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As the name implies, patient-specific latticed hip implants vary in design depending on the properties required by the patient to serve as a valid suitable organ. Unit cells are typically built based on a 3D design of beams, and the properties of unit cells change depending on their geometries, which, in turn, are defined by two main parameters: beam length and beam thickness. Due to the continuous increase in the complexity of the unit cells’ designs and their reactions against different loads, the call for machine learning techniques is inevitable to help explore the parameters of the unit cells that can build lattice structures with specific desirable properties. In this study, a machine learning technique is used to predict the best defining parameters (length and thickness) to create a latticed design with a set of required properties (mainly porosity). The data (porosity, mass, and latticed area) from the properties of three unit-cell types, applied to the latticed part of a hip implant design, were collected based on the random length and thickness for three unit-cell types. Using the linear regression algorithm (a supervised machine learning method) from the scikit-learn library, a machine learning model was developed to predict the value of the porosity for the lattice structures based on the length and thickness as input data. The number of samples needed to generate an accurate result for each type of unit cell is also discussed.

Architected materials are increasingly applied in form of lattice structures to biomedical implant design for the purpose of optimizing the implant’s biomechanical properties. Since the porous design of the lattice structures affects the resulting properties of the implant, its parameters are being investigated by numerous research articles. The design-related parameters of the unit cells for a strut-architected material are mainly the pore size and the strut thickness. Until today, researchers have not been able to decide on the perfect values of the unit cell parameters for the osseointegration process and tissue regeneration. Based on in vivo and in vitro experiments conducted in the field, researchers have suggested a range of values for the parameters of the lattice structures where osseointegration is in acceptable status. The present study presents a comprehensive review of the research carried out until today, experimenting and proposing the optimum unit cell parameters to generate the most suitable lattice structure for the osseointegration procedure presented in orthopedic applications. Additional recommendations, research gaps, and instructions to improve the selection process of the unit cell parameters are also discussed.

A design engineer has to deal with increasingly complex design tasks on a daily basis, for which the available design time is shrinking. Market competitiveness can be improved by using optimization if the design process can be automated. If there is limited information about the behavior of the objective function, global search methods such as simulated annealing (SA) should be used. This algorithm requires the selection of a number of parameters based on the task. A procedure for reducing the time spent on tuning the SA algorithm for computationally expensive, simulation-driven optimization tasks was developed. The applicability of the method was demonstrated by solving a shape optimization problem of a rubber bumper built into air spring structures of lorries. Due to the time-consuming objective function call, a support vector regression (SVR) surrogate model was used to test the performance of the optimization algorithm. To perform the SVR training, samples were taken using the maximin Latin hypercube design. The SA algorithm with an adaptive search space and different cooling schedules was implemented. Subsequently, the SA parameters were fine-tuned using the trained SVR surrogate model. An optimal design was found using the adapted SA algorithm with negligible error from a technical aspect.

Design and testing of real materials is a costly process and usually requires some specific equipment. To alleviate this task numerical methods can be leveraged. In this work we show possible modelling technique for closed-cell material structure using Weaire–Phelan unit cells. As an example existing aluminum structures were used and modelled parametrically, allowing to establish different geometrical models for different applications. Numerical simulations for compression was also done on the developed models to reveal the material response. The influence on the cell wall thickness and the friction between the material and the compression plate was investigated. It was found that the friction coefficient has no significant effect on the material response, except in the case where bonded connection was assumed. It was also demonstrated that material response and the porosity controlled by cell wall thickness have an approximately linear relationship with each other. This method proved to be a flexible and alternative solution of real laboratory tests and targeted to reduce costs of material design. [ABSTRACT FROM AUTHOR]

As the name implies, patient-specific latticed hip implants vary in design depending on the properties required by the patient to serve as a valid suitable organ. Unit cells are typically built based on a 3D design of beams, and the properties of unit cells change depending on their geometries, which, in turn, are defined by two main parameters: beam length and beam thickness. Due to the continuous increase in the complexity of the unit cells' designs and their reactions against different loads, the call for machine learning techniques is inevitable to help explore the parameters of the unit cells that can build lattice structures with specific desirable properties. In this study, a machine learning technique is used to predict the best defining parameters (length and thickness) to create a latticed design with a set of required properties (mainly porosity). The data (porosity, mass, and latticed area) from the properties of three unit-cell types, applied to the latticed part of a hip implant design, were collected based on the random length and thickness for three unit-cell types. Using the linear regression algorithm (a supervised machine learning method) from the scikit-learn library, a machine learning model was developed to predict the value of the porosity for the lattice structures based on the length and thickness as input data. The number of samples needed to generate an accurate result for each type of unit cell is also discussed. [ABSTRACT FROM AUTHOR]

The research is dedicated to determine one of the most important mechanical properties which is the Young's modulus. Its value is crucial for clearly explaining and understanding the results of any mechanical loading experiment. Three cylindrical samples of 15 mm height and 7.5 mm diameter were designed using SpaceClaim application in the ANSYS Software and then 3D printed using Direct Metal Laser Sintering via EOS M 290 3D printer. The specimens were then tested under compression in order to determine the value of the Young's modulus for titanium alloy of grade 23 (Ti, Al, V, O, N, C, H, Fe, Y). The finite element method was executed using ANSYS mechanical to run a comparison between laboratory results with nominal results of the Young's modulus. Young's modulus value is affected by the 3D printing accuracy and quality, the material's quality as well; however, the deviation is within 10%. [ABSTRACT FROM AUTHOR]

