Finite Element Analysis Process Research Articles

Machine learning based finite element analysis (FEA) surrogate for hip fracture risk assessment and visualization

Classical computational biomechanical approaches are costly and require a high level of expertise, often limiting their clinical application. A data-driven Machine Learning (ML) framework can serve as an effective alternative for disease diagnosis and prediction. This study aimed to develop an ML-based modeling approach integrating Quantitative Computed Tomography-based Finite Element Analysis (QCT-based FEA) to assess fall-induced hip fracture probability and probable facture locations in an elderly individual. Specifically, this study focused on predicting patient-specific hip fracture risk using fracture risk index (FRI), calculated on the basis of 1st and 3rd principal strains, and visualizing strain distributions in the proximal femur by generating surrogate FE models via supervised CatBoost model. The training dataset, comprising clinical, anatomical, and mechanical (loading) features, was obtained from QCT image data and QCT-based FEA, which also provided the FRI and strain distributions as targets. The optimized CatBoost model demonstrated 76% accuracy with an AUROC of 0.81 in fracture risk prediction, as well as correlation coefficients of 0.73 for the 1st principal strain and 0.76 for the 3rd principal strain in the surrogate FE models for visualization. Although trained on a limited dataset, this study highlights the efficacy of ML-based surrogate modeling in the QCT-based FEA process for predicting hip fracture risk and visually identifying fracture locations.

Influence of earthquake vertical excitations on Sloshing-Created P-Δ effect in elevated water Tanks: Experimental Validation, numerical simulation and proposing a modification for Housner model

Seismic sloshing in elevated water tanks creates some additional base moment, which simultaneous with the effect of strong vertical acceleration of near-source earthquakes may increase the P-Δ effect, as an important factor in seismic design of these structures. This study was performed to investigate the extent of this combined effect. First, an experiment was conducted to provide a set of results for validating the finite element analysis (FEA) modeling, conducted as the second part of the study. The test tank was cylindrical with a diameter of 60 cm, installed on an innovative simple shaking table, capable of creating simultaneous horizontal and vertical harmonic excitations. Different frequencies and water depths were used in the tests. For verification, the maximum sloshing height was measured and compared with FEA results. The verified FEA process was used to simulate elevated tanks of actual sizes for various water height to tank diameter and also tank height to tank diameter ratios, using three-component accelerograms of seven selected earthquakes, mostly having strong vertical components, creating totally 126 dynamic FEA cases. In the last stage, a modification was done on the Housner spring-mass model for using it in 3D state with consideration of vertical excitations. Numerical results indicate that the sloshing height and the base moment could increase in average about 20% and 10%, respectively, due to the effect of earthquake vertical excitations. Also, comparison of FEA results with those obtained by using the proposed 3D modified Housner model shows a good agreement of around 90% on average.

Vibration Reliability Analysis of Drum Brake Using the Artificial Neural Network and Important Sampling Method

This research aims to evaluate the calculation accuracy and efficiency of the artificial neural network-based important sampling method (ANN-IS) on reliability of structures such as drum brakes. The finite element analysis (FEA) result is used to establish the ANN sample in ANN-based reliability analysis methods. Because the process of FEA is time-consuming, the ANN sample size has a very important influence on the calculation efficiency. Two types of ANNs used in this study are the radial basis function neural network (RBF) and back propagation neural network (BP). RBF-IS and BP-IS methods are used to conduct reliability analysis on training samples of three different sizes, and the results are compared with several reliability analysis methods based on ANNs. The results show that the probability of failure of the RBF-IS method is closer to that of the Monte-Carlo simulation method (MCS) than those of other methods (including BP-IS). In addition, the RBF-IS method has better calculation efficiency than the other methods considered in this study. This research demonstrates that the RBF-IS method is well suited to structure reliability problems.

Determining the optimum heating time of small sized test specimen made from weldable mild steel

The usage of CHT (Continuous Heating Transformations) diagrams for a given steel or equivalent grade, requires knowledge of heating temperature, average heating rate, and the heating time. Definition of these technological parameters are primarily based on the complex relationship system between the geometric, thermal parameters and the heating device. In our research, we mainly focused on those physical key parameters that can mostly influence the heating and transformation rate. These parameters provide realistic, usable data for analysing the process of thermal diffusion and FEA (Finite Element Analysis) tests. During analysis, an easy-to-use function relationship was determined for approaching the heating rate more precisely. This method allows handling the CHT charts easily, within a selected range, regarding weldable mild steels.

