The purpose of this paper is to obtain a model that quickly predicts SPRINGBACK in the three-point bending process of steel / PUR / steel sandwich panels. Firstly, based on the finite element simulation, the SPRINGBACK behavior for different punch radius, length between supports, and foam thickness is established. The results obtained by the finite element analysis show a satisfactory agreement with the experimental results. Secondly, three machine learning approaches are applied, including linear regression (LR), artificial neural network (ANN), and support vector machine (SVM) in order to predict the SPRINGBACK of sandwich panels in the three-point bending process. The performance of these approaches is investigated by using some statistical tools like mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The obtained results show that the ANN approach is the best model for predicting the SPRINGBACK of sandwich panels when considering accuracy.

Cold roll forming is a sheet metal forming process in whichthe bending occurs gradually in several forming steps from an undeformed strip to a finishedprofile. Althoughsimple appearance, the process is influenced by several parametersthat complicate the process design and control such as SPRINGBACK.The main purpose in this article is to determine a simple criterion for the SPRINGBACK and to introduce a fast solution to obtain it and therefore artificial neural network was proposed for achiving this goal. Cold roll forming of a channel section in the first station was simulated by a commercial package named “Msc Marc Mentat”. The data obtained from the finite element simulations were used as training and testingsetsfor neural networks. Perfect performance of neural network was proved when the neural network outputs were compared with the testing set that did not exist in the training set.

Spingback is one of the important factors in metal forming process. Study on SPRINGBACK shows that this phenomenon can have great impacts on shape and formation of the part. Therefore, to increase the accuracy and the quality of the produced part, the important factors have to be recognized. In this paper, machining forming for thin part which is a combination of machining and incremental forming is first introduced.Then numerical and experimental results are compared. The EFFECTs of forming parameters such as tool diameter, part angle, forming steps, and tool position on SPRINGBACK are presented. The results show that the step depth has a great affect on SPRINGBACK.

This study provides an approach to predict the SPRINGBACK phenomenon during post-solidification coolingin a functionally graded hybrid composite cylindrical shell with a transverse isotropic structure. Here, thematerial properties are given with a general parabolic power-law function. During the theoretical analysis, an appropriate transformation is introduced in the equilibrium equation, which is resulting in ahypergeometrical differential equation. Thermoelastic solutions are obtained and analyzed for ahomogeneous, nonhomogeneous, and elastic-plastic state. The solution is validated by applying it to themultilayered functionally graded cylindrical shell using the transfer or propagator matrix method.

SPRINGBACK is one of the key factors to influence the quality of drawn sheet metal parts in deep drawing process. Previous investigations about the SPRINGBACK have shown that, the amount of work-piece deformation can be affected by this phenomenon in the deep drawing process.Therefore, to optimise this manufacturing process and to increase the dimensional accuracy of the parts, it is essential to find out the EFFECTive parameters and bring the deep drawing process under producer control. In this paper, the finite element analysis of SPRINGBACK was applied and the EFFECTs of many physical parameters on the SPRINGBACK such as, type of materials, blankholder force, coefficient of friction and tool clearance were studied. Then, it was proposed how to decrease the EFFECT of SPRINGBACK for different materials. For simplicity, two dimensional analyses (plane-strain simulation) were performed and the results were compared with experimental data. A good agreement was found between numerical and experimental results.

Today, the use of metallic bipolar plates in the fuel cell industry has attracted the attention of many researchers due to its much lower cost than thick graphite plates produced by machining. The best method for the production of metallic bipolar plates is the forming process. Among the different forming methods, the stamping process has a higher production rate, simpler process, and lower production cost. One of the major problems in the formation of the metallic bipolar plates is the SPRINGBACK of the sheet after forming, which causes distortion and nonuniformity in the formed channels. In this study, the EFFECTs of geometrical parameters such as draft angle, corner radius, depth of the channel, and process parameter such as lubricant on filling a profile as well as SPRINGBACK of a formed sheet made of stainless steel 304 with a thickness of 0. 1mm were investigated. For this purpose, the simulation was performed using ABAQUS finite element software and the results were verified by experimental analysis. Then the outputs were evaluated by changing the input parameters in the simulation. The results showed that the draft angle and channel width had the most influence on the SPRINGBACK value of the formed plates. The results related to the process parameter such as the lubricant EFFECT showed that the SPRINGBACK value is almost independent of the lubricant parameter. However, in quite equal conditions, the stress distribution in the corners and channel walls is much more uniform when using the lubricant.

The most prominent feature of sheet material forming process is an elastic recovery phenomenon during unloading which leads to SPRINGBACK and side wall curl. Therefore evaluation of SPRINGBACK and side wall curl is mandatory for production of precise products. In this paper, the EFFECTs of some parameters such as friction coefficient, sheet thickness, yield strength of sheet and blank-holder force on the SPRINGBACK and side wall curl in U-bending of DP600 steel alloy sheet has been investigated. The investigations have been done by computer simulation. In the simulations the ABAQUS finite element software has been used and the results compared to experimental ones. The finite element results have been validated from experimental results. After validation of FEM simulation via experimental results a powerful, rapid and efficient tool was introduced for investigating some important aspects and parameters of U-bending process such as blank-holder force, sheet thickness, yield strength of sheet and friction coefficient. MINITAB, a statistical software, was used to analyze finite element results. With the use of MINITAB, equations for prediction of SPRINGBACK and side wall curl radius by friction coefficient, sheet thickness, yield strength and blank-holder force were obtained.

The technique of Sheet Stamping by Deformable Forming Tools (SSDFT) has been recognized as a promising alternative to control the SPRINGBACK of sheet blanks. This paper deals with the SPRINGBACK of elastic-plastic circular plates on deformable dies subjected to transverse external forces. Different significant parameters are examined within the framework of the present investigation. For instance, the influence of the friction coefficient between the workpiece and elastomer on the equivalent plastic strain and, therefore, on the SPRINGBACK, for different boundary conditions, are investigated in detail. Moreover, the EFFECTs of the thickness of the workpiece on the SPRINGBACK, for both deformable and conventional dies, are discussed.    

The current research was conducted with the aim of the EFFECT of attachment style on emotion regulation with the mediating role of identity style. The research method was descriptive and structural equation analysis. The statistical population of the research was all second year high school students of Kashan city in the academic year of 1400-1401, of which 500 people were selected by random cluster method. Data were collected based on three questionnaires: Gross & Jan (2003) Emotional Regulation Inventory, Identity Style Scale (6G-ISI) and Collins and Reid (1990) Attachment Style Questionnaire. The data are obtained by structural equation modeling and using Laserl 8.8 and 26 spss software. analyzed. Based on the results, secure attachment style at a significance level of 0.01, avoidant attachment style at a significance level of 0.05, and ambivalent attachment style at a significance level of 0.01 directly predicted emotion regulation. Among the three attachments, secure attachment style predicted emotion regulation at a significant level of 0.01, with the mediation of identity styles. Informational identity style at a significance level of 0.01, confused identity style at a significance level of 0.05 directly predicted emotion regulation. Among the three attachments, only the secure attachment style at a significance level of 0.01 had a significant role in predicting the normative identity style. According to the results of this study, emotion regulation is one of the most important constructs of educational psychology and an important factor in determining mental health and successful performance. It is in education that the factors affecting emotion regulation and its antecedents should be identified. Planning for training and holding educational workshops in this field in schools is suggested for students.