A tool for computer-aided orientation selection in additive manufacturing processes

Design for Additive Manufacturing: Trends, opportunities, considerations, and constraints

“…Since the discretization in modern AM processes is rarely isotropic, the surface roughness and resulting material properties [113][268] [274] are also usually anisotropic. One common method to address these anisotropies is to modify the part [25][118] [328] or assembly [232] orientation to minimize their impact. Other options include finishing operations after each layer [362] (Fig.…”

A statistical method for build orientation determination in additive manufacturing

“…However, it has limitations to deal with high dimensional hypersurface fitting where more than three evaluation criteria should be considered and more benchmarking datasets should be provided for fitting. To conduct exhaustive computation without these tough considerations, in Hur and Lee (1998), Hur et al (2001), Choi and Samavedam (2002), Masood et al (2003), Pandey et al (2004), Kim and Lee (2005), Canellidis et al (2009), Padhye and Deb (2011), Strano et al (2011), Armillotta et al (2013), Paul and Anand (2015), advanced evolutionary algorithms, e.g. genetic algorithm (GA), were used to rotate an STL model in 3D with a given rotation step length, and the evaluation criteria were used in fusion, in sequence or in a hierarchical way as objective functions to advance the convergence in searching the optimal orientation.…”

“…Recently, except for these evaluation criteria mentioned above, new criteria emerged with specific application needs and process requirements. In Armillotta et al (2013), authors proposed to use local feature integrity as an evaluation criterion, which shows the importance of paying attention to the regional area of an STL model but not always the whole STL model. In Ulu et al (2015), mechanical properties of the part were considered together with the surface quality and other factors.…”

“…People usually care more about key features/areas regarding desired functions. In Armillotta et al (2013), Moroni et al (2015), key features were emphasized more in the orientation determination process. For a given part model, users or designers mainly concentrate on the functional geometries, while the process planners may also take other nonfunctional geometric shapes into consideration, as they are important to the production results.…”

“…This means the evaluation iteration is not conducted each time in the level of triangle facet computation. However, in the exhaustive computation-based optimization methods as proposed in Hur and Lee (1998), Hur et al (2001), Choi and Samavedam (2002), Masood et al (2003), Pandey et al (2004), Kim and Lee (2005), Canellidis et al (2009), Padhye andDeb (2011), Strano et al (2011), Armillotta et al (2013) and Paul and Anand (2015), for each evaluation in the optimization iterations, all the facets of an STL model should be computed when applying different constraints or objective functions. Hence, it is not difficult to imagine that the proposed statistical evaluation method could reduce great computation as compared with those methods, especially when facing complex STL models with large number of facets.…”