Objectives and content

Inverse Engineering Techniques is a set of methodologies and tools that allow automatically to obtain optimal designs starting from the product specification. At present these technologies of design, widely applied in structural and shape product optimization, are not used in design of forging processes. The intention of this project is to develop the necessary technology to implement this advanced CAD/CAE tool in the forging processes industries.

The industrial objectives of the project will be mainly a reduction of a 70% in the time of design of a forging process for a new or revised component. This fact will lead to reduce in 2 or 3 the time-to-market of the forging industries.

These objectives will be reach developing and applying in the forging industries, a new advanced CAE tool for Automatic Optimum design of forging processes:

- A mathematical research will he carried out to obtain a valid formulation in nonlinear Sensitivity Analysis (numerical evaluation of the influence of design parameters in the final forged products).

- This numerical Sensitivity Analysis will be validated against experimental sensitivity analysis obtained from forming trials.

- Existing CAE systems (like advanced FEM forging simulators) will be merged with automatic optimization techniques specific for forging.
- All these ingredients will be implemented and integrated as a very innovative tool for inverse designing of forging processes.

- A prototype of this tool will be used to optimize a set of forging pilot processes. For this RTD work will collaborate two R&D centres expert in numerical non-linear problems, four forging industries in different sector (automotive, agriculture machinery, aerospace, power generation, oil-industry), and one software house with expertise in industrialisation of numerical software.

At the end, it will be explored the applicability of the results to other file of the metal forming (rolling, extrusion,...); and it will be carried out several training-demonstration sessions.