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.

"The answer is blowin' in the wind" - case study of a perforated roof screen

A recently-completed building was fitted with a roof screen fabricated from perforated sheet metal panels having "U"-shaped upturned flanges. When wind impinges on the panels, complex tone clusters are generated, leading to complaints from the occupants. The unusual character of the tonal spectrum is reminiscent of a film sound effect intended to simulate a hovering extraterrestrial spacecraft. After some preliminary (but inconclusive) field investigations, it was decided to test samples of the perforated panel in a large commercial wind tunnel where the speed and angle of the airstream could be controlled. Tones generated in the tunnel were found to occur in groups or clusters - these are most pronounced when the airstream's angle of incidence is close to grazing. Gradually increasing airspeed caused the frequency of the tones to "jump" from one cluster to the next higher cluster. The physical principles of the tone-generating mechanism are not fully understood; however, it appears that structural resonances in the panel flanges are excited by air flowing over the perforate. Some form of a positive structural-acoustical feedback loop is involved since a) the frequencies within each tone cluster are quite stable and, b) damping the panel flanges extinguishes the tones.

Temperature suppression of metal panel in electromagnetic field

Purpose The purpose of this study is to suppress the temperature rise of high voltage wall bushing metal plate. Design/methodology/approach First, the authors built a model of a traditional metal plate and got the magnetic field intensity distribution by FEA tools. Optimized according to the magnetic field intensity distribution, the authors slot the traditional metal plate and embed permanent magnets in the slot. Finally, the authors got the temperature distribution diagrams of the above three cases at different current levels by FEA tools. Findings Slotted metal plate is beneficial to suppress magnetic induction intensity, but the improvement of the magnetic induction intensity uniformity is not obvious. The method of embedding a permanent magnet in a slotted metal plate can optimize the magnitude and uniformity of the magnetic induction intensity in the metal plate. The larger the current passing through the metal plate, the better the temperature suppression effect of the slotted metal plate and the slotted metal plate embedded in the permanent magnet. Originality/value The effect of structural factors, slotting plate and setting permanent magnets on slots on the temperature of supporting plate is studied. The paper proposes two methods, slotting metal panels and embedding permanent magnet metal panels, to solve the problems of eddy current loss and high calorific value of the panel, which is of great significance to the safety of the grid equipment.