANSACTIONS OF THE ASME, Vol: 16, ISSN: 1932-6181
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Burge T, Jones G, Jordan C, et al., 2022, A computational tool for automatic selection of total knee replacementimplant size using x-ray images, Frontiers in Bioengineering and Biotechnology, Vol: 10, Pages: 1-11, ISSN: 2296-4185
Purpose: The aim of this study was to outline a fully automatic tool capable of reliably predicting the most suitable total kneereplacement implant sizes for patients, using bi-planar X-ray images. By eliminating the need for manual templating or guidingsoftware tools via the adoption of convolutional neural networks, time and resource requirements for pre-operative assessmentand surgery could be reduced, the risk of human error minimized, and patients could see improved outcomes.Methods: The tool utilizes a machine learning-based 2D – 3D pipeline to generate accurate predictions of subjects’ distal femur andproximal tibia bones from X-ray images. It then virtually fits different implant models and sizes to the 3D predictions, calculatesthe implant to bone root-mean-squared error and maximum over/under hang for each, and advises the best option for thepatient. The tool was tested on 78, predominantly White subjects (45 female/33 male), using generic femur component and tibiaplate designs scaled to sizes obtained for five commercially available products. The predictions were then compared to the groundtruth best options, determined using subjects’ MRI data.Results: The tool achieved average femur component size prediction accuracies across the five implant models of 77.95% in termsof global fit (root-mean-squared error), and 71.79% for minimizing over/underhang. These increased to 99.74% and 99.49% with ±1size permitted. For tibia plates, the average prediction accuracies were 80.51% and 72.82% respectively. These increased to99.74% and 98.98% for ±1 size. Better prediction accuracies were obtained for implant models with fewer size options, howeversuch models more frequently resulted in a poor fit.Conclusion: A fully automatic tool was developed and found to enable higher prediction accuracies than generally reported formanual templating techniques, as well as similar computational methods.
Hong F, Tendera L, Myant C, et al., 2022, Vacuum-Formed 3D Printed Electronics: Fabrication of Thin, Rigid and Free-Form Interactive Surfaces, SN Computer Science, Vol: 3, ISSN: 2662-995X
Vacuum-forming is a common manufacturing technique for constructing thin plastic shell products by pressing heated plastic sheets onto a mold using atmospheric pressure. Vacuum-forming is ubiquitous in packaging and casing products in the industry, spanning fast moving consumer goods to connected devices. Integrating advanced functionality, which may include sensing, computation and communication, within thin structures is desirable for various next-generation interactive devices. Hybrid additive manufacturing techniques like thermoforming are becoming popular for prototyping freeform surfaces owing to their design flexibility, speed and cost-effectiveness. This paper presents a new hybrid method for constructing thin, rigid and free-form interconnected surfaces via fused deposition modelling (FDM) 3D printing and vacuum-forming that builds on recent advances in thermoforming circuits. 3D printing the sheet material allows for the embedding of conductive traces within thin layers of the substrate, which can be vacuum-formed but remain conductive and insulated. This is an unexplored fabrication technique within the context of designing and manufacturing connected things. In addition to explaining the method, this paper characterizes the behavior of vacuum-formed 3D printed sheets, analyses the electrical performance of printed traces after vacuum-forming, and showcases a range of sample artefacts constructed using the technique. In addition, the paper describes a new design interface for designing conformal interconnects that allows designers to draw conductive patterns in 3D and export pre-distorted sheet models ready to be printed.
Zhou Y, Myant C, Stewart R, 2022, Multifunctional and stretchable graphene/textile composite sensor for human motion monitoring, Journal of Applied Polymer Science, Vol: 139, ISSN: 0021-8995
Sensors based on electronic textiles (e-textiles) have become increasingly prominent in the field of biomechanical monitoring technology due to multiple properties such as being lightweight, flexible, and comfortable, with increasing potential in incorporating into long-term monitoring devices. Previous research has been conducted into textile strain sensors based on graphene for human motion monitoring, however most graphene e-textile strain sensors exhibit poor sensitivity and stretchability. To our knowledge, no previous research has looked at knitted graphene-based fabrics in regards to the fabric composition of the substrate. In this paper, we propose a graphene/fabric composite sensor using a cost-effective dip coating method of an acrylic/Spandex knit fabric, and further explores its mechanical, electrical, and sensing properties. The developed graphene/textile composite sensor has a wide sensing range (up to 344%) and exhibits a good sensitivity with a high gauge factor of up to 16. As a wearable sensor, our sensing fabric can detect both large and subtle human motions and is able to distinguish between various ranges of joint movements, demonstrating its ability to function as a human motion monitoring system. Our sensor further exhibits the ability to be used as a supercapacitor or capacitive pressure sensor.
