This article investigates the state-of-the-art of polyjet 3D printing of polymers and multi-material structures, with an emphasis on its applications in a range of industrial domains, including aerospace, architecture, toy fabrication, and medical field. While significant research and development in the field of additively manufactured (AM) multi-material and reinforced composite structures have been carried out during the previous decade, the need of the hour is to utilize a single manufacturing platform which would not only help to govern the composition, shape, and characteristics of multi-material 3D printed objects at the microscopic level but would also help industries to replace the conventional AM manufacturing methods and optimize their mechanical performance. Significant advancements in polyjet 3D printing of fiber-reinforced and functionally graded structures with numerous modifications in material composition are reviewed, which may revolutionize the industrial sector. Numerous polyjet printing parameters such as accuracy, printing speed, photo-curing effect, build orientation, layer thickness, print angles, and post-processing are comprehensively discussed, and best outputs to optimize the mechanical performance and enhance the accuracy of the polyjet 3D printing products are highlighted. FEA (finite element analysis) models and analytical relations for multi-material 3D printed structures are presented to predict and enhance the overall performance of polyjet fabrication. Along with the benefits of polyjet manufacturing, a few of the limitations and challenges of polyjet AM are addressed which would benefit the reader to conduct further research in this field and enhance fabrication quality significantly. Vivid comparisons with other multi-material AM fabrication techniques such as FDM, SLA, and SLS with their brief discussion, merits, and demerits are done.
This paper presents an optimization technique to dynamically balance the planar mechanisms in which the shaking forces and shaking moments are minimized using the genetic algorithm (GA). A dynamically equivalent system of point-masses that represents each rigid link of a mechanism is developed to represent link's inertial properties. The shaking force and shaking moment are then expressed in terms of the point-mass parameters which are taken as the design variables. These design variables are brought into the optimization scheme to reduce the shaking force and shaking moment. This formulates the objective function which optimizes the mass distribution of each link. First, the problem is formulated as a single objective optimization problem for which the genetic algorithm produces better results as compared to the conventional optimization algorithm. The same problem is then formulated as a multi-objective optimization problem and multiple optimal solutions are created as a Pareto front by using the genetic algorithm. The masses and inertias of the optimized links are computed from the optimized design variables. The effectiveness of the proposed methodology is shown by applying it to a standard problem of four-bar planar mechanism available in the literature.
A two-stage optimization method for optimal dynamic design of planar mechanisms is presented in this paper. For dynamic balancing, minimization of the shaking force and the shaking moment is achieved by finding optimum mass distribution of mechanism links using the equimomental system of point-masses in the first stage of the optimization. In the second stage, their shapes are synthesized systematically by closed parametric curve, i.e. cubic B-spline curve corresponding to the optimum inertial parameters found in the first stage. The multi-objective optimization problem to minimize both the shaking force and the shaking moment is solved using evolutionary optimization algorithm – “Teaching-learning-based optimization (TLBO) algorithm”. The computational performance of TLBO algorithm is compared with another evolutionary optimization algorithm, i.e. genetic algorithm.
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