scholarly journals Considering multiple process observables to determine material model parameters for FE-cutting simulations

2021 ◽  
Author(s):  
Marvin Hardt ◽  
Thomas Bergs
Keyword(s):  
Chip Thickness ◽  
Material Model ◽  
Experimental Investigations ◽  
Model Parameters ◽  
Inverse Approach ◽  
Local Material ◽  
Novel Approach ◽  
Validation Parameters ◽  
Multiple Process ◽  
Cutting Simulations

AbstractAnalyzing the chip formation process by means of the finite element method (FEM) is an established procedure to understand the cutting process. For a realistic simulation, different input models are required, among which the material model is crucial. To determine the underlying material model parameters, inverse methods have found an increasing acceptance within the last decade. The calculated model parameters exhibit good validity within the domain of investigation, but suffer from their non-uniqueness. To overcome the drawback of the non-uniqueness, the literature suggests either to enlarge the domain of experimental investigations or to use more process observables as validation parameters. This paper presents a novel approach merging both suggestions: a fully automatized procedure in conjunction with the use of multiple process observables is utilized to investigate the non-uniqueness of material model parameters for the domain of cutting simulations. The underlying approach is two-fold: Firstly, the accuracy of the evaluated process observables from FE simulations is enhanced by establishing an automatized routine. Secondly, the number of process observables that are considered in the inverse approach is increased. For this purpose, the cutting force, cutting normal force, chip temperature, chip thickness, and chip radius are taken into account. It was shown that multiple parameter sets of the material model can result in almost identical simulation results in terms of the simulated process observables and the local material loads.

Friction Mechanism of Tread Blocks on Snow Surfaces

1997 ◽  
Vol 25 (4) ◽  
pp. 245-264 ◽  
Author(s):  
R. Mundl ◽  
G. Meschke ◽  
W. Liederer
Keyword(s):  
Numerical Simulations ◽  
Material Model ◽  
Experimental Investigations ◽  
Model Parameters ◽  
Elastoplastic Material ◽  
Compression Tests ◽  
The Finite Element Method ◽  
Friction Mechanism ◽  
Insight Into ◽  
Rubber Block

Abstract Evaluation of tread pattern designs with respect to performance of winter tires on snow is still predominantly based on empirical knowledge. To gain greater insight into the complex interaction between the elastic tread block and the inelastically deforming snow, numerical simulations by means of the Finite Element Method (FEM) were carried out in conjunction with experimental investigations. An elastoplastic material model for snow was developed. Calibration of the model parameters is based on shear and compression tests conducted on specimens made of natural and artificial snow. Good correlation is obtained between results from laboratory experiments and from numerical simulations with respect to the deformations and the frictional behavior of a single rubber block sliding on snow.

scholarly journals On the Application of the Particle Swarm Optimization to the Inverse Determination of Material Model Parameters for Cutting Simulations

Modelling ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 129-148
Author(s):  
Marvin Hardt ◽  
Deepak Jayaramaiah ◽  
Thomas Bergs
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Material Model ◽  
Cutting Process ◽  
Parameter Determination ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Cutting Simulations ◽  
Number Of Iterations

The manufacturing industry is confronted with increasing demands for digitalization. To realize a digital twin of the cutting process, an increase of the model reliability of the virtual representation becomes necessary. Thereby, different models are required to represent the experimental behavior of the workpiece material or frictional interactions. One of the most utilized material models is the Johnson–Cook material model. The material model parameters are determined either by conventional or by non-conventional material tests, or inversely from the cutting process. However, the inverse parameter determination, where the model parameters are iteratively modified until a sufficient agreement between experimental and numerical results is reached, is not robust and requires a high number of iterations. In this paper, an approach for the inverse determination of material model parameters based on the Particle Swarm Optimization (PSO) is presented. The approach was investigated by the inverse re-identification of an initial parameter set. The conducted investigations showed that a material model parameter set can be determined within a small number of iterations. Thereby, the determined material model parameters resulted in deviations of approximately 1% in comparison to their target values. It was shown that the PSO is suitable for the inverse material parameter determination from cutting simulations.