Hong F, Hodges S, Myant C, et al., 2022, Open5x: Accessible 5-axis 3D printing and conformal slicing, Conference on Human Factors in Computing Systems - Proceedings
The common layer-by-layer deposition of regular, 3-axis 3D printing simplifies both the fabrication process and the 3D printer's mechanical design. However, the resulting 3D printed objects have some unfavourable characteristics including visible layers, uneven structural strength and support material. To overcome these, researchers have employed robotic arms and multi-axis CNCs to deposit materials in conformal layers. Conformal deposition improves the quality of the 3D printed parts through support-less printing and curved layer deposition. However, such multi-axis 3D printing is inaccessible to many individuals due to high costs and technical complexities. Furthermore, the limited GUI support for conformal slicers creates an additional barrier for users. To open multi-axis 3D printing up to more makers and researchers, we present a cheap and accessible way to upgrade a regular 3D printer to 5 axes. We have also developed a GUI-based conformal slicer, integrated within a popular CAD package. Together, these deliver an accessible workflow for designing, simulating and creating conformally-printed 3D models.
Bahshwan M, Gee M, Nunn J, et al., 2022, In situ observation of anisotropic tribological contact evolution in 316L steel formed by selective laser melting, Wear, Vol: 490-491, Pages: 1-12, ISSN: 0043-1648
A consensus on the tribological performance of components by additive-versus conventional manufacturing has not been achieved; mainly because the tribological test set-ups thus far were not suited for investigating the underlying microstructure's influence on the tribological properties. As a result, utilization of additive manufacturing techniques, such as selective laser melting (SLM), for tribological applications remains questionable. Here, we investigate the anisotropic tribological response of SLM 316L stainless steel via in situ SEM reciprocating micro-scratch testing to highlight the microstructure's role. As-built 316L SLM specimens were compared against annealed wire-drawn 316L. We found that: (i) microgeometric conformity was the main driver for achieving steady-state friction, (ii) the anisotropic friction of the additively manufactured components is limited to the break-in and is caused by the lack of conformity, (iii) the cohesive bonds, whose strength is proportional to frictional forces, are stronger in the additively manufactured specimens likely due to the dislocation-dense, cellular structures, (iv) low Taylor-factor grains with large dimension stimulate microcutting in the form of long, thin sheets with serrated edges. These findings uncover some microstructurally driven tribological complexities when comparing additive to conventional manufacturing.
Burge TA, Jeffers JRT, Myant CW, 2022, Development of an automated mass-customization pipeline for knee replacement surgery using biplanar X-Rays, Journal of Mechanical Design, Vol: 144, Pages: 1-11, ISSN: 1050-0472
For standard “off-the-shelf” knee replacement procedures, surgeons use X-ray images to aid implant selection from a limited number of models and sizes. This can lead to complications and the need for implant revision due to poor implant fit. Customized solutions have been shown to improve results but require increased preoperative assessment (Computed Tomography or Magnetic Resonance Imaging), longer lead times, and higher costs which have prevented widespread adoption. To attain the benefits of custom implants, whilst avoiding the limitations of currently available solutions, a fully automated mass-customization pipeline, capable of developing customized implant designs for fabrication via additive manufacturing from calibrated X-rays, is proposed. The proof-of-concept pipeline uses convolutional neural networks to extract information from biplanar X-ray images, point depth, and statistical shape models to reconstruct the anatomy, and application programming interface scripts to generate various customized implant designs. The pipeline was trained using data from the Korea Institute of Science and Technology Information. Thirty subjects were used to test the accuracy of the anatomical reconstruction, ten from this data set, and a further 20 independent subjects obtained from the Osteoarthritis Initiative. An average root-mean-squared error of 1.00 mm was found for the femur test cases and 1.07 mm for the tibia. Three-dimensional (3D) distance maps of the output components demonstrated these results corresponded to well-fitting components, verifying automatic customization of knee replacement implants is feasible from 2D medical imaging.
