Checking of convergence of the finite element model for the software complex Simufact Welding with the real experiment using the method of additive manufacturing WAAM

A new dynamic model of rotor–blade systems

Adaptive Methoden zur Pfadverfolgung bei Entfestigung

Mechanical behavior properties were investigated for cabinet-type cabinet doors in kitchen furniture and drawer bottoms and joints used as storage areas under load in accordance with relevant standards (BS EN 16122). In the first stage, values physical and mechanical for particle board (PB) and medium-density fiberboard (MDF) were determined. According to the test results in the second stage, it was determined that the doors assembled using a torque of 1.3 N/m in the door tests were less deformed than those assembled with 0.63 N/m. According to the finite element analysis and real test results carried out in the final stage, it has been determined that the vertical loading analysis applied on the doors coincides with the real experiments by 85%, horizontal loading by 84%, and slam shut by 50%. The doors didn’t pass the final stage durability test in real experiments, and the analysis results revealed that the deformation areas were the same as for real experiment. In the drawers; strength 85%, displacement 84%, and slam shut 94% overlap are represented. The drawers completed the durability test in real experiments, and in the analysis, it was determined that the deformation that occurred under high stresses was in the same areas.

There are significant quality and reliability problems for components/products made by additive manufacturing (AM) due to various reasons. Selective laser melting (SLM) process is one of the popular AM techniques and it suffers from low quality and reliability issue as well. Among many reasons, the lack of accurate and efficient models to simulate the SLM process could be the most important one because reliability and quality quantification rely on accurate models; otherwise, a large number of experiments should be conducted for reliability and quality assurance. To date, modeling techniques for the SLM process are either computationally expensive based on finite element (FE) modeling or economically expensive requiring a significant amount of experiment data for data-driven modeling. This paper proposes the integration of FE and data-driven modeling with systematic calibration and validation framework for the SLM process based on limited experiment data. Multi-fidelity models are the FE model for the SLM process and a machine learning model constructed based on the FE model instead of real experiment data. The machine learning model, after incorporation of the learned physics from the FE model, is then further improved based on limited real experiment data through the calibration and validation framework. The proposed work enables the development of highly efficient and accurate models for melt pool prediction of the SLM process under various configurations. The effectiveness of the framework is demonstrated by real experiment data under 14 different printing configurations.

The effect of percent foam fill ratio on the energy absorption capacity of axially compressed thin-walled multi-cell square and circular tubes

In order to improve the weldability of invar alloy, temperature field distribution is studied using finite element analysis method and real experiment under different welding parameters and different thickness of welded materials. Temperature field distribution and welding penetration is also disclosed by real experiment and finite element method analysis. Finally, the effect of welding parameters on temperature distribution and welding penetration is discussed. The results show that Ni48Fe52 invar alloys can be welded to carbon steel with full penetration using welding parameters of I = 132 A, U = 17.1 V, v = 4 mm/s, and δ = 1.88 mm.

One of the most common issues affecting the performance and reliability of a welded junction is distortion. The welding sequence has been found to have a considerable impact on distortions. In this regard, weld Sequence Optimization (WSO) is useful for preventing these constraints at the early designing phase, and minimizing total capital costs in the manufacturing sectors. Welding processes are usually decided by skilled welders, and in certain circumstances, an experimental strategy may be required. In the present study, realistic experimentation, simulation software, and Artificial Neural Networks (ANN) are used for minimizing the distortion in a complex structure. Experimentation is conducted using Gas Metal Arc Welding for a dissimilar joint from Stainless Steel SS304 with Mild Steel AISI1008, and the Finite Element Model (FEM) was created using MSC Simufact Welding solver and confirmed through a variety of trials. The objectives of this paper is to develop and test a practical strategy for predicting distortion induced during welding process and WSO employing ANN model by hot-encoding. The finding revealed that the distortion is reduced by 87.7 % from the maximum distortion obtained during the welding process.

A rock pocket is a deficient volume within hardened concrete consisting of coarse aggregate and voids that reduce the overall stiffness of the concrete members. The leakage of wet concrete from the form, segregation, or insufficient consolidation during concrete placement may leave rock pockets in concrete construction. This study is concerned with the detection, location and quantification of internal defects, particularly rock pockets, in reinforced concrete members. This is achieved by coupling in situ vibration testing with finite element analysis through Bayesian inference. First, the importance of providing sufficient physical evidence while calibrating the finite element models is illustrated using simulated experiments. With simulated experiments, model calibration successfully detected not only the locations but also the severity of rock pocket defects. Then, the results of impact hammer tests, completed on a concrete beam defected with rock pockets are presented. The finite element model of the test beam is segmented, and the stiffness properties of these segments are independently calibrated with the help of Bayesian calibration techniques using varying amounts of experimental information. The success in detecting defects obtained using simulated experiments, was not observed when the procedure is applied to the scaled concrete beams tested under laboratory conditions. However, the cause(s) of the poor performance with real experiments can be attributed to several factors, each of which requires further evaluation. (Publication approved for unlimited, public release on November-4- 2009, Unclassified.)KeywordsPosterior DistributionMarkov Chain Monte CarloMode ShapeDefect DetectionConcrete BeamThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Material parameter identification of the elementary ply damage mesomodel using virtual micro-mechanical tests of a carbon fiber epoxy system

The results of a numerical experiment are presented, within the framework of which finite element modeling of the reinforced concrete beams support sections with external composite reinforcement beams inclined sections operation was carried out, as well as their comparison with the results of an experiment carried out on real laboratory samples, similarly reinforced on the support areas by external composite U-shaped cross clamps. The strength of inclined sections of both real laboratory specimens and their design models was considered. Both numerical and real experiments were carried out at three values of the shear fracture span: 1.5h0; 2.0h0 and 2.5h0. The obtained results of the numerical experiment are compared with the results of laboratory tests on beams in order to analyze the accuracy of the calculation results and to find the optimal method for modeling and taking into account the external composite reinforcement clamps in the design models that would most accurately reflect their contribution to the operation of the inclined section of bent reinforced concrete elements. For the numerical experiment, the method of finite element modeling of samples was used. An assessment of the method used for computational modeling of the operation of inclined sections reinforced with external composite reinforcement is given.