scholarly journals Additive Manufacturing-Based In Situ Consolidation of Continuous Carbon Fibre-Reinforced Polycarbonate

Materials ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2450
Author(s):  
Andreas Borowski ◽  
Christian Vogel ◽  
Thomas Behnisch ◽  
Vinzenz Geske ◽  
Maik Gude ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Carbon Fibre ◽  
Bending Test ◽  
Thermoplastic Composites ◽  
Experimental Investigations ◽  
Material Structure ◽  
Three Point Bending ◽  
Continuous Fibre ◽  
Local Material

Continuous carbon fibre-reinforced thermoplastic composites have convincing anisotropic properties, which can be used to strengthen structural components in a local, variable and efficient way. In this study, an additive manufacturing (AM) process is introduced to fabricate in situ consolidated continuous fibre-reinforced polycarbonate. Specimens with three different nozzle temperatures were in situ consolidated and tested in a three-point bending test. Computed tomography (CT) is used for a detailed analysis of the local material structure and resulting material porosity, thus the results can be put into context with process parameters. In addition, a highly curved test structure was fabricated that demonstrates the limits of the process and dependent fibre strand folding behaviours. These experimental investigations present the potential and the challenges of additive manufacturing-based in situ consolidated continuous fibre-reinforced polycarbonate.

Local material symmetry group for first- and second-order strain gradient fluids

2021 ◽  
pp. 108128652110216
Author(s):  
Victor A. Eremeyev
Keyword(s):  
Symmetry Group ◽  
Strain Gradient ◽  
Constitutive Equations ◽  
Strain Energy ◽  
Mass Density ◽  
Material Model ◽  
Second Order ◽  
Material Symmetry ◽  
Local Material ◽  
Current Mass

Using an unified approach based on the local material symmetry group introduced for general first- and second-order strain gradient elastic media, we analyze the constitutive equations of strain gradient fluids. For the strain gradient medium there exists a strain energy density dependent on first- and higher-order gradients of placement vector, whereas for fluids a strain energy depends on a current mass density and its gradients. Both models found applications to modeling of materials with complex inner structure such as beam-lattice metamaterials and fluids at small scales. The local material symmetry group is formed through such transformations of a reference placement which cannot be experimentally detected within the considered material model. We show that considering maximal symmetry group, i.e. material with strain energy that is independent of the choice of a reference placement, one comes to the constitutive equations of gradient fluids introduced independently on general strain gradient continua.

scholarly journals Explaining Neural Networks Using Attentive Knowledge Distillation

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1280
Author(s):  
Hyeonseok Lee ◽  
Sungchan Kim
Keyword(s):  
Neural Networks ◽  
Model Prediction ◽  
Saliency Map ◽  
Model Parameters ◽  
Learning Capability ◽  
Fine Grained ◽  
Network Layers ◽  
Saliency Maps ◽  
Novel Approach ◽  
Knowledge Distillation

Explaining the prediction of deep neural networks makes the networks more understandable and trusted, leading to their use in various mission critical tasks. Recent progress in the learning capability of networks has primarily been due to the enormous number of model parameters, so that it is usually hard to interpret their operations, as opposed to classical white-box models. For this purpose, generating saliency maps is a popular approach to identify the important input features used for the model prediction. Existing explanation methods typically only use the output of the last convolution layer of the model to generate a saliency map, lacking the information included in intermediate layers. Thus, the corresponding explanations are coarse and result in limited accuracy. Although the accuracy can be improved by iteratively developing a saliency map, this is too time-consuming and is thus impractical. To address these problems, we proposed a novel approach to explain the model prediction by developing an attentive surrogate network using the knowledge distillation. The surrogate network aims to generate a fine-grained saliency map corresponding to the model prediction using meaningful regional information presented over all network layers. Experiments demonstrated that the saliency maps are the result of spatially attentive features learned from the distillation. Thus, they are useful for fine-grained classification tasks. Moreover, the proposed method runs at the rate of 24.3 frames per second, which is much faster than the existing methods by orders of magnitude.

scholarly journals Variation of Passive Biomechanical Properties of the Small Intestine along Its Length: Microstructure-Based Characterization