Zhou Y, Zhang C, Myant C, et al., 2022, Knitted Graphene Supercapacitor and Pressure-Sensing Fabric †
This research utilizes a simple and effective dip coating/ultrasonication method to prepare porous graphene-coated sensing fabrics made with commercially produced acrylic/spandex yarn with multifunctional performance. We examine the electrochemical performance of graphene-coated fabrics and explore their potential in applications involving pressure sensors. The results show that our graphene-coated fabric demonstrates a maximum specific capacitance value of 17.4 F/g. When applied as a pressure sensor, the capacitance change rate of our sensor increases linearly with the increase in pressure applied to the fabrics. Our sensor also shows a fast response in a pressure loading–unloading test, which indicates an outstanding sensing property and shows promising capabilities as a supercapacitor.
Yang Q, Myant C, 2022, ADAPTIVE SLICING BASED ON ACCURATELY ASSESSING THE VARIATIONS OF THE MODEL’S GEOMETRY FOR STAIRCASE EFFECT AND DIMENSIONAL DEVIATION MITIGATION
Current additive manufacturing (AM) technologies commonly cause geometric inaccuracies (e.g. staircase effect and dimensional deviation) in the printed parts. These geometric inaccuracies significantly limit the industry potential of AM as they are the primary cause of reduced energy efficiency (e.g. turbine blade and wind tunnel testing models) and failed implants (e.g. hip and cranial implants and dental prostheses). To improve the geometric accuracy while balancing the build time in AM, previous studies have proposed adaptive slicing. It works by varying decreasing layer thickness in sections of high curvature. However, current adaptive slicing methodologies have all faced difficulties adjusting layer thickness precisely according to the variations in the model’s geometry, limiting the geometric accuracy improvement. This paper tackles this difficulty by assessing the geometric variations of the model by directly evaluating the ratio of the volume of each sliced layer’s geometric deviation to the volume of its corresponding region in the digital (triangulated surface) model. This assessment allows all the topological information of the corresponding region to be considered in measuring the geometric deviation (volume) between each sliced layer and its corresponding region, and therefore, it can accurately indicate the geometric variations of the model. Through this precise indication to modify each layer thickness, this paper has developed an adaptive slicing methodology that can better optimise the trade-off between build-time reduction and geometric accuracy improvement than other slicing methodologies. For example, it can reduce the build time by nearly half compared to other existing slicing methodologies by assuming a similar degree of the geometric accuracy of printed parts. This slicing was evaluated using six different test models and compared with three current slicing methodologies (voxelisation-based, cusp height-based, and uniform slicing)
Butt H, Nissim L, Gao L, et al., 2021, Transient mixed lubrication model of the human knee implant, Biosurface and Biotribology, Vol: 7, Pages: 206-218
The human knee implant is computationally modelled in the mixed lubrication regime to investigate the tribological performance of the implant. This model includes the complex geometry of the implant components, unlike elliptical contact models that approximate this geometry. Film thickness and pressure results are presented for an ISO gait cycle to determine the lubrication regime present within the implant during its operation. It was found that it was possible for the lubrication regime to span between elastohydrodynamic, mixed and boundary lubrication depending on the operating conditions of the implant. It was observed that the tribological conditions present in one condyle were not necessarily representative of the other. Multiple points of contact were found within the same condyle, which cannot be computed by the elliptical contact solvers. This model can be used to balance forces in all directions, instead of only the normal loads, as often done in elliptical contact models. This work is an initial step towards understanding the role of the complex geometry in the tribological characteristics of the human knee implant when operating in physiological conditions.
