FE Prediction Research Articles

Time delay reservoir computing with a silicon microring resonator and a fiber-based optical feedback loop

Silicon microring resonators serve as critical components in integrated photonic neural network implementations, owing to their compact footprint, compatibility with CMOS technology, and passive nonlinear dynamics. Recent advancements have leveraged their filtering properties as weighting functions, and their nonlinear dynamics as activation functions with spiking capabilities. In this work, we investigate experimentally the linear and nonlinear dynamics of microring resonators for time delay reservoir computing, by introducing an external optical feedback loop. After effectively mitigating the impact of environmental noise on the fiber-based feedback phase dependencies, we evaluate the computational capacity of this system by assessing its performance across various benchmark tasks at a bit rate of few Mbps. We show that the additional memory provided by the optical feedback is necessary to achieve error-free operation in delayed-boolean tasks that require up to 3 bits of memory. In this case the microring was operated in the linear regime and the photodetection was the nonlinear activation function. We also show that the Santa Fe and Mackey Glass prediction tasks are solved when the microring nonlinearities are activated. Notably, our study reveals competitive outcomes even when employing only 7 virtual nodes within our photonic reservoir. Our findings illustrate the silicon microring’s versatile performance in the presence of optical feedback, highlighting its ability to be tailored for various computing applications.

Influence of Cooling and Lubrication Parameters on Robot Bone Grinding Temperature and Prediction Modeling

In the process of robot bone grinding, a large amount of heat is generated, which will cause mechanical and thermal damage to bone tissues and nerves. It is necessary to study the influence of cooling and lubrication parameters on the robot bone grinding temperature and establish the prediction models among them. The FE model of single abrasive bone grinding was established to explore the influence of cooling and lubrication parameters on the bone grinding temperature. Response surface design of experiment was carried out to obtain the simulation results, and Design-Expert was used to establish a multiple regression prediction models of grinding temperature under the condition of different cooling and lubrication. Through the variance and response surface comparative analysis of the prediction model, the influence rules of the bone grinding parameters and the cooling and lubrication parameters on the bone grinding temperature was obtained. A robot bone grinding experiment was performed to prove the accuracy of FE simulation and prediction model. The research results show that the relationship between grinding temperature and cooling lubrication parameters obtained by FE simulation, RSM prediction and experiment verification is consistent, and the simulation model and prediction model of cooling and lubrication parameters are proven to be correct and effective. The influence rules and prediction effects obtained in this study will provide a reasonable scheme for doctors to implement robot bone grinding with high efficiency and low damage, and establish the theoretical basis for the effective control of robot bone grinding force thermal damage.

Application of non-associated flow rule for prediction of nonuniform material flow during deep drawing of tailor welded blanks

In order to enhance FE prediction capability, researchers are presently showing interest in applications of non-associated flow rule (NAFR) coupled with Hill48 quadratic function in different sheet metal forming operations. In this work, the concept of NAFR based model was implemented for the first time in FE simulation of deep drawing of DP980-IFHS tailor welded blanks (TWBs) of coated and uncoated sheets. The NAFR model was formulated using two approaches: namely, stress-value based Hill48 as yield function and R-value based Hill48 as the plastic potential function in the first approach, and the vice-versa in the second approach. Also, the classical associated flow rule (AFR) based approach coupled with the anisotropic Hill48 yield model was implemented in the FE simulation for comparison purpose. For improving the prediction accuracy, a mixed hardening equation by combining Voce and Swift hardening law was incorporated as the constitutive equation. It was found that FE simulation implementing the NAFR approach predicted the deep drawing behaviour of the parent materials and TWBs more accurately compared to that of the AFR approach.

Evolution Behaviors and Reduction Mechanism of Curing Residual Stresses in GLARE Laminates under a Hot-Pressing Condition.

