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Session 14

Statistical Analysis of Process Chains : Novel PRO-CHAIN Components
The robustness of production processes and the quality of resulting products suffer from variations in important material and process parameters, geometry and external influences, which can have substantial and critical influences. Therefore these variations have to be analyzed and transferred over process steps in order to achieve considerably better forecasting quality. We developed the PRO-CHAIN strategy for statistical analysis of sensitivity and stability as well as multi-objective robust design-parameter optimization of whole process chains, even for simulation results on highly resolved grids. PRO-CHAIN constructs an ensemble of simulation results; this data base reflects local variations of functionals. Newly developed PRO-CHAIN components deal with transforming and ensemble compression of the data base via a fast principal component analysis with user-controlled accuracy. Essential features are the classification of design parameters into importance and nonlinearity classes in order to reduce the design space and to get an adequate accuracy for an efficient optimization. In this paper we address the importance of this classification and appropriate kinds of classification measures. Another main novel PRO-CHAIN component is the fast and accurate interpolation of new designs on the whole grid. This interpolation works also for nonlinear applications like crash if the design of experiments is adequate for a high-quality metamodel. The interpolation is based on a nonlinear metamodel with radial basis functions accelerated by a specialized principal component decomposition. Summarized, PRO-CHAIN is now able to fully locally analyze a chain consisting of several process steps with regard to sensitivity and robustness and to predict new designs with user-controlled accuracy. In each step, the influence of parameters onto criteria is classified and sensitivity is measured. PRO-CHAIN is able to propagate the essential scatter due to parameter uncertainty locally over the steps, keeping the necessary number of simulation runs small. Additionally, PRO-CHAIN allows for predicting new designs fully locally, allowing for immediate answers to what-if scenarios, without additional time-spending simulation runs. Thus PRO-CHAIN is a very efficient strategy for statistical analysis of process chains, involving parameter uncertainties, in order to get a robustly optimized solution. Recently, we integrated the efficient interpolation method described into DesParO along with LS- DYNA d3plot readers/writers: on one hand, as a so-called “mixing functionality” for constructing and dumping interpolated results, on the other hand into the novel DesParO Geometry Viewer. Now, DesParO allows for an interactive exploration of the design space, connected with direct interpolation and visualization of the new design and its functionals, like thickness, effective plastic strains and damages as well as statistical measures, locally on the whole grid. Results are presented for the forming-to-crash process chain for a ZStE340 metal blank of a B-pillar. In detail, results of importance and nonlinearity classifications in each process step are shown as well as the prediction of new designs by means of DesParO.
How to Use LS-OPT for Parameter Estimation – hot stamping and quenching applications
The “direct” heat transfer problem is one in which material properties and boundary conditions are specified, and LS-DYNA [1] is used to calculate the temperature response of the nodes in the mesh. The “inverse” heat transfer problem is one in which the temperature response of a node point in the mesh (e.g., a surface node) is specified from experimental measurements, and the objective is to calculate material properties and boundary conditions that cause this temperature response. This paper describes how to use LS-OPT [2] to solve the “inverse” heat transfer problem. Applications include: - calculating material parameters for austenite-to-martensite phase change kinetics, fitting material properties to experimental data - calculating contact heat transfer coefficients as a function of temperature and pressure during hot stamping, fitting a function to experimental data - calculating boiling heat transfer coefficients for quenching in liquids , fitting a load curve to experimental data
Deep drawing simulation of α-titanium alloys using LS-Dyna
Titanium alloys have excellent properties for their target applications; however their use is still limited by high price and formability issues. To avoid extensive on-site trials and to cut development costs, a numerical simulation method is developed for the deep drawing process of α-titanium (hexagonal close packed) alloy sheet using LS-Dyna. The Barlat 1989 material model is adopted for modelling the plastic response of the material and the necessary input data is examined. It is found that in order to adequately capture the plastic properties of HCP titanium, load curves are needed both for strain hardening and to capture the strain dependency of the plastic strain ratio. A procedure for determining the material input data from the tensile test results is developed and an exemplary data set is given. To identify a suitable value of the Barlat flow potential exponent m a parametric analysis is carried out using a simulation of the Erichsen cupping test. Forming limit diagrams are adopted for failure prediction, the forming limit curves are determined using the Nakajima method and a simplified procedure for obtaining limiting shear strains on a tensile testing machine is presented. To confirm the method an example of a deep drawn end-cap for a motorcycle exhaust muffler is presented and the simulation compared to the physical forming process with good results.
A New Method for CrachFEM ‘Damage’ Parameter Calculation & Transfer from Autoform to LS-Dyna
Analysis of formability of advanced high strength steel sheets with phenomenologically based failure criteria with separate treatment of instability, shear and normal fracture
Despite their wide application in sheet metal forming analysis, Forming Limit Diagrams cannot supply reliable results for the cases involving non-proportional strain paths or material classes with reduced ductility such as advanced high strength steels (AHSS). Fracture criteria appear as complimentary tools for assessment of formability in these cases. CrachFEM, as an advanced failure model, merges an instability criterion that includes strain hardening and yield loci effects with fracture criteria which monitor damage accumulation for ductile normal fracture and ductile shear fracture separately where stress triaxiality ratio and maximum shear stress dependence are taken into account, respectively. In the present study, rectangular deep drawing of two AHSS classes is studied both experimentally and numerically. Blanks with different rolling directions and blank orientations with respect to the punch are taken into account. Simulations are conducted using CrachFEM failure model and LS-DYNA where texture of the sheet due to rolling is modeled with Hill’48 type anisotropic yield locus. Experimental studies reveal that the failure occurs mainly due to instability with necking whereas in- plane shear stress state in drawing zone seems to be insufficient to create shear fracture. Numerical results show not only the predictive capability of CrachFEM but also regarding weaknesses which needs improvement for better predictions.