A parametric design framework for the mass customization of bicycle helmet

Cluster analysis of 3D head shapes plays a crucial role in the mass customization design of products related to the head. Head shapes exhibit variations across different races, and designing helmets exclusively for Chinese individuals cannot solely rely on or reference foreign head models. Currently, research on cluster analysis of Chinese head shapes is limited, especially concerning shape variances. To address this, we developed an improved k-medoids algorithm and integrated Cluster Validity Index as an assessment metric. This enabled us to cluster 339 Chinese young males aged 18 to 30 into 7 groups based on their head shapes. By comparing our improved algorithm to the traditional k-medoids method, we affirmed its superiority in achieving higher sample participation rates and reducing inter-cluster sample disparities. To simplify the helmet design and editing process, and to improve the efficiency of mass customization, we have developed a parametric modeling program for bicycle helmets based on the head shape clustering results. Results from the Helmet Fit Index and stress simulation analysis demonstrate that our approach significantly enhances helmet fit and wearer comfort.

A parametric additive hazard model for time-to-event analysis

BACKGROUND

In recent years, the use of non- and semi-parametric models which estimate hazard ratios for analysing time-to-event outcomes is continuously criticized in terms of interpretation, technical implementation, and flexibility. Hazard ratios in particular are critically discussed for their misleading interpretation as relative risks and their non-collapsibility. Additive hazard models do not have these drawbacks but are rarely used because they assume a non- or semi-parametric additive hazard which renders computation and interpretation complicated.

METHODS

As a remedy, we propose a new parametric additive hazard model that allows results to be reported on the original time rather than on the hazard scale. Being an essentially parametric model, survival, hazard and probability density functions are directly available. Parameter estimation is straightforward by maximizing the log-likelihood function.

RESULTS

Applying the model to different parametric distributions in a simulation study and in an exemplary application using data from a study investigating medical care to lung cancer patients, we show that the approach works well in practice.

CONCLUSIONS

Our proposed parametric additive hazard model can serve as a powerful tool to analyze time-to-event outcomes due to its simple interpretation, flexibility and facilitated parameter estimation.

Parametric investigation of ultrashort pulsed laser surface texturing on aluminium alloy 7075 for hydrophobicity enhancement

Hydrophobicity plays a pivotal role in mitigating surface fouling, corrosion, and icing in critical marine and aerospace environments. By employing ultrafast laser texturing, the characteristic properties of a material's surface can be modified. This work investigates the potential of an advanced ultrafast laser texturing manufacturing process to enhance the hydrophobicity of aluminium alloy 7075. The surface properties were characterized using goniometry, 3D profilometry, SEM, and XPS analysis. The findings from this study show that the laser process parameters play a crucial role in the manufacturing of the required surface structures. Numerical optimization with response surface optimization was conducted to maximize the contact angle on these surfaces. The maximum water contact angle achieved was 142º, with an average height roughness (Sa) of 0.87 ± 0.075 µm, maximum height roughness (Sz) of 19.4 ± 2.12 µm, and texture aspect ratio of 0.042. This sample was manufactured with the process parameters of 3W laser power, 0.08 mm hatch distance, and a 3 mm/s scan speed. This study highlights the importance of laser process parameters in the manufacturing of the required surface structures and presents a parametric modeling approach that can be used to optimize the laser process parameters to obtain a specific surface morphology and hydrophobicity.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00170-024-12971-8.

Fast NURBS-based parametric modeling of human calves with high-accuracy for personalized design of graduated compression stockings

BACKGROUND AND OBJECTIVES

Accurate human body models are increasingly demanded by high-quality human-centered ergonomic applications, especially the design and manufacturing of compressive functional apparels. However, existing parametric models in related works are not capable to accurately describe detailed local shape features of human.

METHODS

In this work, a high-accuracy parametric modeling approach for human limb was proposed. 3D Scans of human calves were studied. Key data points of the scanned human calves were identified according to human anatomy, forming a quasi-triangular mesh of feature points. Then, non-uniform rational B-splines (NURBS) method was implemented. Control points were calculated from the key data points, with which the human calf shapes can be reconstructed by the smooth NURBS surface, giving rise to a new parametric model of human calves. Error between the scanned and reconstructed calf shapes were analyzed to verify the effectiveness of this model.

RESULTS

Error analysis showed that, this proposed method delivers a high-efficiency and high-accuracy parametric shape modeling approach with averaged error observed as only 0.37% for all the 260 subjects, much less compared to previous relative works (around 5%). For tentative application, customized medical compression stockings were designed based on this model and proved as valid to exert desired gradient compression on the according calf mannequin.

