Elementary Solutions and Process Sensitivities for Plane-Strain Sheet-Metal Forming

1992 ◽  
Vol 59 (2S) ◽  
pp. S23-S28 ◽  
Author(s):  
M. L. Wenner
Keyword(s):  
Exact Solution ◽  
Plane Strain ◽  
Sheet Metal ◽  
Process Parameters ◽  
Sheet Metal Forming ◽  
Metal Forming ◽  
Computer Programs ◽  
Equilibrium Equations ◽  
Numerical Errors ◽  
Low Levels

Solutions to simple, plane-strain sheet-metal forming problems are developed using an exact solution to the membrane equilibrium equations and apre-integrated version of the plasticity equations. This approach allows the numerical errors to be driven to very low levels in an economical manner. Both stretch and draw conditions are considered, and solutions are presented for several problems which have been proposed as benchmarks for sheet-metal forming computer programs. The simplicity of this approach allows us to calculate formulas for the sensitivity of the solutions to process parameters. It is demonstrated that draw problems can exhibit very strong dependencies on parameters, especially drawbead restraint force.

scholarly journals Influence of process parameters on surface roughness and forming time of Al-1100 sheet in incremental sheet metal forming

2019 ◽  
Vol 13 (2) ◽  
pp. 4911-4927
Author(s):  
Swagatika Mohanty ◽  
Srinivasa Prakash Regalla ◽  
Yendluri Venkata Daseswara Rao
Keyword(s):  
Surface Roughness ◽  
Response Surface ◽  
Sheet Metal ◽  
Process Parameters ◽  
Sheet Metal Forming ◽  
Feed Rate ◽  
Metal Forming ◽  
Wall Angle ◽  
Surface Method ◽  
Incremental Sheet Metal Forming

Product quality and production time are critical constraints in sheet metal forming. These are normally measured in terms of surface roughness and forming time, respectively. Incremental sheet metal forming is considered as most suitable for small batch production specifically because it is a die-less manufacturing process and needs only a simple generic fixture. The surface roughness and forming time depend on several process parameters, among which the wall angle, step depth, feed rate, sheet thickness, and spindle speed have a greater impact on forming time and surface roughness. In the present work, the effect of step depth, feed rate and wall angle on the surface roughness and forming time have been investigated for constant 1.2 mm thick Al-1100 sheet and at a constant spindle speed of 1300 rpm. Since the variable effects of these parameters necessitate multi-objective optimization, the Taguchi L9 orthogonal array has been used to plan the experiments and the significance of parameters and their interactions have been determined using analysis of variance (ANOVA) technique. The optimum response has been brought out using response surfaces. Finally, the findings of response surface method have been validated by conducting additional experiments at the intermediate values of the parameters and these results were found to be in agreement with the predictions of Taguchi method and response surface method.

Optimization for Stamping Process Parameters of Front Fender Based on Numerical Simulation Technology

2010 ◽  
Vol 102-104 ◽  
pp. 232-236 ◽  
Author(s):  
Zhi Feng Liu ◽  
Qi Zhang ◽  
Wen Tong Yang ◽  
Jian Hua Wang ◽  
Yong Sheng Zhao
Keyword(s):  
Numerical Simulation ◽  
Sheet Metal ◽  
Process Parameters ◽  
Sheet Metal Forming ◽  
Metal Forming ◽  
Simulation Analysis ◽  
Explicit Method ◽  
Simulation Technology ◽  
Stamping Process ◽  
Automotive Panel

According to the characteristic which is more and difficult to determine about the automotive panel forming factors, based on the dynamic explicit method, taking the typical automobile front fender for example, do the simulation analysis by using of DYNAFORM. On the premise of taking springback factors into account, analog the best stamping process parameters has been optimized from the analysis results after simulation such as sheet metal forming limited drawing(FLD)and sheet metal thinning drawing.

