Materials Young's Modulus Identification Using Genetic Algorithms

This research aims at characterizing and predicting the Young's modulus of thin film materials that are utilized in the microelectromechanical systems (MEMS). As a proof of concept, aluminum and TEOS thin films were analyzed using bilayer cantilever as a test structure. Due to the lack of understanding of the mechanical behavior of thin film materials in the micro-scale domain, empirical models were developed that utilize soft computing techniques. As a result, this methodology is foreseen to be an essential tool for MEMS designers as it can estimate and predict effective Young's modulus of materials in the micro-scale domain. In the estimation phase, 2D search and micro genetic algorithm were studied and in the prediction phase, back propagation based neural networks and one dimensional radial basis function networks (1D-RBFN) were studied. All combinations of these soft computing techniques are evaluated. Based on the results, we conclude that among the various combinations tested, the combination of 1D-RBFN (prediction phase) and GA (estimation phase) presented the best results. Research is in progress in applying other algorithms such as support vector machines as well as investigating other novel test structures that can be used to extract other material properties such as coefficient of thermal expansion.

Most of the electromagnetic devices, especially electrical machines, have the disadvantage to be exposed to high vibrations caused by magnetic forces. The aim of this study is to propose a methodology to optimize the cylindrical stators generally used in electrical machines regarding the vibration phenomena. Techniques for vibration reduction require knowledge of the proper frequencies, which depend on mechanical shapes and dimensions as well as material properties such as mass density, Young's modulus and Poisson's ratio. This paper proposes a new approach which is based on the identification of mass density (lamination stacking factor) and Young's modulus in the goal to minimize the vibratory behavior of electrical machines. In this goal, we have used artificial intelligent and finite element method (FEM) analysis to solve the magneto‐mechanical inverse problem (IP). In the proposed approach, a Multilayer Perceptron Neural Network (MLPNN) is used as a forward model in order to decrease the FEM time consuming. Thus, a Genetic Algorithm (GA) is used to solve the IP in a reasonable time of running. An example study of an induction machine proves that the developed approach may be applied in both design and identification applications. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, the ability of the artificial neural network which was trained based on a Genetic algorithm used to predict the shear capacity of the reinforced concrete beams strengthened with the side-bonded fiber reinforced polymer (FRP). A database of experimental data including 95 data which were published in literature was collected and used to the network. Seven inputs including the width of the beam, effective depth, FRP thickness, Young modulus, the tensile strength of FRP and also FRP ratio were used to predict the shear capacity of the reinforced concrete beams strengthened with the side-bonded fiber reinforced polymer. The best values of the weights and the biases were obtained by the Genetic algorithm. For increasing the ability of the model to predict the considered target, it was suggested that the predicted values considered smaller. The results indicated that the proposed neural network based on genetic algorithm was able to predict the shear capacity of the considered elements.

The selection of nanofillers and compatibilizing agents, and their size and concentration, are always considered to be crucial in the design of durable nanobiocomposites with maximized mechanical properties (i.e., fracture strength (FS), yield strength (YS), Young’s modulus (YM), etc). Therefore, the statistical optimization of the key design factors has become extremely important to minimize the experimental runs and the cost involved. In this study, both statistical (i.e., analysis of variance (ANOVA) and response surface methodology (RSM)) and machine learning techniques (i.e., artificial intelligence-based techniques (i.e., artificial neural network (ANN) and genetic algorithm (GA)) were used to optimize the concentrations of nanofillers and compatibilizing agents of the injection-molded HDPE nanocomposites. Initially, through ANOVA, the concentrations of TiO2 and cellulose nanocrystals (CNCs) and their combinations were found to be the major factors in improving the durability of the HDPE nanocomposites. Further, the data were modeled and predicted using RSM, ANN, and their combination with a genetic algorithm (i.e., RSM-GA and ANN-GA). Later, to minimize the risk of local optimization, an ANN-GA hybrid technique was implemented in this study to optimize multiple responses, to develop the nonlinear relationship between the factors (i.e., the concentration of TiO2 and CNCs) and responses (i.e., FS, YS, and YM), with minimum error and with regression values above 95%.

