The increasing demand for micro-injection molding process technology and the corresponding micro-molded products have materialized in the need for models and simulation capabilities for the establishment of a digital twin of the manufacturing process. The opportunities enabled by the correct process simulation include the possibility of forecasting the part quality and finding optimal process conditions for a given product. The present work displays further use of micro-injection molding process simulation for the prediction of feature dimensions and its optimization and microfeature replication behavior due to geometrical boundary effects. The current work focused on the micro-injection molding of three-dimensional microparts and of single components featuring microstructures. First, two virtual a studies were performed to predict the outer diameter of a micro-ring within an accuracy of 10 µm and the flash formation on a micro-component with mass a 0.1 mg. In the second part of the study, the influence of microstructure orientation on the filling time of a microcavity design section was investigated for a component featuring micro grooves with a 15 µm nominal height. Multiscale meshing was employed to model the replication of microfeatures in a range of 17–346 µm in a Fresnel lens product, allowing the prediction of the replication behavior of a microfeature at 91% accuracy. The simulations were performed using 3D modeling and generalized Navier–Stokes equations using a single multi-scale simulation approach. The current work shows the current potential and limitations in the use of micro-injection molding process simulations for the optimization of micro 3D-part and microstructured components.

A special mold provided with a glass window was used in order to directly evaluate the flow progression during the filling phase of the injection molding process in a thin-wall cavity and to validate the simulation of the process with particular focus on the hesitation effect. The flow of the polymer was recorded at 500 frames per second using a high-speed camera (HSC). Two unfilled thermoplastic polymers, acrylonitrile butadiene styrene (ABS), and polypropylene (PP), were used to fill two different 50 mm × 18 mm staircase geometry cavities, which were specifically designed to evaluate the hesitation effect with thicknesses of 1500, 1250, 1000, 750, 500 µm (cavity insert no. 1) and 1500, 1200, 900, 600, 300 µm (cavity insert no. 2). In addition to the video recordings, the simulations were validated using the timings and the data obtained by three pressure sensors and two thermocouples located in the cavity. For each injection cycle recorded on camera the machine data were collected to carefully implement the correct boundary conditions in the simulations. The analysis of the video recordings highlighted that flow progression and hesitation were mainly influenced not only by the thickness, but also by the velocity and the material type. The simulation results were in relatively good agreement with the experiments in terms of flow pattern and progression. Filling times were predicted with an average relative error deviation of 2.5% throughout all the section thicknesses of the cavity. Lower accuracies in terms of both filling times and injection pressure were observed at increasingly thinner sections.

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Engineering of surface structure to obtain specific anisotropic reflectance properties has interesting applications in large scale production of plastic items. In recent work, surface structure has been engineered to obtain visible reflectance contrast when observing a surface before and after rotating it 90 degrees around its normal axis. We build an analytic anisotropic reflectance model based on the microstructure engineered to obtain such contrast. Using our model to render synthetic images, we predict the above mentioned contrasts and compare our predictions with the measurements reported in previous work. The benefit of an analytical model like the one we provide is its potential to be used in computer vision for estimating the quality of a surface sample. The quality of a sample is indicated by the resemblance of camera-based contrast measurements with contrasts predicted for an idealized surface structure. Our predictive model is also useful in optimization of the microstructure configuration, where the objective for example could be to maximize reflectance contrast.