AI-FORM Case Study | C3P Software

AI-FORM Case Study

Case Study I:  Stamping Optimization of Oil Panel

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The initial simulation result - Serious wrinkle and crack existed in the oil panel

Project Background
  • The oil panel part has a complex shape, uneven drawing depth and big saddle feature on parts, which is very easy to cause wrinkle and crack during the stamping process.  It is necessary to optimize the initial blank sheet size,  holding force, drawbead shape and dimension to balance the material flow and avoid defects.
  • The traditional method is combining different conditions for CAE simulation and choice the best one. But this method takes a long time, low efficiency, and quite difficult to find the best solution. So the full integrated automatic optimization is required to solve this problem.

 

Optimization Strategy

According to the initial simulation, in order to find the best working window, the following geometry parameters and process variables were selected:

  • The dimension of the blank sheet (offset of BS in the figure below)
  • The location and length of drawbead (DB1 and DB2)
  • The drawbead restrain force during drawing, it could be used to define the shape and dimension.

 

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Schematic diagram of input parameters

Optimization  Criteria

The objective was defined as follows :

  • Select an area in the saddle region and check the maximum thickness ( to detect the wrinkles).
  • Put a sensor on the blank sheet to check metal flow.
  • A region at the top of the part to calculate the cumulative value of the FLD to determine whether the rupture occurs.

 

Optimization Phases
Due to the relationship between input variables and output results were not fully understood well, and the suitable working window was not clear,  so we carried out the optimization analysis by the following two phases.

Phase I: Using DOE to Find the Feasible Process Window

Phase II: Using PSO to Seek the Best Process Window

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The PSO analysis results in the parallel coordinates plot

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Simulation results within the optimized working window

 (PSO run result of no. 46 )

By adjusting the output axis, it is easy to find that if the blank sheet offset size is between 21~63mm, the drawbead length is between 50~89mm, and the drawbead resistance force coefficient is between 0.29~0.4,  then the stamping result was satisfying.

 

Project Summary
  • CAD driven automatic optimization.
  • Optimize to any dimension and any parameters (geometry, material, process condition etc).
  • Full automatic meshing and FEM setup technology, all simulation results could be used as optimization criteria.
  • No limited to input parameters and output parameters.
  • DOE, GA and PSO method.
  • Professional tools to analysis optimization result, i.e. Pareto front point, parallel coordinator plot, etc.
  • Parallel simulation and parallel optimization.

 

Case study II:  CAD driven optimization for cracking

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Initial design:  The simulation result and  try-out result

Project Background
  • The formability of the stamping part is often limited by its geometric features, however, if the forming feature is far away from the region of the entrance, the difficulty level of forming is usually very high.
  • One very smart solution is storing material as much as possible in the holding stage by modifying the die surface, to balance the wrinkles and rupture of the stamping process. Due to the non-linearity of the problem, without AI, the limited tradition number of manual modifications is very difficult to solve such a problem.
  • In this example, CAD-driven optimization is the key technology.One very smart solution is storing material as much as possible in the holding stage by modifying the die surface, to balance the wrinkles and rupture of the stamping process.
  • The limitation
  • Due to the non-linearity of the problem, without AI, the limited tradition number of manual modifications is very difficult to solve such a problem.

 

 Optimization Phases
1, Improving Blank Shape and Size.

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2, DOE to Understand the Physical Problem.
  • Useing the ParaCAD function to rebuild the binder and addendum surface. And make a parametric design for the key dimension H1 and H2.

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ParaCAD model for die face

  • Optimized the process parameters – Friction factor, Drawbead force.

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3, The Best Working Window.

H2 and friction factor were the major factors as well as the H1 and drawbead  resistance force were the minor factors.

From the analysis result, within the following working windows, the stamping part is safe and quality satisfied.

  • Friction factor:  0.04  to 0.05
  • H2: -55.0 to 30.0
  • H1: -42 to -10
  • Drawbead force: 0.01 to 0.05

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The figure shows the proportion of influence of each variable

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The final result of  mass production and simulation

(The contour was thickness distribution)

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