The development of robust prediction tools based on machine learning (ML) techniques requires the availability of complete, consistent, accurate, and numerous datasets. The application of ML in structural engineering has been limited since, although real size experiments provide complete and accurate data, they are time-consuming and expensive. On the other hand, validated finite element (FE) models provide consistent and numerous synthetic data. Depending on the complexity of the problem, they might require large computational time and cost, and could be subjected to uncertainties and limitation in prediction capability given they are approximations of real-world problems. Hybrid approaches to combine experimental and synthetic datasets have emerged as an alternative to improve the reliability of ML model predictions. In this paper, we explore two hybrid methods to propose a robust approach for the prediction of the extended hollo-bolt (EHB) connection strength, stiffness, and column face displacement: (1) supervised ML methods with data fusion (DF) where learning is optimized with particle swarm optimization (PSO), and (2) artificial neural networks (ANN) based method with model fusion (MF). Based on the analysis of a dataset that combines 22 tensile experimental results with 2000 synthetic datapoints based on FE models, we concluded that using the first method (ML with DF and PSO) is the most suitable method for the prediction of the connection behavior. The ANN-based method with MF shows to be a promising method for the characterization of the EHB connection, however, more extensive experimental data is required for its implementation. Finally, a graphical user interface application was developed and shared in a public repository for the implementation of the proposed hybrid model.

Optimal sensor placement and estimator-based temperature control for a deep drawing process

Model updating improves the correlation between the response of the real structure and the response of the finite-element (FE) model; however, the selection of the updating parameters (parametrization) is crucial for its success. Using full-field modal shapes, a large number of parameters can be updated, e.g., the Young’s moduli of all the finite elements; however, the structural response is not necessarily sensitive to an arbitrary parameter, making the optimization problem ill-conditioned. Additionally, the computation of the full sensitivity matrix is not feasible for relatively large FE models. Not all locations are equally important for model updating; at locations of the highest mechanical loads, more focus is required. In this research, the updating parameters are based on the curvature of the 3D full-field experimental shape, where locations with high curvature are associated with high sensitivity. The assumption is initially researched with the Euler–Bernoulli beam elements and second-order tetrahedrons. The proposed method is investigated on numerical and real experiments, where successful updating was confirmed. With the proposed parametrization and updating approach, a geometrically complex structure is parametrized and the parameters updated without significant user input, generalizing the model-updating procedure.

In many scientific areas, non‐stochastic simulation models such as finite element simulations replace real experiments. A common approach is to fit a meta‐model, for example a Gaussian process model, a radial basis function interpolation, or a kernel interpolation, to computer experiments conducted with the simulation model. This article deals with situations where more than one simulation model is available for the same real experiment, with none being the best over all possible input combinations. From fitted models for a real experiment as well as for computer experiments using the different simulation models, a criterion is derived to identify the locally best one. Applying this criterion to a number of design points allows the design space to be split into areas where the individual simulation models are locally superior. An example from sheet metal forming is analyzed, where three different simulation models are available. In this application and many similar problems, the new approach provides valuable assistance with the choice of the simulation model to be used. Copyright © 2011 John Wiley & Sons, Ltd.

Based on the studied literature, the aim was to investigate the influence of the volume fraction of meat in meat dumplings (pelmeni) and the number of dumplings on their cooking time, as well as to analyze the physico-mathematical and technical aspects to be taken into account (or not) within the framework of the planned experiment. To achieve this goal, the following tasks were set: to conduct experimental studies, to describe the assumptions that need to be accepted when performing the study and to conduct theoretical studies (numerical experiments, using the finite element method). When conducting experiments, dumplings of well-known brands, the names of which were conventionally labeled as “SK”, “SZ”, “RE”, “TS”, were chosen as objects of research. The need to study the dumplings of well-known brands is justified by both consumer interest and a smaller variability of product parameters, which increases the reproducibility of the results of the study. As assumptions adopted in real experiments, the authors emphasize the following ones: distilled water is used in cooking, the volume of a dumpling does not change during cooking, the biochemical composition of the filling can be neglected. As a result of the theoretical analysis, the following assumptions were made: the study of dumpling cooking does not take into account the resistance forces, i. e., turbulent and convective flows, as well as thermo-elastic effects; the time of dumpling floating to the surface is negligibly small; "approximation of initial values" — geometric, physico-chemical parameters of the dumpling do not change during cooking. Cooking time of 1, 5, 10 dumplings of known brands was obtained on the basis of numerical experiments. Using real experiments, the time from immersion of the dumplings in water to their floating to the surface was investigated. As a result of numerical experiments it has been found that the time of heating the center of the dumpling of known brands to the temperature of protein denaturation differs from the time of floating to the surface by about 360 seconds. Consequently, after floating to the surface, the dumplings of known brands need about six more minutes of cooking time until they are fully cooked.