Formability Predictions in Stamping and Process Parameter Optimization Based on the Inverse Approach Code Fast_Stamp
A simplified (one step) method called “inverse approach” (IA) for the numerical analysis of the stamping process has been continuously developed by the authors since the end of the eighties. In the present paper we recall the main finite element formulation aspects, for an efficient estimation of the large elastoplastic strains encountered in deep drawing operations. The capabilities of the code Fast_Stamp include: fixed (imposed) initial blank, bending effects due to tool curvatures, blankholder restraining forces (including drawbeads). Considerations of bending and unbending during forming stages can improve the IA regarding stresses distribution. In this case satisfactory estimation of springback can result. Since 1996 we also developed “math based” optimization algorithms and strategies. The cost functions and constraints are mainly expressed to reduce or control the thickness changes, the localized necking, the wrinkling tendency, the springback after forming. The design variables are describing the ...
On the Determination of the Blank Shape Contour for Thin Precision Parts Obtained by Stamping
The present study deals with the “automatic” determination of the initial blank shape contour for 3D thin metallic precision parts obtained by stamping, knowing the 3D CAD geometry of the final part (the desired product). The forming process can involve several steps presented in this paper that consists in applying a heuristic method of optimization to find out the initial blank shape of thin precision metallic part in order to obtain a final part, with a required 3D geometry (specified). The purpose of the present approach is to replace the experimental trial and error optimization method used currently, which is expensive and time consuming. The principle of the “heuristic” optimization method is to first estimate the blank shape using the Inverse Approach, then to compensate the shape error calculated in each node of the blank contour. The “heuristic” optimization loop is done using a precise incremental code (Abaqus Explicit or Stampack) and, the iterations loop is stopped when the shape errors are within some initially fixed tolerances. The method is tested in the case of a special stamping process where the parts are pressed in one or more steps using a manual press, without blank holder and by the mean of tools having complex shape. The sensitivities of the process parameters regarding the optimal solution are investigated.The present study deals with the “automatic” determination of the initial blank shape contour for 3D thin metallic precision parts obtained by stamping, knowing the 3D CAD geometry of the final part (the desired product). The forming process can involve several steps presented in this paper that consists in applying a heuristic method of optimization to find out the initial blank shape of thin precision metallic part in order to obtain a final part, with a required 3D geometry (specified). The purpose of the present approach is to replace the experimental trial and error optimization method used currently, which is expensive and time consuming. The principle of the “heuristic” optimization method is to first estimate the blank shape using the Inverse Approach, then to compensate the shape error calculated in each node of the blank contour. The “heuristic” optimization loop is done using a precise incremental code (Abaqus Explicit or Stampack) and, the iterations loop is stopped when the shape errors are w...
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.