Study on the scale effect of the material properties for thin sheet metals

Die Hauptziele in dieser Arbeit sind die Fließkurvenermittlung und die Untersuchung der Skalierungseffekte von Dünnblechen. Für die Fließkurvenermittlung wurden Zugversuche und Bulgetests im Mikrobereich durchgeführt, wobei für den Zugversuch im Mikrobereich ein verbessertes optisches Messsystem eingebunden worden ist. Um die Fließkurve im zweiachsigen Bereich ermitteln zu können, wurde ein neuartiger Aufbau des Bulgetests entwickelt, der so genannte Aero-Bulgetest. Dieser steht für eine hohe Aussagequalität und eine gute Reproduzierbarkeit der Ergebnisse bei der Bestimmung der Fließkurven von Dünnblechen. Durch die verbesserten Messmethoden lassen sich Werkstoffeigenschaften für Dünnbleche mit einer Dicke bis zu 10 µm erforschen und Skalierungseffekte bezüglich der Fließkurve untersuchen. Dabei wurden verschiedene geometrische Parameter sowohl im Experiment als auch in der Simulation variiert und untersucht.The main objects of this research are to determine the flow curves of very thin sheet metals for the micro metal forming and to study the scaling effects produced by miniaturization from macro to micro area. To determine flow curves in micro area, the tensile tests as well as the bulge tests were performed in this research. To realize it in micro area, an improved optical measurement system was combined with tensile tests, which makes it possible to determine the flow stress curves up to 10 µm sheet thickness. Furthermore, a bulge test equipment called “Aero-Bulgetest” was developed for the determination of flow stress in biaxial stress state. The developed Aero-Bulgetest shows a high accuracy and good repeatability for the determination of the flow curves of very thin sheet metals. With the improved measurement equipment, it is possible to determine the material properties of very thin sheets up to 10 µm thickness and to study the scaling effects of flow stress. The study on scaling effect was performed with various geometrical parameters in experiment as well as in FE-simulation. As a conclusion, the geometrical scaling effects of very thin sheets are shown

Failure Prediction for the Tearing of a Pin-Loaded Dual Phase Steel (DP980) Adjusting Guide

Owing to their outstanding strength, in recent years, there has been an increased use of advanced high-strength steel (AHSS) sheets in the automotive sector. Their low formability, however, poses a challenge to forming, and failure prediction requires accurate knowledge of its material behavior over a large strain range up to ultimate failure, in order to exploit their full capacity in forming, but also in crash events. For predicting the fracture of an adjusting guide loaded by a pin, first, the force–displacement data are extracted from tensile tests using DP980 specimens of diverse shapes, all of which represent a certain loading mode. Using digital image correlation (DIC), we determine the stress triaxialities corresponding to the diverse loading conditions and establish the triaxiality failure diagram (TFD), which serves as the basis for the generalized incremental stress state-dependent damage model (GISSMO). Then, the damage parameters (necking and failure strains) are determined for each loading mode by reverse engineering-based optimization. Finally, these damage parameters are applied to the adjusting guide, and the numerical results are compared with the experimental data. Comparisons of the external load–displacement curves and the local equivalent strain distributions show that using the damage model with the material parameters obtained in here allows for the accurate prediction of the guide’s failure behavior, and the applicability of GISSMO to complex loading cases

Finite Element Analysis and Support Vector Regression-Based Optimal Design to Minimize Deformation of Indoor Bicycle Handle Frame Equipped with Monitor

Exercise has been gaining importance, as well as people’s interest. Nowadays, exercise bikes and similar home training devices usually feature a monitor to provide visual information and increase people’s convenience. With the increasing size of the mounted monitor frame, it is imperative to consider the monitor weight while designing the handle for an indoor exercise bike equipped with a monitor. In this study, optimal design based on finite element (FE) analysis was applied to increase the safety and robustness of the handle of an indoor bicycle equipped with a monitor. Considering the load that may be imposed on the handle, and its location, four FE analysis cases were performed. Loading conditions that contributed the largest von Mises stress (out of the four cases) were applied. Five design variables were chosen to minimize the effective stress. Moreover, the factor arrangement method using five factors and two levels required the run of 25 = 32 design cases. The resulting Pareto chart and sensitivity analysis confirmed the relationship between the effective stress and the chosen design variables. To obtain a predictive model using support vector regression (SVR), we subsequently increased the data range to five factors and three levels. The SVR prediction model was trained using a polynomial kernel to find the kernel parameters that provided the highest accuracy. Based on the coefficient of determination, the accuracy of the SVR prediction model was found to be superior to the conventional regression-based optimal design method. Additionally, the value of the design variable with the minimum effective stress was obtained using the SVR prediction model. Furthermore, upon redesigning the handle of the bicycle with the optimized design variable values, we found that the maximum effective stress was reduced by 80% compared with the initial model. Finally, the effective stress predicted by the SVR model was similar to that from the FE analysis, which confirmed the reliability of the predictive model