ABSTRACTIn the developing of scaffolds for cell culture, a large number of architectures with different combinations of properties should be tested to determine the best. This can be costly in time, money and materials. In this paper we have proposed an optimization model that aims to maximize the growth of osteoblasts on polymeric scaffolds by regulating their properties and architecture. Based on the optimization model it was implemented a genetic algorithm to calculate the architecture and properties of the scaffolds. The fiber diameter, pore diameter, porosity, Young's modulus and contact angle of the scaffolds were calculated through four electrospinning parameters: voltage (kV), concentration (% w/v), flow rate (ml/h) and distance (cm). A fitness value was assigned to each scaffold and the highest one was chosen as the best condition for osteoblast growth. The preliminary results obtained by the Genetic Algorithm were consistent with the tendencies reported experimentally in other studies. Also, the methodology established here can be easily adapted to different types of polymers and cells. Finally, the optimization model can be applied not only by means of heuristic method, like a Genetic Algorithm, but also by exact methods.

Finite element (FE) model updating belongs to the class of inverse problems in mechanics and is a constrained optimization problem. In FE model updating, the difference between the modal parameters (the frequencies, damping ratios and the mode shapes) obtained from the FE model of the structure and those from the vibration measurements are minimized within an optimization algorithm. The design variables of the optimization problem are the stiffness reduction factors, which represent the damage. In this study, the Genetic Algorithms (GA), the Parallel GA, the local search algorithms, the Trust Region Gauss Newton, the Sequential Quadratic Programming, the Levenberg–Marquardt Techniques and the hybrid versions of these methods are applied within the FE Model Updating Technique for updating the Young's modulus of different FEs of a reinforced concrete beam. Different damage scenarios and different noise levels are taken into account. The results of the study show that the local search algorithms cannot detect, locate and quantify damage in reinforced concrete beam type structures while the GA together with the hybrid and the parallel versions detect, localize and identify the damage very accurately. It is apparent that the hybrid GA & Trust Region Gauss Newton Technique is best in terms of the computation speed as well as accuracy.

The forced response of the first rotor of an engine 3E (technology program) (E3E)-type high pressure compressor (HPC) blisk is analyzed with regard to varying mistuning, varying engine order (EO) excitations and the consideration of aero-elastic effects. For that purpose, subset of nominal system modes (SNM)-based reduced order models are used in which the disk remains unchanged while the Young's modulus of each blade is used to define experimentally adjusted as well as intentional mistuning patterns. The aerodynamic influence coefficient (AIC) technique is employed to model aero-elastic interactions. Furthermore, based on optimization analyses and depending on the exciting EO and aerodynamic influences it is searched for the worst as well as the best mistuning distributions with respect to the maximum blade displacement. Genetic algorithms using blade stiffness variations as vector of design variables and the maximum blade displacement as objective function are applied. An allowed limit of the blades' Young's modulus standard deviation is formulated as secondary condition. In particular, the question is addressed if and how far the aero-elastic impact, mainly causing aerodynamic damping, combined with mistuning can even yield a reduction of the forced response compared to the ideally tuned blisk. It is shown that the strong dependence of the aerodynamic damping on the interblade phase angle is the main driver for a possible response attenuation considering the fundamental blade mode. The results of the optimization analyses are compared to the forced response due to real, experimentally determined frequency mistuning as well as intentional mistuning.

In the governing equation of motion of bond‐based peridynamics, the acceleration of a material point can be considered as the response function of all the displacements of material points in the horizon and a micro stiffness containing Young's modulus and length scale. A group method of data handling (GMDH) neural network is first developed to explicitly derive the discrete bond‐based peridynamic equation of motion based on measured data in this study rather than traditional complicated mathematical derivation. In order to discover the optimal structure more efficiently and to avoid exhaustive search, genetic algorithm is incorporated into GMDH structure. It is found that the prediction results obtained by GMDH model agree well with measured values both for training and testing data. Moreover, the derived equation of motion is expressed as the product of parameter composed of Young's modulus and length scale and linear combination of displacements of material points in the horizon, which is in accordance with the original bond‐based peridynamic formulation. Furthermore, numerical benchmarks associated with elastic deformation and crack problems are performed and compared with analytical solution or finite element analysis result to verify the validity and feasibility of the proposed model.

