On the Use of Homogeneous Polynomial Yield Functions in Sheet Metal Forming Analysis

Sheet metal forming techniques are a major class of stamping and manufacturing processes of numerous parts such as doors, hoods, and fenders in the automotive and related supplier industries. Due to series of rolling processes employed in the sheet production phase, automotive sheet metals, typically, exhibit a significant variation in the mechanical properties especially in strength and an accurate description of their so-called plastic anisotropy and deformation behaviors are essential in the stamping process and methods engineering studies. One key gradient of any engineering plasticity modeling is to use an anisotropic yield criterion to be employed in an industrial content. In literature, several orthotropic yield functions have been proposed for these objectives and usually contain complex and nonlinear formulations leading to several difficulties in obtaining positive and convex functions. In recent years, homogenous polynomial type yield functions have taken a special attention due to their simple, flexible, and generalizable structure. Furthermore, the calculation of their first and second derivatives are quite straightforward, and this provides an important advantage in the implementation of these models into a finite element (FE) software. Therefore, this study focuses on the plasticity descriptions of homogeneous second, fourth and sixth order polynomials and the FE implementation of these yield functions. Finally, their performance in FE simulation of sheet metal cup drawing processes are presented in detail.

Capacitor Discharge Spot Welding of Aluminum, Part 1: Weldability Assessments

A key aspect of integrating automotive sheet into automotive production are the costs associated with joining. While the majority of sheet steel assembly is done with resistance spot welding, that has not readily translated to aluminum. Resistance spot welding of aluminum sheet is challenged by high current demand as well as reduced electrode life. In the latter case, direct current (DC) power supplied by state-of-the-art systems has exacerbated the problem. Recently, technology employing capacitor discharge (CD) welding in conjunction with polarity switching has been developed. This work is a first effort in examining the response of resistance spot welding on aluminum sheet to polarity-switching CD power. In this paper, the current range response between medium-frequency DC (MFDC) and polarity-switching CD was investigated. It was found that polarity-switching CD welding offered improved current ranges over MFDC. In addition, replicate mechanical testing cross-tension results were similar, but tensile shear strengths improved nominally 20–25%. Finally, some limited tests were done to assess the suitability of CD resistance spot welding in the presence of an adhesive. Current range tests with and without a prepulse were done, and both showed excellent weldability.

A Buckling Instability Prediction Model for the Reliable Design of Sheet Metal Panels Based on an Artificial Intelligent Self-Learning Algorithm

Sheets’ buckling instability, also known as oil canning, is an issue that characterizes the resistance to denting in thin metal panels. The oil canning phenomenon is characterized by a depression in the metal sheet, caused by a local buckling, which is a critical design issue for aesthetic parts, such as automotive outer panels. Predicting the buckling instability during the design stage is not straightforward since the shape of the component might change several times before the part is sent to production and can actually be tested. To overcome this issue, this research presents a robust prediction model based on the convolutional neural network (CNN) to estimate the buckling instability of automotive sheet metal panels, based on the major, minor, and Gaussian surface curvatures. The training dataset for the CNN model was generated by implementing finite element analysis (FEA) of the outer panels of various commercial vehicles, for a total of twenty panels, and by considering different indentation locations on each panel. From the implemented simulation models the load-stroke curves were exported and utilized to determine the presence, or absence, of buckling instability and to determine its magnitude. Moreover, from the computer aided design (CAD) files of the relevant panels, the three considered curvatures on the tested indentation points were acquired as well. All the positions considered in the FEA analyses were backed up by industrial experiments on the relevant panels in their assembled position, allowing to validate their reliability. The combined correlation of curvatures and load-displacement curves allowed correlating the geometrical features that create the conditions for buckling instability to arise and was utilized to train the CNN algorithm, defined considering 13 convolution layers and 5 pooling layers. The trained CNN model was applied to another automotive frame, not used in the training process, and the prediction results were compared with experimental indentation tests. The overall accuracy of the CNN model was calculated to be 90.1%, representing the reliability of the proposed algorithm of predicting the severity of the buckling instability for automotive sheet metal panels.

Topology Optimization of Automotive sheet metal part using Altair Inspire

In an optimization problem, different candidate solutions are compared with each other, and then the best or optimal solution is obtained which means that solution quality is fundamental. Topology optimization is used at the concept stage of design. It deals with the optimal distribution of material within the structure. Altair Inspire software is the industry's most powerful and easy-to-use Generative Design/Topology Optimization and rapid simulation solution for design engineers. In this paper Topology optimization is applied using Altair inspire to optimize the Sheet metal Angle bracket. Different results are conducted the better and final results are fulfilling the goal of the paper which is minimizing the mass of the sheet metal part by 65.9% part and Maximizing the stiffness with Better Results of Von- Miss Stress Analysis, Displacement, and comparison with different load cases. This can lead to reduced costs, development time, material consumption, and product less weight.

Direct Optimization of an Automotive Sheet Metal Part Using ANSYS

Optimization of automotive parts nowadays is mainly used to design lightweight and cost-effective vehicle parts in order to improve the cost and efficiency. In this research, a sheet metal part was taken into consideration and optimized using direct optimization module in ANSYS to evaluate the process. An initial Finite Element Analysis (FEA) was done on the sheet metal part by adding forces and constraints in order to initiate direct optimization. The purpose of the optimization is to minimize the mass of the sheet metal part and maintaining a certain Factor of Safety (FOS) by automatically modifying the sheet thickness and the dimension of the side holes. As a result, the best candidate point with 23% mass reduction was found which complied with FOS value was selected for optimal geometry.