Learning advanced engineering online: from distance delivery to online learning of finite element analysis

ABSTRACTThis paper reviews an Open University module for distance learning of finite element analysis (FEA) in MEng degree qualifications recognised for Chartered status. Presentation has evolved from physical hard copy study packs to fully online. The emphasis is on understanding limits and assumptions behind all aspects of FEA for safe and effective use. Guided exercises bring out modelling and analysis issues in real systems. Case studies of a Formula 1 racing car focus on a stepped approach illustrating the FEA process. The paper outlines the assessment processes and teaching philosophies, expected outcomes, marking, handling and processing of students’ final results. The paper gives observations of many years of presenting education modules in FEA for study at a distance. It gives recommendations for module design, content and styles of presentation, and an evaluation of online study compared with traditional methods. Challenges include providing text, video sequences and complicated mathematics in online form for use on different access devices.

Finite Element Analysis Process Reuse Method based on Integrated Information Model

Aiming at the problems such as low efficiency of the simulation analysis, difficulty in information reuse between similar structures, an Finite Element Analysis (FEA) process reuse method based on the modular thought is put forward. The reuse process of FEA is described by the methods of module division and reorganization, and the FEA structural information is expressed using Extensive Makeup Language (XML). Macros and macro library technology of ANSYS are used to construct reusable modules. For the achievement of the module reuse in FEA process, the secondary development technology is conducted by combined with VC + + language with APDL parametric language. The approach is plied in nuclear power valve products through building FEA process reuse platform, which increased the efficiency of simulation analysis in valve products development.

An Expert System for 2D Stress Analysis of Human Teeth

The concepts of rapidly and automatically obtaining the stresses found in specific human teeth and performing quick parametric studies has been a persistent goal in bioengineering. Finite element analysis (FEA) is a powerful approach for human tooth stress analysis and has been applied to various dental researches. However, the whole FEA process is a major challenge to dental researchers since special simulation skills and expert knowledge in mechanics are required for constructing proper models and obtaining accurate results. This paper presents an expert system that permits dentists with no engineering expertise to obtain stress distributions accurately and rapidly for various dental applications. The expert system for 2D stress analysis of human teeth is developed on the customization platforms provided by commercial computer aided design (CAD) software (ProEngineer Wildfire 3.0, Parametric Technology Corporation, Needham, MA, USA) and FEA software (MD Patran R2, MSC Software Corporation, CA, USA), respectively. A knowledge based system is built for helping users carry out the whole FEA process automatically. Graphical user interfaces (GUIs) are constructed for inputting the necessary tooth information such as dimensions, material properties, etc. An orthodontic application is presented for showing how the expert system works. The approaches developed and applied here can be expanded to provide automatic stress analyses for many other applications as well.

Research on the Parametric Varying Mechanism and Modeling of Accelerometer in Storage Condition

Based on the study of the effect of parametric variation of quartz flexible accelerometer in storage condition, the varying mechanism and causes of bias and scale factor varying were analyzed. According to the analysis results of mechanism, the parts of finite element modeling and the parameters-input forms were determined. Considering the varying magnetic properties of permanent magnetic material impacted on the accelerometer, an approach of loading the servo-circuit formulas and modeling the quartz flexible accelerometer in ANSYS finite element analysis (FEA) software was given, and the algorithm process of FEA coupled magneto-structural simulation was also given. Finally, one certain accelerometer was simulated by FEA software, and a temperature testing was made. It is tested that the trend of the test under various temperature stress was consistent with that of the above output voltage values which is obtained by finite element simulation. Moreover, it demonstrates the correctness of the modeling and the analysis results.

