Antonius H. van den Boogaard

Gradient Enhanced Physically Based Plasticity: Implementation and Application to a Problem Pertaining Size Effect : Implementation and application to a problem pertaining size effect

A physically based plasticity model is implemented which describes work hardening of a material as a function of the total dislocation density. The local part of the model, which involves statistically stored dislocations (SSDs) only, is based on Bergstroms original model. The nonlocal part is based on geometrically necessary dislocations (GNDs) which appear and evolve due to existence of large plastic strain gradients. The evolution of GNDs with respect to strain gradients is described based on the flow theory. The gradients are computed explicitly using the converged plastic strain field and the coupling is achieved using a staggered (weak) approach. Gradient computation is carried out using an effcient algorithm that makes use of plastic strain increments at integration points whose arrangement is not necessarily regular. The algorithm is applied on a void growth problem in which high strain gradients occur around the void due to stress concentrations.

Efficient Calculation of Uncertainty Propagation with an Application in Robust Optimization of Forming Processes

Robust optimization is being used in metal forming processes to select the design which is least sensitive to the presence of uncertainty in the input parameters. In most cases, a mathematical surrogate model is built via input data and resulting output obtained from finite element simulations. The influence of uncertainty in input parameters is then considered using a large number of function evaluations via Monte Carlo analysis with a given probabilistic distribution. Although this method is quite fast and simple, it needs a lot of function evaluations to increase accuracy. This random sampling is neither efficient nor reproducible. A new approach is used to calculate the uncertainty propagation analytically. Compared to conventional Monte Carlo approach this method is accurate, fast, stable, and efficient. In addition, it is possible to employ this method with different types of probability distributions and most commonly-used metamodels. To show the applicability of this method in robust optimization process, a stretch-bending process is investigated with two design and two noise variables. Comparing the results obtained by Monte Carlo and the analytical approach shows that different Monte Carlo runs lead to fluctuations around the exact analytical solution. In addition, the analytical approach reduces the evaluation time of finding the robust optimum to a great extent.