CONCLUSIONS

By introducing the non-uniform rational B-splines method, a parametric model capable of characterizing human limbs with high-accuracy was proposed. Using very small amount of data, this model is expected to highly facilitate remote customized design and provide 3D shape references for design of compressive garments. Moreover, the proposed methods can inspire developments of other mixed modeling methods for high-accuracy applications.

Lightweight lattice-based skeleton of the sponge Euplectella aspergillum: On the multifunctional design

The glass sponge, Euplectella aspergillum, possesses a lightweight, silica spicule-based, cylindrical lattice-like skeleton, representing an excellent model system for bioinspired lattices. Previous analysis suggested that the E. aspergillum's skeletal lattice exhibits improved buckling resistance and suppressed vortex shedding. How the sponge's skeletal lattice with diagonally-oriented reinforcing bundle of fused spicules and the ridge system behaves under different loading conditions and achieves dual mechanical and fluidic transport performance requires further investigation. Here, we first quantified the structural descriptors such as length and thickness of the bundles of fused spicules and hole opening diameter of the sponge skeletons with and without the soft tissue covered. Secondly, parametric modeling and simulations of the sponge lattice in comparison with other bioinspired designs under different loading conditions were implemented to obtain the structure-mechanical property relationship. Our results reveal that the double-diagonal reinforcements of the E. aspergillum's lattices show i) tendency to maximize the torsional rigidity in comparison to longitudinal and radial modulus and flexural rigidity, and ii) independency of mechanical properties on the diagonal spacing, leaving freedom to control the hole-opening position for the sponge's fluid transport. Furthermore, our coupled fluid-mechanical simulations suggest that the ridge system spiraling the cylindrical lattice simultaneously improves the radial stiffness and fluid permeability. Finally, we discuss the general mechanical strategies and design flexibility in the sponge's skeletal lattice. Our work provides understanding of the mechanical and functional trade-offs in E. aspergillum's skeletal lattice which may shed light on the design of lightweight tubular lattices.

Parametric modeling of steady-state gradient coil vibration: Resonance dynamics under variations in cylinder geometry

Gradient coil (GC) vibration is the root cause of many problems in MRI adversely affecting scanner performance, image quality, and acoustic noise levels. A critical issue is that GC vibration will be significantly increased close to any GC mechanical resonances. It is well known that altering the dimensions of a GC fundamentally affects the mechanical resonances excited by the GC windings. The precise nature of the effects (i.e., how the resonances are affected) is however not well understood. The purpose of the present paper is to study how the mechanical resonances excited by closed whole-body Z-gradient coils are affected by variations in cylinder geometry. A mathematical Z-gradient coil vibration model recently developed and validated by the authors is used to theoretically study the resonance dynamics under variation(s) in cylinder: (i) length, (ii) mean radius, and (iii) radial thickness. The forced-vibration response to Lorentz-force excitation is in each case analyzed in terms of the frequency response of the GC cylinder's displacement. In cases (i) and (ii), the qualitative dynamics are simple: reducing the cylinder length and/or mean radius causes all mechanical resonances to shift to higher frequencies. In case (iii), the qualitative dynamics are much more complicated with different resonances shifting in different directions and additional dependencies on the cylinder length. The more detailed dynamics are intricate owing to the fact that resonances shift at comparatively different rates and this leads to several novel and theoretically interesting predicted effects. Knowledge of these effects advance our understanding of the basic mechanics of GC vibration and offer practically useful insights into how such vibration may be passively reduced.

Design Optimization and Manufacturing of Bio-fixed tibial implants using 3D printing technology

To obtain high performance (matching, mechanical properties, and biocompatibility) of personalized biomechanical fixation-type tibial implants, three-dimensional reconstruction was performed using a combination of reverse and positive methods. The implant design was optimized using a topological optimization method, the shape-optimized B-unit structure was filled, and the performance was evaluated for implants prepared by direct forming technology of Selective Laser Melting (3D Printing). The results show obviously reduced weight of the tibial implant, increased stress and displacement, yet with a more uniform distribution. The mechanical properties of the tibial implant were lower than those of the B-units, the weight was lighter, and the stress distribution was more uniform. The surface of the tibial implants prepared by SLM appeared clean and bright, the metal texture was good, the structure between the porous struts was clear, the surface had low powder adhesion, the lap joint was good, and no obvious warping deformation or forming defects were observed. The results of this study provide a foundation for the direct application of high performance personalized biofixation implants.

