Significant advancements in the field of additive manufacturing (AM) have increased the popularity of AM in mainstream industries. The dimensional accuracy and surface finish of parts manufactured using AM depend on the AM process and the accompanying process parameters. Part build orientation is one of the most critical process parameters, since it has a direct impact on the part quality measurement metrics such as cusp error, manufacturability concerns for geometric features such as thin regions and small fusible openings, and support structure parameters. In conjunction with the build orientation, the cyclic heating and cooling of the material involved in the AM processes lead to nonuniform deformations throughout the part. These factors cumulatively affect the design conformity, surface finish, and the postprocessing requirements of the manufactured parts. In this paper, a two-step part build orientation optimization and thermal compensation methodology is presented to minimize the geometric inaccuracies resulting in the part during the AM process. In the first step, a weighted optimization model is used to determine the optimal build orientation for a part with respect to the aforementioned part quality and manufacturability metrics. In the second step, a novel artificial neural network (ANN)-based geometric compensation methodology is used on the part in its optimal orientation to make appropriate geometric modifications to counteract the thermal effects resulting from the AM process. The effectiveness of this compensation is assessed on an example part using a new point cloud to part conformity metric and shows significant improvements in the manufactured part's geometric accuracy.
Additive Manufacturing (AM) incorporates a group of processes which utilize a layer-based material deposition approach to manufacture parts. These processes are now widely used in the industry as the primary manufacturing process for fabricating high precision parts. The dimensional accuracy of the parts and components manufactured using AM depend mainly on the type of Additive process used and the process parameters. The part build orientation is one of the principal process parameters which has a direct influence on the staircase effect and volume of support structure required for building the part. These factors eventually contribute to the surface finish, dimensional accuracy, and the post-processing requirements. In this paper, an optimization model is developed to obtain the build orientation which will minimize the support structure volume as well as support contact area and maximize the support structure removal while satisfying all the GD&T callouts. The mathematical correlation of cylindricity, flatness, parallelism, and perpendicularity tolerances with build orientation is analyzed and developed. A voxel-based approach is employed to calculate support structure requirement at any part build orientation, while a ray tracing approach is used to calculate the accessibility of supports and identifying removable supports.
Additive manufacturing (AM) provides tremendous advantage over conventional manufacturing processes in terms of creative freedom, and topology optimization (TO) can be deemed as a potential design approach to exploit this creative freedom. To integrate these technologies and to create topology optimized designs that can be easily manufactured using AM, manufacturing constraints need to be introduced within the TO process. In this research, two different approaches are proposed to integrate the constraints within the algorithm of density-based TO. Two AM constraints are developed to demonstrate these two approaches. These constraints address the minimization of number of thin features as well as minimization of volume of support structures in the optimized parts, which have been previously identified as potential concerns associated with AM processes such as powder bed fusion AM. Both the manufacturing constraints are validated with two case studies each, along with experimental validation. Another case study is presented, which shows the combined effect of the two constraints on the topology optimized part. Two metrics of manufacturability are also presented, which have been used to compare the design outputs of conventional and constrained TO.
Additive manufacturing (AM) processes enable the creation of structures having complex geometry. Among such structures are lattice structures which offer great potential for designing lightweight structures. The combination of AM and cellular lattice structures provide promising design solutions in terms of material usage, cost and part weight. However, the geometric complexity of the structures calls for a robust methodology to incorporate the lattices in parts designs and create optimum lightweight designs. This thesis proposes a novel method for designing lightweight variable-density lattice structures using gyroids. The parametric 3D implicit function of gyroids has been used to control the shape and volume fraction of the lattice.The proposed method is then combined with the density distribution information from topology optimization algorithm. A density mapping and interpolation approach is proposed to map the output of topology optimization into the parametric gyroids structures which results in an optimum light-weight lattice structure with uniformly varying densities across the design space.The proposed methodology has been validated with two test cases.ii
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