Simulation and multi-objective optimization to improve the final shape and process efficiency of a laser-based material accumulation process

Abstract

Common goals of modern production processes are precision and efficiency. Typically, they are conflicting and cannot be optimized at the same time. Multi-objective optimization methods are able to compute a set of good parameters, from which a decision maker can make a choice for practical situations. For complex processes, the use of physical experiments and/or extensive process simulations can be too costly or even unfeasible, so the use of surrogate models based on few simulations is a good alternative. In this work, we present an integrated framework to find optimal process parameters for a laser-based material accumulation process (thermal upsetting) using a combination of meta-heuristic optimization models and finite element simulations. In order to effectively simulate the coupled system of heat equation with solid-liquid phase transitions and melt flow with capillary free surface in three space dimensions for a wide range of process parameters, we introduce a new coupled numerical 3d finite element method. We use a multi-objective optimization method based on surrogate models. Thus, with only few direct simulations necessary, we are able to select Pareto sets of process parameters which can be used to optimize three or six different performance measures.

Optimization of process parameters during hydroforming of tank bottom using NSGA-III algorithm

“…By comparing Kriging and moving least square (MLS) method, it is found that the optimal results obtained by Kriging method are better than MLS method. Bansch et al [21] used meta-heuristic optimization model and finite element simulation for the complex process flow. The method can approximate the Pareto front through few finite element simulations.…”

Analysis of a thermodynamically consistent Navier--Stokes--Cahn--Hilliard model

“…The system under consideration couples a Navier-Stokes-like equation (1a) and (1b) with a Cahn-Hilliard system (1d) and an internal energy balance (1c). On the one hand, this system is interesting in terms of its applications, which reach from modeling of cancer evolution and treatment [26] over fluid flow for mixtures [4,7] and for instance modelling of industrial processes like 3d printing [8]. On the other hand, it serves as an interesting prototype of a thermodynamical consistent model of a complex fluid.…”