2021 ◽  
Vol 8 (3) ◽  
pp. 32
Author(s):  
Dimitrios P. Sokolis
Keyword(s):  
Small Intestine ◽  
Orientation Angle ◽  
Axial Direction ◽  
Material Model ◽  
Biomechanical Properties ◽  
Intestinal Wall ◽  
Model Parameters ◽  
Material Models ◽  
Hyper Elastic ◽  
Rat Tissue

Multiaxial testing of the small intestinal wall is critical for understanding its biomechanical properties and defining material models, but limited data and material models are available. The aim of the present study was to develop a microstructure-based material model for the small intestine and test whether there was a significant variation in the passive biomechanical properties along the length of the organ. Rat tissue was cut into eight segments that underwent inflation/extension testing, and their nonlinearly hyper-elastic and anisotropic response was characterized by a fiber-reinforced model. Extensive parametric analysis showed a non-significant contribution to the model of the isotropic matrix and circumferential-fiber family, leading also to severe over-parameterization. Such issues were not apparent with the reduced neo-Hookean and (axial and diagonal)-fiber family model, that provided equally accurate fitting results. Absence from the model of either the axial or diagonal-fiber families led to ill representations of the force- and pressure-diameter data, respectively. The primary direction of anisotropy, designated by the estimated orientation angle of diagonal-fiber families, was about 35° to the axial direction, corroborating prior microscopic observations of submucosal collagen-fiber orientation. The estimated model parameters varied across and within the duodenum, jejunum, and ileum, corroborating histologically assessed segmental differences in layer thicknesses.

scholarly journals Flexural behavior of composite structural insulated panels with magnesium oxide board facings

2020 ◽  
Vol 20 (4) ◽  
Author(s):  
Łukasz Smakosz ◽  
Ireneusz Kreja ◽  
Zbigniew Pozorski
Keyword(s):  
Magnesium Oxide ◽  
Material Model ◽  
Expanded Polystyrene ◽  
Flexural Behavior ◽  
Joint Analysis ◽  
Model Parameters ◽  
Fe Model ◽  
Computational Results ◽  
Four Point Bending ◽  
Proposed Model

Abstract The current report is devoted to the flexural analysis of a composite structural insulated panel (CSIP) with magnesium oxide board facings and expanded polystyrene (EPS) core, that was recently introduced to the building industry. An advanced nonlinear FE model was created in the ABAQUS environment, able to simulate the CSIP’s flexural behavior in great detail. An original custom code procedure was developed, which allowed to include material bimodularity to significantly improve the accuracy of computational results and failure mode predictions. Material model parameters describing the nonlinear range were identified in a joint analysis of laboratory tests and their numerical simulations performed on CSIP beams of three different lengths subjected to three- and four-point bending. The model was validated by confronting computational results with experimental results for natural scale panels; a good correlation between the two results proved that the proposed model could effectively support the CSIP design process.

Evaluation of the Rotor Temperature Distribution of an Automotive Turbocharger Under Hot Gas Conditions Including Indirect Experimental Validation

2021 ◽  
Vol 143 (3) ◽  
Author(s):  
Christopher Zeh ◽  
Ole Willers ◽  
Thomas Hagemann ◽  
Hubert Schwarze ◽  
Jörg Seume
Keyword(s):  
Temperature Distribution ◽  
Heat Flow ◽  
Internal Combustion Engines ◽  
Fluid Film ◽  
Experimental Investigations ◽  
Inlet Temperature ◽  
Hot Gas ◽  
Experimental Identification ◽  
Fluid Film Bearings ◽  
Validation Parameters

