scholarly journals Inverse Parameter Identification for Hyperelastic Model of a Polyurea

Polymers ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 2253
Author(s):  
Yihua Xiao ◽  
Ziqiang Tang ◽  
Xiangfu Hong
Keyword(s):  
Response Surface ◽  
Inverse Method ◽  
Strain Curve ◽  
Uniaxial Stress ◽  
Surface Model ◽  
Fe Model ◽  
Compression Tests ◽  
Material Parameters ◽  
Hyperelastic Model ◽  
Inverse Procedure

An inverse procedure was proposed to identify the material parameters of polyurea materials. In this procedure, a polynomial hyperelastic model was chosen as the constitutive model. Both uniaxial tension and compression tests were performed for a polyurea. An iterative inverse method was presented to identify parameters for the tensile performance of the polyurea. This method adjusts parameters iteratively to achieve a good agreement between tensile forces from the tension test and its finite element (FE) model. A response surface-based inverse method was presented to identify parameters for the compression performance of the polyurea. This method constructs a radial basis function (RBF)-based response surface model for the error between compressive forces from the compression test and its FE model, and it employs the genetic algorithm to minimize the error. With the use of the two inverse methods, two sets of parameters were obtained. Then, a complete identified uniaxial stress–strain curve for both tensile and compressive deformations was obtained with the two sets of parameters. Fitting this curve with the constitutive equation gave the final material parameters. The present inverse procedure can simplify experimental configurations and consider effects of friction in compression tests. Moreover, it produces material parameters that can appropriately characterize both tensile and compressive behaviors of the polyurea.

Optimization of a Closed Die Forging Process to Manufacture a Gear Wheel by the Use of a Response Surface Model

2014 ◽  
Vol 922 ◽  
pp. 254-259
Author(s):  
Thomas Henke ◽  
Gerhard Hirt ◽  
Markus Bambach
Keyword(s):  
Response Surface ◽  
Cross Section ◽  
Load Capacity ◽  
Hot Forging ◽  
Response Surface Model ◽  
Process Efficiency ◽  
Surface Model ◽  
Fe Model ◽  
Forging Process ◽  
Grain Sizes

Heavy-duty components used in the automotive industry, in wind turbines and in many other industrial applications are often produced using hot forging processes. Nowadays the design of hot forging processes aims for the optimization of process efficiency on the one hand and final mechanical product properties on the other hand. Excellent mechanical properties needed for hot-forged components e.g. high load capacity and high fatigue resistance depend on a fine homogeneous microstructure distribution across the final product’s cross-section. Efficiency in hot forging can be optimized by increasing the temperature during processing, which allows for lower forging loads and lower die stresses, thus improving die life in terms of mechanical fatigue. To guarantee for a fine homogenous microstructure across the cross section of the forged good, dynamic recrystallization (DRX) has to be initiated during deformation and Grain Growth (GG) has to be avoided during dwell times and cooling. Due to the high computational costs of finite element simulations an optimization aiming for lowest possible forging loads and finest possible grain sizes is very time-consuming. In this paper a Response Surface Model (RSM) of the forging process is introduced, which allows for much faster evaluation of the outcome of forging simulations, albeit by interpolation of simulation results, and thus allows for optimization. The information required to create the RSM is obtained by Design Of Experiments (DOE) techniques using an FE-model of the forging process which was calibrated earlier. The process variables considered include the initial temperature of the billet and the die kinematics. Subsequently, an optimization algorithm is combined with the RSM to find the design variables giving minimum possible loads during deformation and finest possible grain sizes in the forged product. The RSMs results are validated by the use of the existing FE-model.

scholarly journals Inverse Method to Determine Fatigue Properties of Materials by Combining Cyclic Indentation and Numerical Simulation

