M.H.A. Bonte

A Metamodel Based Optimisation Algorithm for Metal Forming Processes

Cost saving and product improvement have always been important goals in the metal forming industry. To achieve these goals, metal forming processes need to be optimised. During the last decades, simulation software based on the Finite Element Method (FEM) has significantly contributed to designing feasible processes more easily. More recently, the possibility of coupling FEM to mathematical optimisation algorithms is offering a very promising opportunity to design optimal metal forming processes instead of only feasible ones. However, which optimisation algorithm to use is still not clear. In this paper, an optimisation algorithm based on metamodelling techniques is proposed for optimising metal forming processes. The algorithm incorporates nonlinear FEM simulations which can be very time consuming to execute. As an illustration of its capabilities, the proposed algorithm is applied to optimise the internal pressure and axial feeding load paths of a hydroforming process. The product formed by the optimised process outperforms products produced by other, arbitrarily selected load paths. These results indicate the high potential of the proposed algorithm for optimising metal forming processes using time consuming FEM simulations.

The robust optimisation of metal forming processes

Robustness, reliability, optimisation and Finite Element simulations are of major importance to improve product quality and reduce costs in the metal forming industry. In this paper, we review several possibilities for combining these techniques and propose a robust optimisation strategy for metal forming processes. The importance of including robustness during optimisation is demonstrated by applying the robust optimisation strategy to an analytical test function: for constrained cases, deterministic optimisation will yield a scrap rate of about 50% whereas the robust counterpart reduced this to the required 3σ reliability level.

Modelling, screening, and solving of optimisation problems: Application to industrial metal forming processes

Coupling Finite Element (FEM) simulations to mathematical optimisation techniques provides a high potential to improve industrial metal forming processes. In order to optimise these processes, all kind of optimisation problems need to be mathematically modelled and subsequently solved using an appropriate optimisation algorithm. Although the modelling part greatly determines the final outcome of optimisation, the main focus in most publications until now was on the solving part of mathematical optimisation, i.e. algorithm development. Modelling is generally performed in an arbitrary way. In this paper, we propose an optimisation strategy for metal forming processes using FEM. It consists of three stages: a structured methodology for modelling optimisation problems, screening for design variable reduction, and a generally applicable optimisation algorithm. The strategy is applied to solve manufacturing problems for an industrial deep drawing process.

A Robust Optimisation Strategy for Metal Forming Processes

Robustness, reliability, optimisation and Finite Element simulations are of major importance to improve product quality and reduce costs in the metal forming industry. In this paper, we propose a robust optimisation strategy for metal forming processes. The importance of including robustness during optimisation is demonstrated by applying the robust optimisation strategy to an analytical test function and an industrial hydroforming process, and comparing it to deterministic optimisation methods. Applying the robust optimisation strategy significantly reduces the scrap rate for both the analytical test function and the hydroforming process.

A Study For Efficiently Solving Optimisation Problems With An Increasing Number Of Design Variables

Coupling optimisation algorithms to Finite Element Methods (FEM) is a very promising way to achieve optimal metal forming processes. However, many optimisation algorithms exist and it is not clear which of these algorithms to use. This paper investigates the sensitivity of a Sequential Approximate Optimisation algorithm (SAO) proposed in [1-4] to an increasing number of design variables and compares it with two other algorithms: an Evolutionary Strategy (ES) and an Evolutionary version of the SAO (ESAO). In addition, it observes the influence of different Designs Of Experiments used with the SAO. It is concluded that the SAO is very capable and efficient and its combination with an ES is not beneficial. Moreover, the use of SAO with Fractional Factorial Design is the most efficient method, rather than Full Factorial Design as proposed in [1-4].