Parametric modeling the human calves for evaluation and design of medical compression stockings

BACKGROUND AND OBJECTIVE

Accurate dose of compression imposed by functional compression garments, such as medical compression stockings, is critical for medical treatment or rehabilitation. To evaluate or provide fitted compression stockings for individuals, the real curved calf shapes other than just anthropometric parameters are required.

METHODS

This paper proposes and verifies a simple 3D parametric model of human calf that can fast and efficiently construct representative calves for evaluation or size-design purposes. Instead of selecting feature points, shape parameters were used to characterize the real calves.

RESULTS

Based on principal component analysis and a database of 220 scanned Chinese subjects, the proposed parametric model was found as capable of largely reducing the data complexity to only the 1st order principal component with a major variance percentage of as high as 96%. A methodological framework of parametric modeling was then proposed. Results show that the proposed parametric model and method can fast produce valid representative shapes of patients' calves, with which commercial stockings can be evaluated and customized stockings can be designed.

CONCLUSIONS

It can be concluded that the proposed parametric model and method can highly boost evaluation/size customization of MCSs, and will also inspire the research on customization of other compression garments.

Quantitative assessment of near-infrared spectroscopy time course under hypercapnia using an a priori model-based fitting

BACKGROUND

Partial arterial pressure of carbon dioxide (CO₂) modulates cerebral blood flow through a vasoreactivity mechanism. Near infrared spectroscopy (NIRS) can be used to record these changes in cerebral hemodynamics. However, no laterality comparison of the NIRS signal has been performed despite being a prerequisite for the use of such a method in a vasoreactivity monitoring context. We propose to investigate the NIRS signal laterality in response to a CO₂-inhalation-based hypercapnia paradigm in healthy volunteers.

METHODS

Eleven healthy volunteers (6 women, 5 men, mean age: 31 ± 11) underwent a 3-block-design inhalation paradigm: normoxia (5min, "baseline") - hypercapnia (2min, "stimulation") - normoxia (5min, "post-stimulation"). NIRS signal was measured using a two-channel oximeter (INVOS 5100C, Medtronic, USA) with sensors placed symmetrically on both left and right sides on each subject's forehead. Additional heart rate (HR) monitoring was performed simultaneously. Based on the NIRS mean signal pattern, an a priori model of parametric identification was applied for each channel to quantify parameters of interest (amplitude, time delay, excitation and post-stimulation time) for each inhalation block.

RESULTS

HR increased significantly during the stimulation block. The quality of the model was satisfactory: mean absolute errors between modeled and experimental signals were lower than the resolution of the device. No significant lateralization was found between left and right values of most of the parameters.

CONCLUSION

Due to the lack of lateralization, this parametric identification of NIRS responses to hypercapnia could bring light to a potential asymmetry and be used as a biomarker in patients with cerebrovascular diseases.

The structural origins of brittle star arm kinematics: An integrated tomographic, additive manufacturing, and parametric modeling-based approach

Brittle stars are known for the high flexibility of their arms, a characteristic required for locomotion, food grasping, and for holding onto a great diversity of substrates. Their high agility is facilitated by the numerous discrete skeletal elements (ossicles) running through the center of each arm and embedded in the skin. While much has been learned regarding the structural diversity of these ossicles, which are important characters for taxonomic purposes, their impact on the arms' range of motion, by contrast, is poorly understood. In the present study, we set out to investigate how ossicle morphology and skeletal organization affect the flexibility of brittle star arms. Here, we present the results of an in-depth analysis of three brittle star species (Ophioplocus esmarki, Ophiopteris papillosa, and Ophiothrix spiculata), chosen for their different ranges of motion, as well as spine size and orientation. Using an integrated approach that combines behavioral studies with parametric modeling, additive manufacturing, micro-computed tomography, scanning electron microscopy, and finite element simulations, we present a high-throughput workflow that provides a fundamental understanding of 3D structure-kinematic relationships in brittle star skeletal systems.

