- 4:55 PM

Multiscale Modeling and Analysis: Bridge the Gap between Material Design and Material Application in Surface Coating

Jie Xiao and Yinlun Huang. Department of Chemical Engineering and Materials Science, Wayne State University, 5050 Anthony Wayne Drive, Detroit, MI 48202

Coating quality improvement is always pursued by surface coating industries. Generally speaking, contributions to this theme can be classified into two research areas: coating material design and application process optimization. The two types of activities have not been effectively integrated. Usually, material scientists are interested in mesoscale and microscale material structures and focus on revealing structure-property relationship through a series of experimental tests in labs, which is costly and time consuming. Chemists do not possess deep knowledge in final coating quality through material application in real setting. On the other hand, process system engineers are interested in macroscale material application process and work for improved application with given coating materials. The lack of adequate knowledge in coating properties has made them difficult in addressing various material-related quality problems. The gap between these two types of research activities need be bridged.

In this work, a general multiscale modeling and analysis framework is proposed to bridge the coating material design and application process optimization researches. The principal components in this framework can be presented in the following three successive steps: (1) to determine an appropriate finer scale whose material structure affects directly the specific coating quality, (2) to link this finer scale structure with the process that produces it, and (3) to establish a relationship between this finer-scale structure with the coating quality. Overall, multiscale modeling and simulation will generate finer scale structures and these structures determine the coating quality. A direct link between the material/process and final coating quality has been established through computer simulation in this way. As a result, further product/process analysis and optimization can be carried out.

Following proposed framework, two cases about automotive polymeric coating are investigated. The first case is macro-to-micro modeling on curing. Due to continued customer demand for better quality and more durable coating, interest in improving the physical/chemical damage resistance of automotive coating is intensified. The material used (e.g., acrylics, crosslinkers, and catalysts) and oven curing operation (curing temperature and time) will determine crosslinked network structures, which affects final coating scratch resistance property. In this work, CFD associated with molecular dynamic (MD) simulation will be used to reveal crosslinked structures. The CFD simulation will give macroscopic dynamic curing conditions, while the MD model will generate desired network structure. Scratch resistance properties can be derived from network structure through a statistic approach. The study provides paint designers with valuable recommendations in conducting more detailed study on paint formulation selection, which may effectively reduce their synthesis and testing effort to the selected best performers predicted by the simulation. The second case is macro-to-meso modeling for metallic coating. Meso-structures (flake size, size distribution, surface roughness, and orientation), which directly affect the final coating appearance (gloss, color and texture), are focuses in this case. In an effort to link the coating meso-structure to the processes that produce it, the first-principles-based macroscopic process model and rule-based Monte Carlo techniques are adopted. The paint spray and oven curing processes are intensively studied. For example, in oven curing modeling, the dynamics for the temperature, solvent content, paint viscosity, coating thickness and the resulting flake orientation changes are all taken into account. Consequently, the following objectives can be achieved: (1) predict the coating appearance with specified paint formulation and application processes, (2) identify required processing conditions, if the paint formulation and the desired coating appearance are given, and (3) carry out paint formulation optimization and optimal coating quality control (e.g., optimal flake size distribution, optimal flake orientation control, etc).