During the development phase of plastic components, simulations are being used to an increasing extent. Against the background of product requirements and the inevitable necessity of conserving resources, the expanded use of simulation tools is an essential part of the solution. Among available methods, but so far underutilized with respect to real-life processes, is the molecular dynamics simulation. By the use of this method it is possible to visualize the physical processes occurring on the microscopic level, as e.g. those that arise during plastics processing. This thesis examines how boundary conditions, which mimic the extrusion blow molding process, affect the behavior of polyethylene on the microscopic level. A mesoscopic model (coarse-graining) is applied to describe the polymer. Initially, this model is verified by determining material properties. The uniaxial tensile test is modeled on the micro-scale to identify parameters such as the elastic modulus, yield stress, and Poisson’s ratio. Additionally, thermal properties, particularly those characterizing the crystallization behavior, are identified. The objective of these investigations is the microscopic observation and quantification of effects that occur during dynamic stretching and crystallization processes. The calculated properties show good agreement with the experimental data, especially regarding the thermal parameters. Qualitatively, the stress-strain behavior is reproduced in alignment with experimentally observed results. However, the short time scale of the simulation models leads to micromechanical behavior that is more extreme than what is monitored on a macroscopic level. By extending the simulation models, biaxial stretching processes are simulated. These stretching processes resemble the situation during the inflation of the parison in the extrusion blow molding process. The examination of various cooling conditions, particularly by the use of mold constraints, is another focus of the investigations. The analysis of the biaxially stretched simulations reveals that disentanglement processes during stretching dominate the further development of polymer systems. It is possible to quantify the dynamics of crystallization processes depending on the degree of stretching and cooling conditions through various parameters (distribution of entanglement points, local orientations). The results indicate that coarse-grained molecular dynamics simulations are able to significantly enhance the micromechanical understanding of local events occurring during plastic processing.
Specifically designed and accurate force fields are of central importance in molecular simulations, as they are often required when investigating new or slightly modified systems. Their parameterization, referred to as force-field parameter optimization, is a complex multi-modal optimization challenge. It requires balancing the parameter’s transferability between various optimization objectives, while their interdependencies often are non-trivial and hard to unravel. This cumulative dissertation addresses this optimization challenge by systematically developing and extending an automatized multi-scale force-field parameter optimization workflow. An important feature is ist modular design that (a) allows the optimization of any amount and combination of force-field parameters towards any type and amount of target properties, and (b) facilitates the extension by additional optimization algorithms or objective functions. First, the workflow’s foundation and a proof-of-concept is provided by simultaneously optimizing the Lennard-Jones parameters towards n-octane’s liquid-phase density (i.e. a multi-molecular, thermodynamic property) and its relative conformational Energies (i.e. a single molecular structural property). By showing that the applicability of the force field was expanded, the simultaneous multi-scale optimization workflow is established. Then, the optimization workflow’s hyperparameters are fine-tuned to improveits results and it is shown that by reasonably balancing the optimization objectives using weighting factors the previously introduced errors are reduced. Next, the optimization procedure’s efficiency is increased by substituting the most time-consuming molecular dynamics simulations by machine learning surrogate models. Those simulations are required repeatedly throughout the optimization, and due to the workflow’s iterative nature, they need to be performed just-in-time. By substituting them with machine learning surrogate models the required run time is approximately reduced by 20-fold. Additionally, guidelines for the model’s training and selection are included. Subsequently, as a challenging test of the workflow, the Lennard-Jones Parameters for 1-bromobutane and 2-bromobutane are optimized. They exhibit a σ-hole allowing halogen bonding, which is subject to current research, that can be supported by accurate simulation models. It is shown that the modeling is improved by the herein presented multi-scale optimization approach, but that further refinements with respect to the workflow and the modeling are necessary. In conclusion, promising approaches to improve the optimization workflow and the modeling are suggested.
