A Robust Optimisation Strategy for Metal Forming Processes

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

The robust optimisation of metal forming processes

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

Computational optimisation of robust sheet forming processes

Mathematical optimisation consists of the modelling and solving of optimisation problems. Although both the modelling and the solving are essential for successfully optimising metal forming problems, much of the research published until now has focussed on the solving part, i.e. the development of a specific optimisation algorithm and its application to a specific optimisation problem for a specific metal forming process. We propose a generally applicable optimisation strategy which makes use of FEM simulations of metal forming processes. It consists of a methodology for modelling optimisation problems related to metal forming. Subsequently, screening is applied to reduce the size of the optimisation problem by selecting only the most important design variables. Finally, the reduced optimisation problem is solved by an efficient optimisation algorithm. However, the above strategy is deterministic, which implies that the robustness of the optimum solution is not taken into account. Robustness is a major item in the metal forming industry, hence the deterministic strategy is extended in order to include noise variables (e.g. material variation) in optimisation. This yields a robust optimisation strategy that enables to optimise to a robust solution of the problem, which contributes significantly to the industrial demand to design robust metal forming processes. Just as the deterministic optimisation strategy, it consists of a modelling, screening and solving stage. The deterministic and robust optimisation strategies are compared to each other by application to an analytical test function

Accounting for material scatter in sheet metal forming simulations

Robust design of forming processes is gaining attention throughout the industry. To analyze the robustness of a sheet metal forming process using Finite Element (FE) simulations, an accurate input in terms of parameter variation is required. This paper presents a pragmatic, accurate and economic approach for measuring and modeling one of the main inputs, i.e. material properties and its associated scattering. For the purpose of this research, samples of 41 coils of a forming steel DX54D+Z (EN 10327:2004) from multiple batches have been collected. Fully determining the stochastic material behavior to the required accuracy for precise modeling in FE simulations would involve performing many mechanical experiments. Instead, the present work combines mechanical testing and texture analysis to limit the required effort. Moreover, use is made of the correlations between the material parameters to efficiently model the material property scatter for use in the numerical robustness analysis. The proposed approach is validated by the forming of a series of cup products using the collected material. The observed experimental scatter can be reproduced efficiently using FE simulations, demonstrating the potential of the modeling approach and robustness analysis in general

Adaptive process control strategy for a two-step bending process

A robust production is an important goal in sheet metal forming in order to make the process outcome insensitive to variations in input and process conditions. This would guarantee a minimum number of defects and reduced press downtime. However, for com-plex parts it is difficult to achieve robust settings. Parts without defects can only be real-ized if the process parameters are adapted to the changed conditions.\ud In this paper, an approach for adaptive process control is presented, taking the uncertain-ties and tolerances of the process and material into consideration. The proposed control approach combines feedback and feed-forward control strategies. The most significant improvement is to incorporate feed-forward control with knowledge about the system (also known as predictive models). To create these models high fidelity numerical models have been created. Furthermore, a procedure is presented to update the coefficients of the predictive model to adapt it to the actual process state.\ud To evaluate the control strategy prior to its implementation, a testing environment has been developed. Different test scenarios for common states of the process have been generated to evaluate the improvement of the proposed control strategy