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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.

Jelen közleményben áttekintjük a modern elektromos és hibrid rendszerű járműhajtásokat. Továbbá áttekintjük az azokban alkalmazott villanymotorok, akkumulátorok és üzemanyagcellák típusait, valamint a felépítésükkel és jellemző tulajdonságaikkal foglalkozó szakirodalmat. Továbbá, mivel a hibrid járművek fontos része a belsőégésű motor, áttekintjük azok hatékonyságának növelésére, valamint emissziójuk csökkentésére alkalmazott eljárásokat.

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.

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.

Previously, a dynamic model and a simulation program for series wound DC motors (SWDCM) were developed in MATLAB/Simulink for modelling them in electric vehicles and mechatronic systems. The electromagnetic characteristics of the motor (electric resistances, dynamic inductances), which serve as input parameters of the program, were also measured. Additionally, locked rotor response measurements were performed to test the accuracy of the measured electromagnetic characteristics. This paper presents the experimental procedure and the results of dynamics test measurements that were performed on the same motor, including the procedure for the determination of the necessary input dynamic parameters for the simulation. While the motor spins up from rest, the intensity of the electric current and the angular speed of the rotor are measured. Finally, the simulation and dynamic test results are compared to check the proper operation of the simulation program.

In rubber bumper design, the most important mechanical property of the product is the force–displacement curve under compression and its fulfillment requires an iterative design method. Design engineers can handle this task with the modification of the product shape, which can be solved with several optimization methods if the parameterization of the design process is determined. The numerical method is a good way to evaluate the working characteristics of the rubber product; furthermore, automation of the whole process is feasible with the use of Visual Basic for Application. An axisymmetric finite element model of a rubber bumper was built with the use of a calibrated two-term Mooney–Rivlin material model. A two-dimensional shape optimization problem was introduced where the objective function was determined as the difference between the initial and the optimum characteristics. Our goal was to integrate a surrogate model-based parameter selection of local search algorithms for the optimization process. As a metamodeling technique, cubic support vector regression was selected and seemed to be suitable to accurately predict the nonlinear objective function. The novel optimization procedure which applied the support vector regression model in the parameter selection process of the stochastic search algorithm proved to be an efficient method to find the global optimum of the investigated problem.

A mérnöki kutatások egyik nagy kihívása a mára már széles körben alkalmazott fémhabok geometriai modellezése, valamint a megalkotott 3D-s modell numerikus szilárdságtani vizsgálata. A fémhab struktúrája meglehetősen bonyolult, így a valóságot jól megközelítő CAD modell előállítása önmagában is komoly mérnöki feladat, annak adott terhelésre történő végeselemes szimulációja pedig a kutatások középpontjában áll. A projekt ezen részének célja az előzőekben tárgyalt témakörökre vonatkozó nemzetközi szakirodalom kritikai elemzése és az eredmények adaptálási lehetőségei, továbbá egy térfogatelemzési eljárás bemutatása a fémhab celláival kapcsolatban.

A mérnöki kutatások egyik nagy kihívása a mára már széles körben alkalmazott fémhabok geometriai modellezése, valamint a megalkotott 3D-s modell numerikus szilárdságtani vizsgálata. A fémhab struktúrája meglehetősen bonyolult, így a valóságot jól megközelítő CAD modell előállítása önmagában is komoly mérnöki feladat, annak adott terhelésre történő végeselemes szimulációja pedig a kutatások középpontjában áll. A projekt ezen részének célja az előzőekben tárgyalt témakörökre vonatkozó nemzetközi szakirodalom kritikai elemzése és az eredmények adaptálási lehetőségei, továbbá egy térfogatelemzési eljárás bemutatása a fémhab celláival kapcsolatban.

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]

Previously, a dynamic model and a simulation program for series wound DC motors (SWDCM) were developed in MATLAB/Simulink for modelling them in electric vehicles and mechatronic systems. The electromagnetic characteristics of the motor (electric resistances, dynamic inductances), which serve as input parameters of the program, were also measured. Additionally, locked rotor response measurements were performed to test the accuracy of the measured electromagnetic characteristics. This paper presents the experimental procedure and the results of dynamics test measurements that were performed on the same motor, including the procedure for the determination of the necessary input dynamic parameters for the simulation. While the motor spins up from rest, the intensity of the electric current and the angular speed of the rotor are measured. Finally, the simulation and dynamic test results are compared to check the proper operation of the simulation program. [ABSTRACT FROM AUTHOR]

In rubber bumper design, the most important mechanical property of the product is the force–displacement curve under compression and its fulfillment requires an iterative design method. Design engineers can handle this task with the modification of the product shape, which can be solved with several optimization methods if the parameterization of the design process is determined. The numerical method is a good way to evaluate the working characteristics of the rubber product; furthermore, automation of the whole process is feasible with the use of Visual Basic for Application. An axisymmetric finite element model of a rubber bumper was built with the use of a calibrated two-term Mooney–Rivlin material model. A two-dimensional shape optimization problem was introduced where the objective function was determined as the difference between the initial and the optimum characteristics. Our goal was to integrate a surrogate model-based parameter selection of local search algorithms for the optimization process. As a metamodeling technique, cubic support vector regression was selected and seemed to be suitable to accurately predict the nonlinear objective function. The novel optimization procedure which applied the support vector regression model in the parameter selection process of the stochastic search algorithm proved to be an efficient method to find the global optimum of the investigated problem. [ABSTRACT FROM AUTHOR]