Nissim L, Butt H, Gao L, et al., 2021, Role of protein concentration on transient film thickness in synovial fluid lubricated joints, Biotribology, Vol: 28, Pages: 1-14, ISSN: 2352-5738
A computational model of protein aggregation lubrication has been developed for predicting transient behaviour in lubricated prosthetics. The model uses an advection-diffusion equation to simulate protein transport in order to map concentration changes throughout the contact and inlet zones of an elasto-hydrodynamic contact. Concentration increases lead to exponential increase in fluid viscosity giving rise to lubricating film thicknesses an order of magnitude larger than would be expected using conventional elasto-hydrodynamic theory. The model parameters have been calibrated such that good agreement in transient film thickness is achieved with observed experimental results.KeywordsProtein aggregation lubrication; Elasto-hydrodynamic lubrication; Prostheses
Kalossaka LM, Mohammed AA, Sena G, et al., 2021, 3D printing nanocomposite hydrogels with lattice vascular networks using stereolithography, JOURNAL OF MATERIALS RESEARCH, Vol: 36, Pages: 4249-4261, ISSN: 0884-2914
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Li S, Tan Y, Willis S, et al., 2021, Toward mass customization through additive manufacturing: an automated design pipeline for respiratory protective equipment validated against 205 faces, International Journal of Bioprinting, Vol: 7, ISSN: 2424-7723
Respiratory protective equipment (RPE) is traditionally designed through anthropometric sizing to enable mass production. However, this can lead to long-standing problems of low-compliance, severe skin trauma, and higher fit test failure rates among certain demographic groups, particularly females and non-white ethnic groups. Additive manufacturing could be a viable solution to produce custom-fitted RPE, but the manual design process is time-consuming, cost-prohibitive and unscalable for mass customization. This paper proposes an automated design pipeline which generates the computer-aided design models of custom-fit RPE from unprocessed three-dimensional (3D) facial scans. The pipeline successfully processed 197 of 205 facial scans with <2 min/scan. The average and maximum geometric error of the mask were 0.62 mm and 2.03 mm, respectively. No statistically significant differences in mask fit were found between male and female, Asian and White, White and Others, Healthy and Overweight, Overweight and Obese, Middle age, and Senior groups.
Kalossaka LM, Sena G, Barter LMC, et al., 2021, Review: 3D printing hydrogels for the fabrication of soilless cultivation substrates, Applied Materials Today, Vol: 24, Pages: 1-16, ISSN: 2352-9407
The use of hydrogels in academic research is fast evolving, and becoming more relevant to real life applications across varying fields. Additive Manufacturing (AM) has paved the way towards manufacturing hydrogel substrates with tailored properties which allow for new functionalities and applications. In this review, we introduce the idea of fabricating hydrogels as bioreceptive structures to be used as soilless cultivation substrates. AM is suggested as the fabrication process to achieve structures with features similar to soil. To evaluate this, we first review hydrogel fabrication processes, highlighting their key differences in terms of resolution, printing speed and build volume. Thus, we illustrate the examples from the literature where hydrogels were 3D printed with microorganisms such as algae. Finally, the challenges and future perspectives of printing soilless cultivation substrates are explored.
Li S, Waheed U, Bahshwan M, et al., 2021, A scalable mass customisation design process for 3D-printed respirator mask to combat COVID-19, Rapid Prototyping Journal, Vol: 27, Pages: 1302-1317, ISSN: 1355-2546
PurposeA three-dimensional (3D) printed custom-fit respirator mask has been proposed as a promising solution to alleviate mask-related injuries and supply shortages during COVID-19. However, creating a custom-fit computer-aided design (CAD) model for each mask is currently a manual process and thereby not scalable for a pandemic crisis. This paper aims to develop a novel design process to reduce overall design cost and time, thus enabling the mass customisation of 3D printed respirator masks.Design/methodology/approachFour data acquisition methods were used to collect 3D facial data from five volunteers. Geometric accuracy, equipment cost and acquisition time of each method were evaluated to identify the most suitable acquisition method for a pandemic crisis. Subsequently, a novel three-step design process was developed and scripted to generate respirator mask CAD models for each volunteer. Computational time was evaluated and geometric accuracy of the masks was evaluated via one-sided Hausdorff distance.FindingsRespirator masks were successfully generated from all meshes, taking <2 min/mask for meshes of 50,000∼100,000 vertices and <4 min for meshes of ∼500,000 vertices. The average geometric accuracy of the mask ranged from 0.3 mm to 1.35 mm, depending on the acquisition method. The average geometric accuracy of mesh obtained from different acquisition methods ranged from 0.56 mm to 1.35 mm. A smartphone with a depth sensor was found to be the most appropriate acquisition method.Originality/valueA novel and scalable mass customisation design process was presented, which can automatically generate CAD models of custom-fit respirator masks in a few minutes from a raw 3D facial mesh. Four acquisition methods, including the use of a statistical shape model, a smartphone with a depth sensor, a light stage and a structured light scanner were compared; one method was recommended for use in a pandemic crisis consider