Stress Triaxiality in Anisotropic Metal Sheets—Definition and Experimental Acquisition for Numerical Damage Prediction

Governing void growth, stress triaxiality (η) is a crucial parameter in ductile damage prediction. η is defined as the ratio of mean stress to equivalent stress and represents loading conditions. Attempts at introducing material anisotropy in ductile damage models have started only recently, rendering necessary in-depth investigation into the role of η here. η is commonly derived via finite elemnt (FE) simulation. An alternative is presented here: based on analytical expressions, η is obtained directly from the strains in the critical zone. For anisotropic materials, η associated with a specimen varies with yield criterion and material (anisotropy). To investigate the meaning of triaxiality for anisotropic materials, metal sheets made of dual phase steel DP780, and zirconium alloy Zirlo are chosen. Analytical expressions for η are derived for three popular yield criteria: von Mises, Hill48 and Barlat89. Tensile tests are performed with uniaxial tension, notch, and shear specimens, and the local principal strains, measured via digital image correlation (DIC), are converted to h. The uniaxial tension case reveals that only the anisotropic yield criteria can predict the expected η = 1/3. The ramifications associated with anisotropy become apparent for notched specimens, where η differences are highest; for shear specimens, the yield criterion and material-dependence is relatively moderate. This necessitates η and, consequently, the triaxiality failure diagram (TFD) being accompanied by the underlying yield criterion and anisotropy parameters. As the TFD becomes difficult to interpret, it seems more advantageous to provide pairs of principal strain ratio β and failure strain. Suggestions for deriving representative β and η are made.</jats:p

Determination of the Forming Limit for a ZIRLO™ Sheet with High Anisotropy

In this study, the experimental two-dimensional forming limit diagram (FLD) data for a ZIRLO&trade; sheet, which is used in nuclear fuel rod support grids, were converted and presented as a triaxiality failure diagram (TFD). Most previous studies assumed ZIRLO&trade; to be isotropic when calculating the effective stress and strain. However, for highly anisotropic materials, the anisotropy should be considered for calculations of effective stress and strain; hence, in this study, they were calculated by introducing the normal anisotropy coefficient. To obtain this parameter of the ZIRLO&trade; specimens, tensile tests were performed on specimens with 0&deg;, 45&deg;, and 90&deg; angles with respect to the rolling direction. It was observed that the average normal anisotropy coefficient measured during the tests was 4.94, which is very high. The von Mises isotropic and Hill 48 anisotropic yield criterion were applied to the FLD data that were experimentally determined using a limit dome height test and were converted into effective stress and effective strain. When the FLD is converted to TFD, the curve will increase in the top-right direction if the r-value is greater than 1, and this become more severe as the r-value increases. The TFD, which was converted considering the anisotropy, is almost the same to the TFD obtained using the digital image correlation method in the tensile tests of four specimens with different stress states. If anisotropy is not considered, then the formability is normally underestimated. However, a highly accurate TFD can be obtained with the method proposed in this study

Automatic Screening of Bolts with Anti-Loosening Coating Using Grad-CAM and Transfer Learning with Deep Convolutional Neural Networks

Most electronic and automotive parts are affixed by bolts. To prevent such bolts from loosening through shock and vibration, anti-loosening coating is applied to their threads. However, during the coating process, various defects can occur. Consequently, as the quality of the anti-loosening coating is critical for the fastening force, bolts are inspected optically and manually. It is difficult, however, to accurately screen coating defects owing to their various shapes and sizes. In this study, we applied deep learning to assess the coating quality of bolts with anti-loosening coating. From the various convolutional neural network (CNN) methods, the VGG16 structure was employed. Furthermore, the gradient-weighted class activation mapping visualization method was used to evaluate the training model; this is because a CNN cannot determine the classification criteria or the defect location, owing to its structure. The results confirmed that external factors influence the classification. We, therefore, applied the region of interest method to classify the bolt thread only, and subsequently, retrained the algorithm. Moreover, to reduce the learning time and improve the model performance, transfer learning and fine tuning were employed. The proposed method for screening coating defects was applied to a screening device equipped with an actual conveyor belt, and the Modbus TCP protocol was used to transmit signals between a programmable logic controller and a personal computer. Using the proposed method, we were able to automatically detect coating defects that were missed by optical sorters.</jats:p