Nowadays, variable preparation, forming and processing methods of fiber metal laminates are constantly developing to meet the requirements of different application fields, hence the characteristics and evolution of residual stresses under different manufacturing conditions deserve more attention. In this work, the evolution behaviors of curing residual stresses in GLARE under a hot-pressing condition were studied, and the residual stress reduction mechanism was also explained. Results suggested the FE prediction models of the entire cure process, verified by the fiber Bragg grating (FBG) sensors, were more precise than the traditional elastic model. Moreover, the stress evolution during the cure process mainly occurred in the cooling stage, in which the different coefficient of thermal expansion (CTE) of aluminum and GFRP played a major role. Meanwhile, curing shrinkage stress in the GFRP layer during the holding stage at curing temperature obviously influenced the final stress level. The residual stresses in GFRP layers differed by 9.6 MPa under a hot-pressing and autoclave condition, in which the convection heat transfer condition played a major role as it caused lower thermal stress in the holding stage and a smaller temperature gradient in the cooling stage. Considering this, a lower cooling rate could be a feasible way to obtain GLARE with lower residual stress under a hot-pressing condition.

Geogenic manganese and iron in groundwater of Southeast Asia and Bangladesh – Machine learning spatial prediction modeling and comparison with arsenic

Naturally occurring, geogenic manganese (Mn) and iron (Fe) are frequently found dissolved in groundwater at concentrations that make the water difficult to use (deposits, unpleasant taste) or, in the case of Mn, a potential health hazard. Over 6000 groundwater measurements of Mn and Fe in Southeast Asia and Bangladesh were assembled and statistically examined with other physicochemical parameters. The machine learning methods random forest and generalized boosted regression modeling were used with spatially continuous environmental parameters (climate, geology, soil, topography) to model and map the probability of groundwater Mn > 400 μg/L and Fe > 0.3 mg/L for Southeast Asia and Bangladesh. The modeling indicated that drier climatic conditions are associated with a tendency of elevated Mn concentrations, whereas high Fe concentrations tend to be found in a more humid climate with elevated levels of soil organic carbon. The spatial distribution of Mn > 400 μg/L and Fe > 0.3 mg/L was compared and contrasted with that of the critical geogenic contaminant arsenic (As), confirming that high Fe concentrations are often associated with high As concentrations, whereas areas of high concentrations of Mn and As are frequently found adjacent to each other. The probability maps draw attention to areas prone to elevated concentrations of geogenic Mn and Fe in groundwater and can help direct efforts to mitigate their negative effects. The greatest Mn hazard is found in densely populated northwest Bangladesh and the Mekong, Red and Ma River Deltas of Cambodia and Vietnam. Widespread elevated Fe concentrations and their associated negative effects on water infrastructure pose challenges to water supply. The Mn and Fe prediction maps demonstrate the value of machine learning for the geospatial prediction modeling and mapping of groundwater contaminants as well as the potential for further constituents to be targeted by this novel approach.

3D Finite Element Model on Drilling of CFRP with Numerical Optimization and Experimental Validation.

When drilling Carbon Fibre-Reinforced Plastic (CFRP) materials, achieving acceptable hole quality is challenging while balancing productivity and tool wear. Numerical models are important tools for the optimization of drilling CFRP materials in terms of material removal rate and hole quality. In this research, a macro-Finite Element (FE) model was developed to accurately predict the effect of drill tip geometry on hole entry and exit quality. The macro-mechanical material model was developed treating the Fiber-Reinforced Plastic (FRP) as an Equivalent Homogeneous Material (EHM). To reduce computational time, a numerical analysis was performed to investigate the influence of mass scaling, bulk viscosity, friction, strain rate strengthening, and cohesive surface modelling. A consideration must be made to minimize the dynamic effects in the FE prediction. The experimental work was carried out to investigate the effect of drill tip geometry on drilling forces and hole quality and to validate the FE results. The geometry of the drills used were either double-point angle or a “candle-stick” profile. The 3D drilling model accurately predicts the thrust force and hole quality generated by the two different drills. The results highlight the improvement in predicted results with the inclusion of cohesive surface modelling. The force signature profiles between the simulated and experimental results were similar. Furthermore, the difference between the predicted thrust force and those measured were less than 9%. When drilling with a double-angle drill tip, the inter-ply damage was reduced. This trend was observed in FE prediction.