Numerical investigations on the fatigue failure of forging tools due to thermo-mechanical cyclic loading

During service hot forging dies are exposed to a combination of cyclic thermo-mechanical, tribological and chemical loads. Besides abrasive and adhesive wear on the die surface, fatigue crack initiation with subsequent fracture is one of the most frequent cause of failure. Fatigue cracks in forging dies are caused by local elasto-plastic strains due to cyclic mechanical and thermal loads. Aiming at a reduction of tool and production costs of forgings, tool life extension is necessary. In order to reach this goal, process design and process optimisation are currently done with the help of the finite element analysis (FEA). An integral part of this procedure is the tool design based upon fatigue life maximisation of forging dies in the Low-Cycle-Fatigue (LCF) region. Within the framework of this research the material fatigue damage of a forging die due to cyclic thermo-mechanical loads is analysed based on FEA process simulations. Besides this a material model which considers cyclic elasto-plastic material behaviour was used to describe the constitutive behaviour of the forging dies. The results of a fatigue life damage analysis applied to an industrial forging process in which the lower die shows a clear fatigue crack initiation. The crack initiation sites predicted with the FEA process simulations agree with the ones observed on the real die component.

Interactive visualisation: A support for system identification

Due to the complex and composite nature of the material constituents that the masonry is composed of, accurate analytical modelling of masonry wall panels has proved to be extremely difficult. In practice, engineers are using empirical models such as the yield line or strip analyses methods to predict the response of masonry panels to lateral loads. In research, specialised finite element analysis (FEA) tools have been used, which is mostly limited to a smeared material model over the entire surface of the panel. This is an over simplification of the true behaviour of the masonry as a composite material. In order to overcome this problem, previous research at the University of Plymouth resulted in the development of a numerical model updating process in which ‘corrector factors’ are derived by minimising the error between the theoretical and analytical load deflection relationships over the entire surface of the panel. These corrector factors are used to modify the flexure rigidity of various zones within the panel for use in a non-linear FEA process. This technique has proved to be effective in predicting the response of the masonry panels to lateral loads, which was comparable with the experimental results. However, two key drawbacks of this method were the complexity of deriving corrector factors for panels of different sizes and for panels with and without openings. Additionally, due to the inverse problem nature of the model updating process, a number of scenarios could result in similar error. Initial investigation into the pattern of corrector factors revealed that panel boundaries had a major influence on the behaviour of the panels. Hence, this led the research team to investigate modelling panel boundaries as springs without changing the material properties. The authors used an interactive visualisation clustering genetic algorithm tool (IVCGA). The search engine of the IVCGA uses the GA to derive values for spring constants along the boundaries of the panel and the visualisation tools within the IVCGA identifies clusters of good solutions from the data generated by the GA. This process is then repeated for each objective independently and a set of clusters that are mutually inclusive for all objectives are identified. The paper demonstrates that further exploration of solutions inside this mutually inclusive region can lead to identification of solutions that perform much better than the previous approaches and this could result in finding the correct model for the problem. This new approach of has successfully overcome the drawbacks of the previous research.

Finite Element Analysis and Structure Optimization for Improving the Fatigue Life of Rubber Mounts

The fatigue crack problem of a rubber mount on the crankshaft of a wheel in an automobile was investigated by theoretical calculation and experimental analysis. A finite element analysis (FEA) model of the rubber mount was developed and analyzed using the software MSC.MARC. FEA results imply that stress concentration may arise on the interfaces of the rubber layer and metal layer. Modifications were made to the structure parameters and rubber material of the rubber mount based on the analysis of FEA results. The stress concentration of the rubber mount at the rubber and metal interfaces was improved and the fatigue life of the improved rubber mount was increased. Finally, experiments were made to validate the accuracy of the FEA process and the reliability of the improved method. The proposed FEA and improved method can shorten the product design cycle, decrease the design and trial‐product cost and remarkably improve the product quality.

Finite Element Analysis and Structure Optimization for Improving the Fatigue Life of Rubber Mounts

The fatigue crack problem of a rubber mount on the crankshaft of a wheel in an automobile was investigated by theoretical calculation and experimental analysis. A finite element analysis (FEA) model of the rubber mount was developed and analyzed using the software MSC.MARC. FEA results imply that stress concentration may arise on the interfaces of the rubber layer and metal layer. Modifications were made to the structure parameters and rubber material of the rubber mount based on the analysis of FEA results. The stress concentration of the rubber mount at the rubber and metal interfaces was improved and the fatigue life of the improved rubber mount was increased. Finally, experiments were made to validate the accuracy of the FEA process and the reliability of the improved method. The proposed FEA and improved method can shorten the product design cycle, decrease the design and trial‐product cost and remarkably improve the product quality.