Fault diagnosis of bearings is essential in manufacturing to increase quality. Traditionally, fault diagnosis of tapered roller element bearings is performed by signal processing methods, which handle the nonstationary behaviour of the signal. The wavelet transform is an efficient tool for analysing the vibration signal of the bearings because it can detect the sudden changes and transient impulses in the signal caused by faults in the bearing elements. In this article, manufacturing faults on the outer ring of tapered roller bearings due to the grinding process in manufacturing are investigated. Nine different real values wavelets (Symlet-2, Symlet-5, Symlet-8, db02, db06, db10, db14, Meyer, and Morlet) are compared according to the Energy-to-Shannon-Entropy ratio criteria, and which is efficient for detecting the manufacturing faults is determined. Finally, experiments are carried out on a test rig for determining the geometrical size of the manufacturing faults with all wavelets directly from the vibration signature the result of db02, Symlet-5, and Morlet wavelets are presented. When modelling the bearing structure as an under-damped second-order mass-spring-damper mechanical system, its unit impulse response function is compared to the wavelets on the basis of their Energy-to-Shannon-Entropy ratio to determine the fault size from the vibration signal. The proposed technique has been successfully implemented for measuring defect widths. The maximum deviation in result has been found to be 4.12 % for the defect width which was verified with image analysis methods using an optical microscope and contact measurement. [ABSTRACT FROM AUTHOR]

Wear and surface microgeometry aspects of fiber-reinforced hybrid composite dry friction clutch facings are revealed in a novel way: after different, real life automotive tests during their lifetime. This study examines and reveals the tribological response of friction material surfaces to real life application conditions with two different facing diameters and in two directions. Along the increasing activation energy scale, wear values increased according to two different trends, sorting tests into two main groups, namely 'clutch killer' and 'moderate'. Wear results also highlighted the influence of mileage and test conditions, with clutch killer tests also creating considerable wear-more than 0.1 mm-at inner diameters: 1% higher wear was generated by 90% higher mileage; another 1% increment could be caused by insufficient cooling time or test bench-specific conditions. Surface roughness values trends varied accordingly with exceptions revealing effects of facing size, friction diameter and directions and test conditions: small (S) facings produced significantly decreased Rmax roughness, while large (L) and medium (M) size facings had increased roughness values; Rmax results showed the highest deviations among roughness values in radial direction; tests run with trailer and among city conditions resulted in more than 2% thickness loss and a 40–50% roughness decrease. [ABSTRACT FROM AUTHOR]

In addition to the examination of electric power from local renewables, this study has sought the answer to the question of what proportion of vehicles are fueled by environmentally friendly energy saving technologies in the vehicle fleets of Hungarian settlements. Further, the study attempts to shed light on the self-sufficiency of Hungarian settlements with respect to the electricity and transport segments. In our assessments, the performance of small-scale household power plants (SSHPPs) utilizing local renewable energy sources, and small-scale power plants with installed capacities under 0.5 MW, was taken into account, as were the proportions of vehicles operating with partly or completely clean energy sources in the vehicle fleets of the individual settlements. Finally, the composition of the vehicle fleet has been examined in the light of the quantities of renewable electricity generated in the individual settlements, in order to consider whether these settlements are capable of covering the energy needs of their vehicle stocks from local sources. In the light of the results, the changes generated by the incentives and investments introduced over the past ten years can be established and subsequently, the energy policy needs in the future can be assessed. Our study has incorporated energy geography and settlement geography aspects. [ABSTRACT FROM AUTHOR]

The wear behaviour of two martensitic and one multiphase steel targeted for abrasion and erosion applications in agriculture and mining industry were investigated in three abrasive test systems with different complexity. Scratch tests were performed with different indenter radii, shapes, and loads. The material behaviour was also investigated in multi-asperity contact systems. Pin-on-disc tests were performed with various loads and abrasive particles, as well as abrasive slurry-pot tests with different sliding velocities, distances, and impact angles of the abrasive media were performed. Comparing the test systems, the tested materials ranked similarly based on their wear performance, however, in each configuration, the dominant variable of the wear mechanism differed. The significance and contributions of test paramecenterters, the material's mechanical properties (H, σ M , σ Y , E, ε M , ε B , W, σ c , E c ) and the dimensionless numbers formed from them were investigated on the wear behaviour and the surface deformation. Correlation between parameters was established by multiple linear regression models. The sensitivity of the tested materials to abrasion was evaluated taking into account the wide range of influencing parameters. [ABSTRACT FROM AUTHOR]