Rapid and inexpensive assessment of soil total iron using Nix Pro color sensor

AbstractIn this study, an inexpensive Nix Pro (Nix Sensor Ltd.) color sensor was used to develop prediction models for soil iron (Fe) content. Thirty‐eight soil samples were collected from five agricultural fields across the Animas watershed to develop and validate soil Fe prediction models. We used color space models to develop three different parameter sets for Fe prediction with Nix Pro. The different color space sets were used to develop three new predictive models for Nix Pro‐based Fe content against the lab‐based inductively coupled plasma analyzed Fe content. The model performances were assessed using the coefficient of determination, root mean square error, and model p‐value. Three models (International Commission on Illumination's lightness, ±a axis (redness to greenness), and ± b axis (yellowness to blueness) [CIEL*a*b]; red, green, blue [RGB]; and cyan, magenta, yellow, key [black] [CMYK]) were significant in predicting the Fe content using colorimetric variables with R2 ranging from 0.79 to 0.81. The mean square prediction error (MSPE) and Kling–Gupta efficiency (KGE) Index were calculated to validate models and CMYK was predicted to be a better model (MSPE = 0.13; KGE = 0.601) than CIEL*a*b and RGB models. The results suggest Nix Pro is useful in predicting soil Fe content.

FE stress analysis and prediction of the pull-out of FRP rods glued into glulam timber

ABSTRACT This paper focuses on the FE analysis of the mechanical behavior of glued composite fiber-reinforced polymer (FRP) rods into glulam timber, using a 3D-continuum damage mechanics. The application of FRP to the glulam timber beams, despite the fact of limited investigations to date, offers an interesting and economic solution for strengthening in timber construction. In particular, the use of glued-in FRP rods for timber connections instead of steel is of great interest, due to the improved durability of the joint systems compared to their equivalent counterparts made of steel rods. For pull-out tests, the estimation of the mechanical response of glued FRP rods into glulam timber is very complex because of the combination of the three different materials: FRP rods, epoxy resin and glulam timber as well as the complexity of the expected brittle modes of failure of the timber. On the other hand, the existing prescriptive approaches (standard design codes) did not cover all the modes of failure expected within timber material and their predictivity is highly depending on the loading direction and on the rod material. There is, therefore, still a need to establish a general and predictive FE model to simulate accurately and cost-effectively the glued-in rod timber connections. In this study, a FE model combining 3D continuum damage mechanics (CDM) and cohesive zone modeling approaches is presented and its effectiveness was verified by comparison to experimental results available in the literature.

Toward clinically useful models for individualised prognostication in psychosis

Toward clinically useful models for individualised prognostication in psychosis

FE Prediction and Extrapolation of Multiaxial Ratcheting for R7T Steel

Wear of materials in rail/wheel industry is closely related to the cyclic creep. This contribution presents main results of experimental testing on R7T wheel steel. The cyclic creep is investigated under non-proportional loading conditions simulating a line rolling contact case. McDowell extrapolation was successfully applied to the calculation of twist. Cyclic material model MAKOC and MAKOC with memory surface were used for cyclic creep prediction. The plasticity model is based on AbdelKarim-Ohno kinematic hardening and Calloch isotropic hardening rules. Second material model was extended with Jiang-Sehitoglu memory surface, which is introduced in stress space. Material models were successfully used for predicting accumulation of shear strain.

On the determination of manganese in the presence of iron in pregnant leach solution using derivative spectrophotometry

Mn determination in samples with high Fe content using derivative spectrophotometric method was proposed in this paper, which formaldoxime was used as chromogenic reagent. The derivative method is useful to manipulate spectra data in order to eliminate the noise spectra i.e. Fe and isolate the spectra of elements of interest (Mn). Formaldoxime was chosen since the reagent reacted both to Mn and Fe at the same pH condition. In this paper, the accuracy in Mn determination in the presence of Fe using proposed method would be assessed and discussed. Mn and Fe could be simultaneously determined Mn using zero crossing point of first derivative Fe-formaldoximate spectra at 530 nm for Mn determination, while Fe could be predicted using zero crossing point of second derivative Mn-formaldoximate spectra at 469 nm. Maximum concentration which obeyed the linearity of calibration curve is 2 and 20 mg/L for Mn and Fe, respectively. The error in Mn and Fe prediction depends on ratio of both elements.

