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Experimental Methodology for Rapid Tooling |
Predicting the number of parts that can be molded in a SLA tool is very difficult due to the complexity of the molding process and the nature of SLA resins. The goal of this project is to reliably mold 50 parts in SLA tools. To do so, we must understand the failure mechanisms of SLA tools and relate these failures to molding process variables, mold material properties, part geometries, and the polymer being molded. Due to our research over the past 4 years, we have identified the predominant failure mechanisms of SLA tools: flow failures during injection, fatigue failures due to thermal and mechanical cycling of the tool. Jon Colton is leading this project. Three Masters students worked on various aspects of the project. Joe Crawford focused on flow failures and quantifying their causes. Giang Pham and Vincent Rodet concentrated on different aspects of fatigue failures. He performed an extensive set of computational simulations using ANSYS for determining temperature and stress distributions over a series of shots. Using C-Mold, the pressure distribution on mold features was also determined. A variety of feature height and aspect ratios were tested. Physical experiments were performed in order to compare with the computational predictions. Again, higher aspect ratio features failed earlier. Giang developed an improved ejection force model based on part geometry and SL process variables, including feature sizes, draft angle, Young’s modulus and Poisson’s ratio of the mold material, layer thickness, line width compensation, and border overcure. A series of physical experiments were performed to test the analytical model. This is the first time that an ejection force model based solely on material properties and geometry has included both the mold core and the part being molded. This allows the ejection force for any arbitrary combination of mold and part materials to be determined. Vincent developed correlations between measured properties of SL molds and the injection molding processing conditions so that we have a better understanding of failures and can predict mold failure. The correlation identified testing procedures for new materials. Also, these results help to minimize the effects of fatigue and maximize tool life. Specific factors and properties investigated included: mold build orientation in the SL machine, physical aging of the material during molding, tensile fatigue tests at elevated temperatures to test macroscopic fatigue failure due to ejection, fracture tests at different temperature and thermal aging levels to address crack initiation, and the effect of additional curing processes. All three students graduated in Spring 2001. As a result, we can conclude that SL molds fail during either injection of the molten polymer or ejection of the molded part. We developed models of SL mold failure under both conditions that correspond well to physical experiments. As a result, we have identified the significant factors in the SL and injection molding processes that influence mold life (the number of parts that can be molded before mold breakage). Furthermore, these models can be used to predict mold life. These results aid designers and molders who are considering the usage of SL molding by providing guidance in: assessing the suitability of SL molds for molding specific parts, fine-tuning part and mold designs to facilitate SL molding, setting process variables for the SL and molding processes |
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