Incremental sheet metal forming of Ti–6Al–4V alloy for aerospace application

2020 ◽  
Vol 44 (1) ◽  
pp. 56-64 ◽  
Author(s):  
R. Mohanraj ◽  
S. Elangovan
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Sheet Metal ◽  
Process Parameters ◽  
Experimental Work ◽  
Sheet Metal Forming ◽  
Metal Forming ◽  
Geometric Accuracy ◽  
Element Analysis ◽  
Incremental Sheet Metal Forming

Driven by an increasing demand from the aerospace industry, thin sheet forming of titanium and its alloys is gaining prominence in scientific research. The design and manufacture of aerospace components requires the utmost precision and accuracy. It is essential to have good control over the process parameters of the forming process. Processes such as incremental sheet metal forming (ISMF) are highly controlled in the current manufacturing environment, but improvements in geometric accuracy and thinning are still needed. Although ISMF has greater process competence for manufacturing airframe structures with minimal costs, the process has its own negative effect on geometric accuracy due to elastic springback and sheet thinning. In this study, finite element analysis and experimental work are performed, considering process parameters such as spindle speed, feed rate, step depth, and tool diameter, to study the geometric accuracy and thinning of Ti–6Al–4V alloy sheet, while incrementally forming an aerospace component with asymmetrical geometry. The results are useful for understanding the geometric accuracy and thinning effects on parts manufactured by single point incremental forming (SPIF). Results from finite element analysis and experimental work are compared and found to be in good agreement.

On Applying Kriging Approximate Optimization to Sheet Metal Forming

2011 ◽  
Vol 63-64 ◽  
pp. 3-7
Author(s):  
Yan Min Xie
Keyword(s):  
Sheet Metal ◽  
Process Parameters ◽  
Sheet Metal Forming ◽  
Metal Forming ◽  
Nonlinear Function ◽  
Kriging Model ◽  
Engineering Problem ◽  
Forming Process ◽  
Element Analysis ◽  
Metal Forming Process

This paper presents a methodology to effectively determine the optimal process parameters using finite element analysis (FEA) and design of experiments (DOE) based on Metamodels. The idea is to establish an approximation function relationship between quality objectives and process parameters to alleviate the expensive computational expense in the optimization iterations for the sheet metal forming process. This paper investigated the Kriging metamodel approach. In order to prove accuracy and efficiency of Kriging method, the nonlinear function as test functions is implemented. At the same time, the practical nonlinear engineering problems such as square drawing are also optimized successfully by proposed method. The results prove Kriging model is an effective method for nonlinear engineering problem in practice.

scholarly journals Single and ensemble classifiers for defect prediction in sheet metal forming under variability

2019 ◽  
Vol 32 (16) ◽  
pp. 12335-12349 ◽  
Author(s):  
M. A. Dib ◽  
N. J. Oliveira ◽  
A. E. Marques ◽  
M. C. Oliveira ◽  
J. V. Fernandes ◽  
...  
Keyword(s):  
Sheet Metal ◽  
Process Parameters ◽  
Sheet Metal Forming ◽  
Metal Forming ◽  
Sheet Thickness ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Ensemble Classifiers ◽  
Ensemble Models ◽  
Using Data

AbstractThis paper presents an approach, based on machine learning techniques, to predict the occurrence of defects in sheet metal forming processes, exposed to sources of scatter in the material properties and process parameters. An empirical analysis of performance of ML techniques is presented, considering both single learning and ensemble models. These are trained using data sets populated with numerical simulation results of two sheet metal forming processes: U-Channel and Square Cup. Data sets were built for three distinct steel sheets. A total of eleven input features, related to the mechanical properties, sheet thickness and process parameters, were considered; also, two types of defects (outputs) were analysed for each process. The sampling data were generated, assuming that the variability of each input feature is described by a normal distribution. For a given type of defect, most single classifiers show similar performances, regardless of the material. When comparing single learning and ensemble models, the latter can provide an efficient alternative. The fact that ensemble predictive models present relatively high performances, combined with the possibility of reconciling model bias and variance, offer a promising direction for its application in industrial environment.

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