FE prediction of thermal performance and stresses in an automotive disc brake system

This study aims to identify thermal effects in the structure and the contact behavior of a disc-pad assembly using a finite element approach. The first analysis is performed on the disc-pad model in the absence of thermal effects. The structural performance of the disc-pad model is predicted in terms of factors such as the deformation and von Mises stress. Next, thermomechanical analysis is performed on the same disc-pad model with the inclusion of convection, adiabatic, and heat flux elements. The predicted temperature distribution, deformation, stress, and contact pressure are presented. The structural performance between the two analyses (mechanical and thermomechanical) is compared. For this contribution, we were able to find and use some experimental results on the contact pressure distributions inner brake pads to validate our thermomechanical model. This study can assist brake engineers in choosing a suitable analysis method to critically evaluate the structural and contact behavior of the disc brake assembly.

Mechanism-based modelling of plastic deformation in magnesium alloys

The plastic deformation of two different rolled magnesium sheets (AZ31 and ZE10) under quasi-static tensile and compressive loading conditions at room temperature is studied. Beside glide by dislocation motion, deformation twinning leads to evolving flow stress asymmetry and evolving plastic anisotropy in these alloys. These mechanisms cause a significant change in the shape of the yield surface with accumulated plastic deformation which cannot be described by traditional hardening concepts. A phenomenological plasticity model in which the primary deformation mechanisms, slip and (extension) twinning, are treated separately is developed here and incorporated in the finite element framework. Deformations caused by these mechanisms are modelled by a symmetric and an asymmetric plastic potential, respectively. The hardening functions are coupled to account for the latent hardening. The necessary input for model parameter calibration is provided by mechanical tests along different orientations of the rolled sheets. Tensile tests of notched samples and shear tests are furthermore incorporated in the parameter optimisation strategy, which is based on minimising the difference between experimental behaviour and FE prediction over the entire deformation range up to failure. The model accounts for the characteristic tension-compression asymmetry and the evolution of strain anisotropy. Both, the convex-up and the concave-down shaped stress–strain response are predicted. The computational model is exemplified by studying the evolution of the plastic multipliers during a shear test. It is shown that despite its phenomenological character, the model predicts the dominant deformation mechanism present at monotonous loading of the magnesium sheets investigated.

Experimental and numerical investigation of composite bolted π-joint subjected to bending load

This present work investigated the failure mechanism of a novel composite bolted π-joint subjected to bending load by experimental and finite element simulation. A test sample manufactured by resin transfer moulding process (RTM) was tested. A 3D progressive damage model developed in ABAQUS/Standard was used to simulate the failure of the π-joint. Based on good correlation of failure load and damage distribution between experimental results and FE prediction, further investigation was extended to the effect of two primary assembly clearances on mechanical behavior of the π-joint. The study results reveal that delamination of the fillet region in L-preform is the π-joint's failure mode. Moreover, the assembly clearances have little effect on the failure load of the joint.

Ductile fracture model in the shearing process of zircaloy sheet for nuclear fuel spacer grids

Features of sheared edges are predicted based on material properties of Zircaloy obtained from the tensile test and ductile fracture model such as the Gurson-Tvergaard-Needleman (GTN) and Johnson-Cook models. The sheared edges formations are numerically analyzed in each ductile model. An appropriate ductile fracture model is selected to study the relative depth of sheared edges with respect to process parameters. The tendency of failure parameters that are affected by sheared edges and fracture duration is investigated. We applied changes on parameters of failure models to show that the punch force curve and the ratio of characteristic lengths could be coincided, which led us to conclude that the GTN and Johnson-Cook models are equivalent. In the Johnson-Cook model, however, the characteristic length of the sheared edges does not change as each failure parameter reaches a critical value. Hence, the FE prediction model for forming defects is developed using the GTN failure model. Finally, the characteristic length of sheared edges have been measured using the FE prediction model for shearing process parameters such as punch velocities, clearance, and tool wear. Our results showed that the punch-die clearance is the most significant factor that affects forming defects when compared to other factors.

FE Prediction of Hysteretic Component of Rubber Friction

The hysteretic part of the friction coefficient for rubber sliding on an ideal rigid, rough surface has been investigated by FE technique. The FE models were created by using two different FE softwares, ABAQUS and MSC.MARC. The surface roughness has been considered by using two different sine waves having a wavelength of 100 μm and 11.11 μm, as well as their superposition. Parameters of the viscoelastic material models of the rubber were gained, firstly from a fit to the measured storage modulus, secondly from a fit to the measured loss factor master curve of the rubber. The effect of viscoelastic material models, comparing 10-term and 40-term generalized Maxwell models was also considered together with the temperature effect between −50 and150°C. According to the results, both postprocessing methods, namely, the reaction force and the energy-based approach, show very similar coefficients of friction. The 40-term Maxwell model fitted to both the storage modulus and loss factor curve provided the most realistic results. The tendency of the FE results has been explained by semianalytical theory.