Compressive strength, a crucial mechanical property of cement mortars, is typically measured destructively. However, there is a need to evaluate the strength of unique cement-based samples at various ages without causing damage. In this paper, a predictive framework using a genetic algorithm (GA) is proposed for estimating the compressive strength of ordinary cement-based mortars based on their dynamic elastic modulus, measured non-destructively using the impulse excitation technique. By combining the Popovics model (PM) and the Lydon–Balendran model (LBM), the static elastic modulus of samples was calculated using constant coefficients, representing an equivalent compressive strength. A GA was then employed to determine optimal values for these coefficients. The results showed that the LBM-based strength was dominant in the middle range of the dynamic Young's modulus while the PM-based strength was dominant for higher and lower values of the dynamic Young's modulus. The model was found to have a small root mean square error (3.1%). The findings suggest that this non-destructive model has potential for predicting the mechanical properties of cement mortars. It allows efficient evaluation of compressive strength without destructive testing, offering advantages for reliable assessments of cement-based materials.

Simultaneous determination of Young's modulus and residual stress via dynamic analysis of nanobridge exhibiting beam-to-string behaviors using a genetic algorithm

<div class="section abstract"><div class="htmlview paragraph">Tortuosity, viscous characteristic length and thermal characteristic length are three important parameters for estimating the acoustic performance of porous materials, and it is usually measured by ultrasonic measurement technology, which is costly. In this paper, a method for identifying the tortuosity, viscous characteristic length and thermal characteristic length for the porous fiber materials mixed with kapok fiber and two kinds of other fiber materials is proposed. The tortuosity is calculated by using the porosity and high-frequency normal sound absorption coefficient of porous materials. According to the normal sound absorption coefficient curve of porous materials under plane wave incidence, viscous characteristic length and thermal characteristic length are identified through the Johnson-Champoux-Allard-Lafarge (JCAL) model and genetic algorithm by using the measured parameters, the calculated tortuosity and static thermal permeability. The measured parameters include static airflow resistivity, porosity, Young's modulus, Poisson's ratio, structural loss factor, thickness and density. The error between the parameters identification results and the experiments is analyzed. The error between the simulations and the experiments of the normal sound absorption coefficient under plane wave incidence is very small, which proves the accuracy of this method. The relationship between sound absorption performance of kapok mixed fiber porous materials, and tortuosity, viscous characteristic length and thermal characteristic length is investigated. Assuming the frequency range is located between 1000Hz to 4000Hz under the plane wave incidence, taking the average normal sound absorption coefficient of the materials as the objective function, the JCAL model and genetic algorithm are then used to obtain the optimal parameters of kapok mixed fiber porous materials.</div></div>