Xu Y, Cartwright B, Advincula L, et al., 2021, Generalised scaling law for soft contact tribology: Influence of load and asymmetric surface deformation, TRIBOLOGY INTERNATIONAL, Vol: 163, ISSN: 0301-679X
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Whitehouse S, Myant C, Cann PM, et al., 2021, Fluorescent imaging of razor cartridge/skin lubrication, SURFACE TOPOGRAPHY-METROLOGY AND PROPERTIES, Vol: 9, ISSN: 2051-672X
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Hong F, Myant C, Boyle D, 2021, Thermoformed Circuit Boards: Fabrication of highly conductive freeform 3D printed circuit boards with heat bending, CHI Conference on Human Factors in Computing Systems, Pages: 1-10
Fabricating 3D printed electronics using desktop printers has become moreaccessible with recent developments in conductive thermoplastic filaments.Because of their high resistance and difficulties in printing traces invertical directions, most applications are restricted to capacitive sensing. Inthis paper, we introduce Thermoformed Circuit Board (TCB), a novel approachthat employs the thermoformability of the 3D printed plastics to constructvarious double-sided, rigid and highly conductive freeform circuit boards thatcan withstand high current applications through copper electroplating. Toillustrate the capability of the TCB, we showcase a range of examples withvarious shapes, electrical characteristics and interaction mechanisms. We alsodemonstrate a new design tool extension to an existing CAD environment thatallows users to parametrically draw the substrate and conductive trace, andexport 3D printable files. TCB is an inexpensive and highly accessiblefabrication technique intended to broaden HCI researcher participation.
Vlădescu S-C, Bozorgi S, Hu S, et al., 2021, Effects of beverage carbonation on lubrication mechanisms and mouthfeel, Journal of Colloid and Interface Science, Vol: 586, Pages: 142-151, ISSN: 0021-9797
The perception of carbonation is an important factor in beverage consumption which must be understood in order to develop healthier products. Herein, we study the effects of carbonated water on oral lubrication mechanisms involved in beverage mouthfeel and hence taste perception. Friction was measured in a compliant PDMS-glass contact simulating the tongue-palate interface (under representative speeds and loads), while fluorescence microscopy was used to visualise both the flow of liquid and oral mucosal pellicle coverage.When carbonated water is entrained into the contact, CO2 cavities form at the inlet, which limit flow and thus reduce the hydrodynamic pressure. Under mixed lubrication conditions, when the fluid film thickness is comparable to the surface roughness, this pressure reduction results in significant increases in friction (>300% greater than under non-carbonated water conditions). Carbonated water is also shown to be more effective than non-carbonated water at debonding the highly lubricious, oral mucosal pellicle, which again results in a significant increase in friction. Both these transient mechanisms of starvation and salivary pellicle removal will modulate the flow of tastants to taste buds and are suggested to be important in the experience of taste and refreshment. For example this may be one reason why flat colas taste sweeter.
Bin M, Cheung PYK, Crisostomi E, et al., 2021, Post-lockdown abatement of COVID-19 by fast periodic switching, PLoS Computational Biology, Vol: 17, Pages: 1-34, ISSN: 1553-734X
COVID-19 abatement strategies have risks and uncertainties which could lead to repeating waves of infection. We show—as proof of concept grounded on rigorous mathematical evidence—that periodic, high-frequency alternation of into, and out-of, lockdown effectively mitigates second-wave effects, while allowing continued, albeit reduced, economic activity. Periodicity confers (i) predictability, which is essential for economic sustainability, and (ii) robustness, since lockdown periods are not activated by uncertain measurements over short time scales. In turn—while not eliminating the virus—this fast switching policy is sustainable over time, and it mitigates the infection until a vaccine or treatment becomes available, while alleviating the social costs associated with long lockdowns. Typically, the policy might be in the form of 1-day of work followed by 6-days of lockdown every week (or perhaps 2 days working, 5 days off) and it can be modified at a slow-rate based on measurements filtered over longer time scales. Our results highlight the potential efficacy of high frequency switching interventions in post lockdown mitigation. All code is available on Github at https://github.com/V4p1d/FPSP_Covid19. A software tool has also been developed so that interested parties can explore the proof-of-concept system.
Hong F, Myant C, Boyle DE, 2021, Thermoformed Circuit Boards: Fabrication of highly conductive freeform 3D printed circuit boards with heat bending., Publisher: ACM, Pages: 669:1-669:1
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