Architected materials are increasingly applied in form of lattice structures to biomedical implant design for the purpose of optimizing the implant's biomechanical properties. Since the porous design of the lattice structures affects the resulting properties of the implant, its parameters are being investigated by numerous research articles. The design-related parameters of the unit cells for a strut-architected material are mainly the pore size and the strut thickness. Until today, researchers have not been able to decide on the perfect values of the unit cell parameters for the osseointegration process and tissue regeneration. Based on in vivo and in vitro experiments conducted in the field, researchers have suggested a range of values for the parameters of the lattice structures where osseointegration is in acceptable status. The present study presents a comprehensive review of the research carried out until today, experimenting and proposing the optimum unit cell parameters to generate the most suitable lattice structure for the osseointegration procedure presented in orthopedic applications. Additional recommendations, research gaps, and instructions to improve the selection process of the unit cell parameters are also discussed. [ABSTRACT FROM AUTHOR]

The development of medical implants is an ongoing process pursued by many studies in the biomedical field. The focus is on enhancing the structure of the implants to improve their biomechanical properties, thus reducing the imperfections for the patient and increasing the lifespan of the prosthesis. The purpose of this study was to investigate the effects of different lattice structures under laboratory conditions and in a numerical manner to choose the best unit cell design, able to generate a structure as close to that of human bone as possible. Four types of unit cell were designed using the ANSYS software and investigated through comparison between the results of laboratory compression tests and those of the finite element simulation. Three samples of each unit cell type were 3D printed, using direct metal laser sintering technology, and tested according to the ISO standards. Ti6Al4V was selected as the material for the samples. Stress–strain characteristics were determined, and the effective Young's modulus was calculated. Detailed comparative analysis was conducted between the laboratory and the numerical results. The average Young's modulus values were 11 GPa, 9 GPa, and 8 GPa for the Octahedral lattice type, both the 3D lattice infill type and the double-pyramid lattice and face diagonals type, and the double-pyramid lattice with cross type, respectively. The deviation between the lab results and the simulated ones was up to 10%. Our results show how each type of unit cell structure is suitable for each specific type of human bone. [ABSTRACT FROM AUTHOR]

Featured Application: The developed surrogate model-based parameter tuning method of the simulated annealing algorithm is suitable for solving not only numerical simulation optimization problems but also other computationally intensive model-driven optimizations. A design engineer has to deal with increasingly complex design tasks on a daily basis, for which the available design time is shrinking. Market competitiveness can be improved by using optimization if the design process can be automated. If there is limited information about the behavior of the objective function, global search methods such as simulated annealing (SA) should be used. This algorithm requires the selection of a number of parameters based on the task. A procedure for reducing the time spent on tuning the SA algorithm for computationally expensive, simulation-driven optimization tasks was developed. The applicability of the method was demonstrated by solving a shape optimization problem of a rubber bumper built into air spring structures of lorries. Due to the time-consuming objective function call, a support vector regression (SVR) surrogate model was used to test the performance of the optimization algorithm. To perform the SVR training, samples were taken using the maximin Latin hypercube design. The SA algorithm with an adaptive search space and different cooling schedules was implemented. Subsequently, the SA parameters were fine-tuned using the trained SVR surrogate model. An optimal design was found using the adapted SA algorithm with negligible error from a technical aspect. [ABSTRACT FROM AUTHOR]

Thanks to the great efforts of our reviewers, I Applied Sciences i was able to maintain its standards for the high quality of its published papers. Rigorous peer-reviews are the basis of high-quality academic publishing. [Extracted from the article]

Featured Application: In the design of engineering polymeric products, the detailed finite element investigation is indispensable. In most cases, polymeric products are investigated with multilinear material models over the entire stress-strain range. The definition of these material models requires material parameters, which must be obtained through time and cost demanding laboratory measurements. For some applications, such as quasi-static loading, it would be sufficient to use a linear elastic material model, the material constants of which can be obtained quickly. This article can help designers to apply the right material model as the linear elastic material model can be used until the 0.96% strain value in the case of the PA6 material with sufficient accuracy. One of the most critical issues during polymer finite element simulations is the selection of the proper material models. The widely used and accepted multilinear material models require load case-specific material tests, which are time and cost demanding. Data for these characteristics must be acquired by standardized measurements. On the other hand, the parameters required to create a linear elastic material model in most cases are easy to obtain, and the establishment of the model is a shorter process. This research is aimed to provide information to engineers about the possibility of modeling the nonlinear elastic materials by using linear elastic material models and about the limits of such models. To create the most accurate material models, laboratory measurements were performed on polyamide (PA6) material, which is a widely used raw material in the industry. Test specimens were manufactured to obtain material constants according to the ISO 527-2 standard, and for validating the effectiveness of the applied material models, three different tensile specimens were created, which were tested under quasi-static loading in the elastic region. A comprehensive finite element investigation was performed, and the numerical results were then compared to laboratory measurements using the GOM Aramis digital image correlation (DIC) system. By comparing the optically measured strain data to the numerical results, it was determined that the nonlinear elastic materials can be modeled using linear elastic models in a well identifiable strain range with sufficient accuracy. [ABSTRACT FROM AUTHOR]