Development of seismic vulnerability index methodology for reinforced concrete buildings based on nonlinear parametric analyses

This paper presents a simplified method in the seismic vulnerability assessment of reinforced concrete (RC) buildings based on proposed seismic vulnerability index (SVI) methodology. The employed procedure is derived with some modifications from the Italian GNDT and the European Macro-seismic approaches. Eight parameters were modeled in three distinct vulnerability classes to estimate the vulnerability indices of RC structures. The vulnerability classes were categorized based on the earthquake resistant design (ERD) defined as; (Low, Moderate, and High)-ERDs. Nonlinear time history analysis (NL-THA) and nonlinear static analysis (NL-SA) were carried out to define the weight of each parameter in order to calculate the seismic vulnerability index in a specific intensity (PGA) of an earthquake event. Knowing that it ranges from 0 to 1 from less vulnerable to most vulnerable with respect to the seismic intensity. In addition, the engineering demand parameter (EDP) used to determine the vulnerability index as the maximum top displacement of the structure. After determining the (SVI), The mean damage states were developed to evaluate the estimated physical damage of buildings in distinct seismic intensities. •This simplified methodology helps to manage and implements strategies for the safety of the communities before earthquake takes place by investigating the vulnerability classes for each building type.•Modeling the parameters that have an influence on the structural behavior without considering the past-damages observations through an analytical approach.•Developing the seismic vulnerability index can reduce or limit the role of the rapid visual screening methods, which is based on expert opinion decisions, and depends on observations of damages caused by earthquakes, and can be a useful framework criterion in earthquake filed.

Pre-procedural fit-testing of TAVR valves using parametric modeling and 3D printing

BACKGROUND

Successful transcatheter aortic valve replacement (TAVR) requires an understanding of how a prosthetic valve will interact with a patient's anatomy in advance of surgical deployment. To improve this understanding, we developed a benchtop workflow that allows for testing of physical interactions between prosthetic valves and patient-specific aortic root anatomy, including calcified leaflets, prior to actual prosthetic valve placement.

METHODS

This was a retrospective study of 30 patients who underwent TAVR at a single high volume center. By design, the dataset contained 15 patients with a successful annular seal (defined by an absence of paravalvular leaks) and 15 patients with a sub-optimal seal (presence of paravalvular leaks) on post-procedure transthoracic echocardiogram (TTE). Patients received either a balloon-expandable (Edwards Sapien or Sapien XT, n = 15), or a self-expanding (Medtronic CoreValve or Core Evolut, n = 14, St. Jude Portico, n = 1) valve. Pre-procedural computed tomography (CT) angiograms, parametric geometry modeling, and multi-material 3D printing were utilized to create flexible aortic root physical models, including displaceable calcified valve leaflets. A 3D printed adjustable sizing device was then positioned in the aortic root models and sequentially opened to larger valve sizes, progressively flattening the calcified leaflets against the aortic wall. Optimal valve size and fit were determined by visual inspection and quantitative pressure mapping of interactions between the sizer and models.

RESULTS

Benchtop-predicted "best fit" valve size showed a statistically significant correlation with gold standard CT measurements of the average annulus diameter (n = 30, p < 0.0001 Wilcoxon matched-pairs signed rank test). Adequateness of seal (presence or absence of paravalvular leak) was correctly predicted in 11/15 (73.3%) patients who received a balloon-expandable valve, and in 9/15 (60%) patients who received a self-expanding valve. Pressure testing provided a physical map of areas with an inadequate seal; these corresponded to areas of paravalvular leak documented by post-procedural transthoracic echocardiography.

CONCLUSION

We present and demonstrate the potential of a workflow for determining optimal prosthetic valve size that accounts for aortic annular dimensions as well as the active displacement of calcified valve leaflets during prosthetic valve deployment. The workflow's open source framework offers a platform for providing predictive insights into the design and testing of future prosthetic valves.

Feasibility study applying a parametric model as the design generator for 3D-printed orthosis for fracture immobilization

Background

Applying 3D printing technology for the fabrication of custom-made orthoses provides significant advantages, including increased ventilation and lighter weights. Currently, the design of such orthoses is most often performed in the CAD environment, but creating the orthosis model is a time-consuming process that requires significant CAD experience. This skill gap limits clinicians from applying this technology in fracture treatment. The purpose of this study is to develop a parametric model as the design generator for 3D–printed orthoses for an inexperienced CAD user and to evaluate its feasibility and ease of use via a training and design exercise.

Results

A set of automatic steps for orthosis modeling was developed as a parametric model using the Visual Programming Language in the CAD environment, and its interface and workflow were simplified to reduce the training period. A quick training program was formulated, and 5 participants from a nursing school completed the training within 15 mins. They verified its feasibility in an orthosis design exercise and designed 5 orthoses without assistance within 8 to 20 mins. The few faults and program errors that were observed in video analysis of the exercise showed improvable weaknesses caused by the scanning quality and modeling process.

Conclusions

Compared to manual modeling instruction, this study highlighted the feasibility of using a parametric model for the design of 3D–printed orthoses and its greater ease of use for medical personnel compared to the CAD technique. The parametric model reduced the complex process of orthosis design to a few minutes, and a customized interface and training program accelerated the learning period. The results from the design exercise accurately reflect real-world situations in which an inexperienced user utilizes a generator as well as demonstrate the utility of the parametric model approach and strategy for training and interfacing.