Knowledge of in-situ stresses and geomechanical properties is important for wellbore stability and hydraulic fracture optimization applications. Both mechanical rock properties (e.g., Young's modulus and Poisson's ratio) and the stresses represent the initial step in constructing a geomechanical model that will eventually require static calibration from the lab or field tests. Nonetheless, a wellbore deformation-based inverse analysis solution has become an alternative method that characterizes in-situ stress in particular. In this paper, a genetic algorithm and probabilistic analysis methods are proposed and integrated into a well-drilled known analytical method to characterize both stresses and geomechanical properties. Systematic steps have been applied to this analysis. First, borehole geometry (i.e., multi-arm caliper), mud weight, and vertical pressure (from the density log) are well-defined inputs for deformation-stress relationships. Unknown parameters have also been determined and include horizontal stress, Poisson's ratio, and Young's modulus. Subsequently, the minimum and maximum expected values for each unknown parameter have been defined. Thousands of combinations have been created by the analytical equation (fitness function). In addition, the semi-genetic algorithm concept was used as an optimization method to find the best solution from a wide range of inputs for a given fitness function. The first hundred strongest fitness combinations were then chosen for the next level, which had a noticeably higher frequency number using the statistical analysis technique. The approach was checked with a real field example, the results indicated the measured values of geomechanical properties, and horizontal stress were reasonably consistent with the actual field data and previous studies in the field. In particular, the proposed approach allows for a realistic estimate of the most difficult stress (i.e., Max horizontal stress), which was ~45 % higher than minimum horizontal stress. The proposed technique was developed to reduce in-situ pressure uncertainties and geomechanical properties for the studied area. Results from this paper presented a simple and practical alternative method for the determination of geomechanical parameters using a simple logging tool (e.g., a caliper) that theoretically provides a robustness guide for wellbore stability and hydraulic fracture models for tight gas fields.

This paper employs a back analysis method to determine soil strength parameters of the Mohr-Coulomb model from in situ geotechnical measurements. The lateral displacement of a soil nailed wall retaining an excavation in Tehran city used as a criterion for the back analysis. For this purpose, a genetic algorithm is applied as an optimization algorithm to minimize the error function, which can perform the back analysis process. When the accuracy of modeling is verified, the back analysis is performed automatically by creating a link between genetic algorithm in MATLAB and Abaqus software using Python programming language. This paper demonstrated that the genetic algorithm is a particularly suitable tool to determine 9 soil strength parameters simultaneously for 3 soil layers of the project site to decrease the difference of lateral displacement between the results of project monitoring and numerical analysis. The soil strength parameters have increased, with the most changes in Young's modulus of the first to third layers as the most effective parameter, 49.45%, 61.67% and 64.35% respectively. The results can be used in advanced engineering analyses and professional works.

A nondestructive and faster methodology to quantify mechanical properties of polypropylene (PP) pellets, obtained from an industrial plant, was developed with Raman spectroscopy. Raman spectra data were obtained from several types of samples such as homopolymer PP, random ethylene-propylene copolymer, and impact ethylene-propylene copolymer. Multivariate calibration models were developed by relating the changes in the Raman spectra to mechanical properties determined by ASTM tests (Young's traction modulus, tensile strength at yield, elongation at yield on traction, and flexural modulus at 1% secant). Several strategies were evaluated to build robust models including the use of preprocessing methods (baseline correction, vector normalization, de-trending, and standard normal variate), selecting the best subset of wavelengths to model property response and discarding irrelevant variables by applying genetic algorithm (GA). Linear multivariable models were investigated such as partial least square regression (PLS) and PLS with genetic algorithm (GA-PLS) while nonlinear models were implemented with artificial neural network (ANN) preceded by GA (GA-ANN). The best multivariate calibration models were obtained when a combination of genetic algorithms and artificial neural network were used on Raman spectral data with relative standard errors (%RSE) from 0.17 to 0.41 for training and 0.42 to 0.88% validation data sets.

This study proposes a suitable composite material for acetabular cup replacements in hip joint that involves ultrahigh molecular weight polyethylene, a clinically proven material, as the matrix. To design new ultrahigh molecular weight polyethylene composites with multiple reinforcements for the improvement in mechanical and tribological performance, artificial neural network and genetic algorithm, the two artificial intelligence techniques, are employed. Published reports on the use of ultrahigh molecular weight polyethylene reinforced with multi-walled carbon nanotube and graphene are used as database to develop two artificial neural network models for Young's modulus and tensile strength. The optimum solutions are obtained using genetic algorithm, where the artificial neural network models are used as the objective functions. Two different composites, derived from the optimum solutions, are made reinforcing both multi-walled carbon nanotube and graphene. Tensile and wear tests show significant enhancement in the properties. The structures of the composites are also characterized, and wear mechanisms are discussed.