Stereolithography (SL) is an additive manufacturing process in which liquid photopolymer resin is cross-linked and converted to solid with a UV laser light source. Traditional models of SL processes do not consider the complex chemical reactions and species transport occurring during photopolymerization and, hence, are incapable of accurately predicting resin curing behavior. In this paper, a 2D photopolymerization model based on ordinary differential equations is presented that incorporates the effects of oxygen inhibition and diffusion during the polymerization process. This model accurately predicts the cured part height when compared to experiments conducted on a mask based stereolithgraphy system. The simulated results also show the characteristic edge curvature as seen in experiments. Parametric studies were conducted to investigate the possibilities to improve the accuracy of the model for predicting the edge curvature.
Stereolithography is an additive manufacturing process in which liquid photopolymer resin is cross-linked and converted to solid polymer with an ultraviolet light source. Exposure Controlled Projection Lithography (ECPL) is a stereolithographic process in which incident radiation, patterned by a dynamic mask, passes through a transparent substrate to cure a photopolymer layer that grows progressively from the substrate surface. In contrast to existing stereolithography techniques, this technique uses a gray-scale projected image, or alternatively a series of binary bit-map images, to produce a three-dimensional polymer object with the desired shape, and it can be used on either flat or curved substrates. Like most stereolithographic technologies, ECPL works in a unidirectional fashion. Calibration constants derived experimentally are fed to the software used to control the system. This unidirectional fabrication method does not, by itself, allow the system to compensate for minor variations, thereby limiting the overall accuracy of the process. We present here a simple, real-time monitoring system based on interferometry, which can be used to provide feedback control to the ECPL process, thus making it more robust and increasing system accuracy. The results obtained from this monitoring system provide a means to better visualize and understand the various phenomena occurring during the photopolymerization of transparent photopolymers.
In this study, a process planning method is presented for the Mask Projection Micro-Stereolithography process with the ability to cure a film of various thicknesses on flat or curved transparent substrates. In this process, incident radiation, patterned by a dynamic mask, passes through a transparent substrate to cure photopolymer resin layers that grow progressively from the substrate surface. When compared to existing Stereolithography techniques, this technique eliminates the necessity of recoating, reducing process time and improving accuracy. A regression model is adopted in this process plan to accurately control the curing front on the substrate. An existing Mask Projection Micro-Stereolithography process planning method is extended to account for radiation transmission through the substrate. The models are verified using experiments.
Jariwala has over nine years of research experience in modeling, simulation, engineering design, and manufacturing process development, with research focus on design of polymer based micro additive manufacturing process. During his Ph.D. studies, he was also a participant of the innovative TI:GER R program (funded by NSF:IGERT), which prepares students to commercialize high impact scientific research results. Dr. Jariwala has participated and led several research projects from funded by NSF, the State of Georgia and Industry sponsors. At Georgia Tech, he is responsible for enhancing corporate support for design courses, managing design and fabrication/prototyping facilities, coordinating the design competitions/expo and teaching design courses, with a strong focus on creating and enabling multidisciplinary educational experiences. AbstractTeam formation for Capstone Design projects is a complex challenge due to the many parameters involved, many of which are intangible. Of all the challenges in organizing a Capstone Design course, team formation is considered the primary one due to its importance in facilitating synergistic partnerships for successful completion of the Capstone project. The authors propose a process for facilitating team creation in an organic fashion amongst students by collecting and providing information about student interest on a per project basis as well as on each student's skills. This allows students to gauge the interest levels of potential teammates for projects they are interested in as well and choose teammates that have complementary skills, thus maximizing their potential for success. To measure the efficacy of this system, post-process data analytics was utilized to gauge the utility of the proposed method to help form student teams. Findings from these analyses are discussed along with opportunities for future improvements.
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