Robust Parameter Identification to Perform the Modeling of pta and poxB Genes Deletion Effect on Escherichia Coli

The aim of this study was to design a robust parameter identification algorithm to characterize the effect of gene deletion on Escherichia coli (E. coli) MG1655. Two genes (pta and poxB) in the competitive pathways were deleted from this microorganism to inhibit pyruvate consumption. This condition deviated the E. coli metabolism toward the Krebs cycle. As a consequence, the biomass, substrate (glucose), lactic, and acetate acids as well as ethanol concentrations were modified. A hybrid model was proposed to consider the effect of gene deletion on the metabolism of E. coli. The model parameters were estimated by the application of a least mean square method based on the instrument variable technique. To evaluate the parametric identifier method, a set of robust exact differentiators, based on the super-twisting algorithm, was implemented. The hybrid model was successfully characterized by the parameters obtained from experimental information of E. coli MG1655. The significant difference between parameters obtained with wild-type strain and the modified (with deleted genes) justifies the application of the parametric identification algorithm. This characterization can be used to optimize the production of different byproducts of commercial interest.

Magnetic resonance spectroscopic imaging at superresolution: Overview and perspectives

The notion of non-invasive, high-resolution spatial mapping of metabolite concentrations has long enticed the medical community. While magnetic resonance spectroscopic imaging (MRSI) is capable of achieving the requisite spatio-spectral localization, it has traditionally been encumbered by significant resolution constraints that have thus far undermined its clinical utility. To surpass these obstacles, research efforts have primarily focused on hardware enhancements or the development of accelerated acquisition strategies to improve the experimental sensitivity per unit time. Concomitantly, a number of innovative reconstruction techniques have emerged as alternatives to the standard inverse discrete Fourier transform (DFT). While perhaps lesser known, these latter methods strive to effect commensurate resolution gains by exploiting known properties of the underlying MRSI signal in concert with advanced image and signal processing techniques. This review article aims to aggregate and provide an overview of the past few decades of so-called "superresolution" MRSI reconstruction methodologies, and to introduce readers to current state-of-the-art approaches. A number of perspectives are then offered as to the future of high-resolution MRSI, with a particular focus on translation into clinical settings.

A framework for parametric modeling of ankle ligaments to determine the in situ response under gross foot motion

Ligament sprains account for a majority of injuries to the foot and ankle complex, but ligament properties have not been understood well due to the difficulties in replicating the complex geometry, in situ stress state, and non-uniformity of the strain. For a full investigation of the injury mechanism, it is essential to build up a foot and ankle model validated at the level of bony kinematics and ligament properties. This study developed a framework to parameterize the ligament response for determining the in situ stress state and heterogeneous force-elongation characteristics using a finite element ankle model. Nine major ankle ligaments and the interosseous membrane were modeled as discrete elements corresponding functionally to the ligamentous microstructure of collagen fibers and having parameterized toe region and stiffness at the fiber level. The range of the design variables in the ligament model was determined from existing experimental data. Sensitivity of the bony kinematics to each variable was investigated by design of experiment. The results highlighted the critical role of the length of the toe region of the ligamentous fibers on the bony kinematics with the cumulative influence of more than 95%, while the fiber stiffness was statistically insignificant with an influence of less than 1% under the given variable range and loading conditions. With the flexibility of variable adjustment and high computational efficiency, the presented ankle model was generic in nature so as to maximize its applicability to capture the individual ligament behaviors in future studies.

The transfer function of neuron spike

The mathematical modeling of neuronal signals is a relevant problem in neuroscience. The complexity of the neuron behavior, however, makes this problem a particularly difficult task. Here, we propose a discrete-time linear time-invariant (LTI) model with a rational function in order to represent the neuronal spike detected by an electrode located in the surroundings of the nerve cell. The model is presented as a cascade association of two subsystems: one that generates an action potential from an input stimulus, and one that represents the medium between the cell and the electrode. The suggested approach employs system identification and signal processing concepts, and is dissociated from any considerations about the biophysical processes of the neuronal cell, providing a low-complexity alternative to model the neuronal spike. The model is validated by using in vivo experimental readings of intracellular and extracellular signals. A computational simulation of the model is presented in order to assess its proximity to the neuronal signal and to observe the variability of the estimated parameters. The implications of the results are discussed in the context of spike sorting.