Abstract While turbocharging is a key technology for improving the performance and efficiency of internal combustion engines, the operating behavior of the turbocharger is highly dependent on the rotor temperature distribution as it directly modifies viscosity and clearances of the fluid film bearings. Since a direct experimental identification of the rotor temperature of an automotive turbocharger is not feasible at an acceptable expense, a combination of numerical analysis and experimental identification is applied to investigate its temperature characteristic and level. On the one hand, a numerical conjugate heat transfer (CHT) model of the automotive turbocharger investigated is developed using a commercial CFD-tool and a bidirectional, thermal coupling of the CFD-model with thermohydrodynamic lubrication simulation codes is implemented. On the other hand, experimental investigations of the numerically modeled turbocharger are conducted on a hot gas turbocharger test rig for selected operating points. Here, rotor speeds range from 64.000 to 168.000 rpm. The turbine inlet temperature is set to 600 °C and the lubricant is supplied at a pressure of 300 kPa with 90 °C to ensure practically relevant boundary conditions. Comparisons of measured and numerically predicted local temperatures of the turbocharger components indicate a good agreement between the analyses. The calorimetrically determined frictional power loss of the bearings as well as the floating ring speed are used as additional validation parameters. Evaluation of heat flow of diabatic simulations indicates a high sensitivity of local temperatures to rotor speed and load. A cooling effect of the fluid film bearings is present. Consequently, results confirm the necessity of the diabatic approach to the heat flow analysis of turbocharger rotors.

FE-basierte Analyse*/FE-based analysis of the influence of remachining on component characteristics adjusted by forming processes

2018 ◽  
Vol 108 (01-02) ◽  
pp. 41-46
Author(s):  
F. Vogel ◽  
M. Tiffe ◽  
M. Metzger ◽  
D. Prof. Biermann
Keyword(s):  
Simulation Model ◽  
Fe Analysis ◽  
Experimental Investigations ◽  
Model Parameters ◽  
Sequential Process ◽  
Process Chain ◽  
Innovative Material

Bei der Auslegung verknüpfter Prozessschritte zur Herstellung von Bauteilen mit gezielt eingestellten Eigenschaften finden vermehrt FE-basierte Simulationssysteme Anwendung, um den Aufwand experimenteller Untersuchungen insbesondere im Hinblick auf den gesteigerten Einsatz innovativer Werkstoffkonzepte gering zu halten. Im Folgenden wird die Ausarbeitung von Konzepten zur Anpassung von Parametern zur Materialmodellierung sowie zur Verknüpfung von Einzelsimulationen der Prozesskette erläutert.   Regarding the increased application of innovative material concepts in sequential process steps for manufacturing components with tailored properties, the FE-analysis can be used to reduce the effort of experimental investigations. In this article, the development of concepts for the adjustment of simulation model parameters and the conjunction of process chain single simulations are described.

Iron loss simulation using a local material model

2019 ◽  
Vol 38 (4) ◽  
pp. 1224-1234
Author(s):  
Benedikt Groschup ◽  
Silas Elfgen ◽  
Kay Hameyer
Keyword(s):  
A Priori ◽  
Material Model ◽  
Edge Length ◽  
Iron Loss ◽  
Local Magnetization ◽  
Content Type ◽  
Local Loss ◽  
Cut Edge ◽  
Iron Losses ◽  
Local Material

Purpose The cutting process of the electric machine laminations causes residual mechanical stress in the soft magnetic material. A local magnetic deterioration can be observed and the resulting local and global iron losses increase. A continuous local material model for the consideration of the changing magnetization properties has been introduced in a previous work as well as an a priori assessment of iron losses. A local iron loss calculation considering both a local magnetization and local loss parameters misses yet. The purpose of this study is to introduce a local iron loss calculation model considering both a local magnetization and local loss parameters. Design/methodology/approach In this paper, an approach for local iron loss simulation is developed and a comparison to the cut-edge length-dependent loss model is given. The comparison includes local loss distribution in the lamination as well as the impact on the overall motor efficiency and vehicle range in an electric vehicle driving cycle. Findings For an analysis of the resulting local iron loss components, both the local magnetization and iron loss parameters must be considered using physically based models. Consistently, a local iron loss model is presented in the work. The developed model can be used to gain detailed information of the local loss distribution inside the machine. The comparability of this local iron loss with the cut-edge length approach for overall system characteristics, e.g. efficiency or driving range, is shown. Originality/value A local iron loss simulation approach is a physical accurate model to describe the influence of cutting techniques on electric machine characteristics. A comparison with the less complicated a priori assessment gives detailed information about the necessity of the local model under consideration of the given problem.

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