2020 ◽  
Author(s):  
Hafiz Muhammad Sajjad ◽  
Hamad ul Hassan ◽  
Matthias Kuntz ◽  
Benjamin Josef Schäfer ◽  
Petra Sonnweber-Ribic ◽  
...  
Keyword(s):  
Material Properties ◽  
Inverse Method ◽  
Fatigue Testing ◽  
Kinematic Hardening ◽  
Pure Copper ◽  
Uniaxial Stress ◽  
Cyclic Plasticity ◽  
Fatigue Properties ◽  
Stress Strain ◽  
Material Parameters

The application of instrumented indentation to assess material properties like Young’s modulus and micro-hardness has become a standard method. In recent developments, indentation experiments and simulations have been combined to inverse methods, from which further material parameters as yield strength, work hardening rate, and tensile strength can be determined. In this work, an inverse method is introduced by which material parameters for cyclic plasticity, i.e. kinematic hardening parameters, can be determined. To accomplish this, cyclic Vickers indentation experiments are combined with finite element simulations of the indentation with unknown material properties, which are then determined by inverse analysis. To validate the proposed method, these parameters are subsequently applied to predict the uniaxial stress-strain response of a material with success. The method has been validated successfully for a quenched and tempered martensitic steel and for technically pure copper, where an excellent agreement between measured and predicted cyclic stress-strain-curves has been achieved. Hence, the proposed inverse method based on cyclic nanoindentation, as a quasi-non-destructive method, could complement or even substitute the resource-intensive conventional fatigue testing in the future for some applications.

scholarly journals Parametric Analysis on Landing Gear Strut Friction of Light Aircraft for Touchdown Performance

2021 ◽  
Vol 11 (12) ◽  
pp. 5445
Author(s):  
Shengyong Gan ◽  
Xingbo Fang ◽  
Xiaohui Wei
Keyword(s):  
Response Surface ◽  
Energy Absorption ◽  
Landing Gear ◽  
Surface Model ◽  
Absorption Properties ◽  
Surface Method ◽  
Design Variables ◽  
Landing Impact ◽  
Landing Response ◽  
Parametric Studies

The aim of this paper is to obtain the strut friction–touchdown performance relation for designing the parameters involving the strut friction of the landing gear in a light aircraft. The numerical model of the landing gear is validated by drop test of single half-axle landing gear, which is used to obtain the energy absorption properties of strut friction in the landing process. Parametric studies are conducted using the response surface method. Based on the design of the experiment results and response surface functions, the sensitivity analysis of the design variables is implemented. Furthermore, a multi-objective optimization is carried out for good touchdown performance. The results show that the proportion of energy absorption of friction load accounts for more than 35% of the total landing impact energy. The response surface model characterizes well for the landing response, with a minimum fitting accuracy of 99.52%. The most sensitive variables for the four landing responses are the lower bearing width and the wheel moment of inertia. Moreover, the max overloading of sprung mass in LC-1 decreases by 4.84% after design optimization, which illustrates that the method of analysis and optimization on the strut friction of landing gear is efficient for improving the aircraft touchdown performance.

An Adaptive Response Surface Method Using Bayesian Metric and Model Bias Correction Function

2014 ◽  
Vol 136 (3) ◽  
Author(s):  
Lei Shi ◽  
Ren-Jye Yang ◽  
Ping Zhu
Keyword(s):  
Response Surface ◽  
Bias Correction ◽  
Adaptive Response ◽  
Large Scale ◽  
Correction Method ◽  
Data Uncertainty ◽  
Correction Function ◽  
Surface Model ◽  
Model Bias ◽  
Adaptive Response Surface Method

The Bayesian metric was used to select the best available response surface in the literature. One of the major drawbacks of this method is the lack of a rigorous method to quantify data uncertainty, which is required as an input. In addition, the accuracy of any response surface is inherently unpredictable. This paper employs the Gaussian process based model bias correction method to quantify the data uncertainty and subsequently improve the accuracy of a response surface model. An adaptive response surface updating algorithm is then proposed for a large-scale problem to select the best response surface. The proposed methodology is demonstrated by a mathematical example and then applied to a vehicle design problem.

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