FE prediction of residual stresses of investment casting in a Bottom Core Vane under equiaxed cooling

This paper presents an investment casting simulation of the Bottom Core Vane (BCV) component of an aero-engine subjected to equiaxed cooling. The simulation is carried out using the Finite Element Method (FEM). Two FE codes (ABAQUS and ProCAST) are used for performing the simulation and the results from both codes are compared and reviewed. The main goal is to predict the residual stresses in the BCV in a casting simulation. Inconel 718 material is used for the ingot (BCV) and a ceramic material is used for the mould. The gap formation and the gap conductance models, between the ingot and the mould, are implemented into the Finite Element Analyses (FEA). Sensitivity studies of the mechanical calculations in ABAQUS and ProCAST, mould filling, mould materials, convection and friction coefficients are conducted to investigate their influences on the predicted residual stresses.

Application of advanced Grammatical Evolution to function prediction problem

Grammatical Evolution (GE) is one of the evolutionary algorithms to find functions and programs, which can deal according to a syntax with tree structure by one-dimensional chromosome of Genetic Algorithm. An original GE starts from the definition of the syntax by means of Backus Naur Form (BNF). Chromosome in binary number is translated to that in decimal number. The BNF syntax selects according to the remainder of the decimal number with respect to the total number of candidate rules. In this study, we will introduce three schemes for improving the convergence property of the original GE. In numerical examples, the original GE is compared in function identification problem with the Genetic Programming (GP), which is one of the most popular evolutionary algorithm to find unknown functions or programs. Three algorithms are compared in Santa Fe trail problem and prediction problem of Nikkei stock average, which finds programs to control artificial ants collecting foods. The results show that the efficiency of schemes depends on the problem to be solved and that the schemes 1 and 2 are effective for Santa Fe trail problem and prediction problem of Nikkei stock average, respectively.

A friction model for dry contacts during metal-forming processes

In a metal-working process, the friction between the material and the tools influences the process by modifying the strain distribution of the workpiece. This frictional behavior is often taken into account by using a constant coefficient of friction in the finite element simulations. However, friction coefficient varies in time and space with many parameters. This paper aims at modeling of friction in dry contacts which happens at metal-forming processes in the lack of lubricant. The coefficient of dry friction in this model is a function of contact area ratio (which is a function of surface contact characteristics) and strain hardening exponent (material property). The V-bending process of aluminum alloy 6061-T4 sheets was studied experimentally and numerically using ABAQUS/Standard with two kinds of friction models: Coulomb friction and newly developed dry friction models. The results clearly showed that the developed dry friction model has better results in predicting load-stroke curves and springback compared to traditional Coulomb friction model. The FE prediction error for 6061-T4 AA is 16.9% using Coulomb friction model and 9.2% using dry friction model.

A parametric FEA system for fixturing of thin-walled cylindrical components

Machining of thin-walled components has increasingly become a difficulty for manufacturers. Advanced digital analyses have been developed by many researchers to model, predict and reduce errors induced by machining processes. Fixtures for thin-walled components to increase the rigidity of components, improve dynamic performance and reduce machining cost have been widely used in industries. However, modelling to simulate the impact of fixture on the quality of thin-walled components is seldom reported. Moreover, today's machining shop floors, characterized by a large variety of products in small batch sizes, require flexible simulation tools that can be quickly reconfigured. Parametric technology is a key to implement it. This paper proposed a parametric finite element analysis (FEA) system that can automatically mesh components, assign material properties and boundary condition, and create FEA files ready for calculation with limited human interference. The system is focused on thin-walled cylindrical components, including straight thin-walled cylinder, conic thin-walled cylinder and angle-varying thin-walled cylinder. Based on the FE prediction, whether or not a fixture is required and the impact of a support fixture on the component quality can be assessed.