Dynamic force analysis for bottom-up projection-based Additive Manufacturing using finite element analysis
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Additive Manufacturing (AM) is a new manufacturing technology that fabricates complex three dimensional objects by adding materials layer upon layer. These materials can be plastics, metals, or even human tissues. Using additive manufacturing has advantages, e.g. there is no need to build a mold, changes in designs can be made without additional cost, constraints on tooling will be eliminated, and design and manufacturing can both take place at your living place at your free time. All these advantages make this technology suitable for realizing new concepts like just-in-time manufacturing, personal fabrication, food printing, organ printing etc. Economist magazine has titled additive manufacturing "the third industrial revolution". However, despite all the significant advancements and applications of this technology, especially in the last decade, some technical challenges still need to be addressed. Poor surface quality, expensive machines, low reliability and repeatability, and limited materials compatible to this technology are some of the shortages of AM. Any attempt for tackling these problems and developing more reliable and inexpensive additive manufacturing processes, makes us closer to the complete realization of a third industrial revolution. In this research, we report a robust optimization-based method for modeling bottom-up (constrain-surface) additive manufacturing process using finite element analysis. The result of this thesis will be used as a basis for designing an online dynamic force analysis to prevent the failure and break of cured photopolymer parts in bottom-up projection-based AM machines. Photopolymerization processes that can be considered as the first commercialized additive manufacturing technology, combine photochemistry and laser technology to build parts from photopolymer resins. These processes have the best accuracy and surface quality. Based on the material filling mechanism, photopolymerization processes can be divided into two main categories: free surface and constrain-surface (bottom-up). The constrain-surface mechanism has been developed recently and widely used in industry. It gives better vertical resolution, higher material filling rate, less production time, and less photopolymer waste. These properties make it a suitable candidate for producing multi-material products using photopolymerization. However, during the pulling up stage, the substantial force generated between the formed part and the material container has high risk of breaking the part and therefore reduces the process reliability. To mitigate this problem, substantially slow motion is set to improve the reliability. However, it slows down the process. An adaptive mechanics-based process should be developed to control the pulling up process and achieve a reliable process. However, not so many research works have been done in this important area. In this thesis, a predictive model and online monitoring system has been proposed to adaptively adjust the motion profile according to separation mechanism in such a way that separation is efficiently achieved without breaking the fabricated part. The whole process was modeled as a FEA model and the results of the simulation were in line with the real experimental data. In addition, a new MATLAB-based optimization methodology was proposed to evaluate and optimize mechanical parameters that are difficult to calculate using experimental mechanical tests e.g. cohesive stiffness or damage parameters. The model in the future works will be integrated into the bottom-up projection-based machine to design the online predictive control system. The results can dramatically improve the repeatability of Bottom-up photo-polymerization, where the knowledge to be obtained will benefit other AM processes at large. The improved reliability and repeatability together with the improved speed, in turn, will significantly reduce the material waste and energy usage, which is in full compliance with recent trend toward green economy.