Fast slicing orientation determining and optimizing algorithm for least volumetric error in rapid prototyping

Abstract

Rapid prototyping fabricates physical prototypes from three-dimensional designing models using the additive process with layers. Aims at reducing the inevitable volumetric error induced in phrase of model slicing which impacts the shape accuracy of fabricated entity, a fast determining scheme of optimal slicing orientation for least volumetric error is proposed. The work analyses the staircase effect between two consecutive layers, then infers a direct computing formula of volume deviation of a whole model. Introduces the term of area weighted normal to express the significant effect of facet area on volumetric error and converts the optimal orientation determining problem to the least absolute deviation linear regression issue. Employs prominent components analysis on weighted normal set to obtain an approximate orientation efficiently, then optimizes the solution through few searchings in neighboring orientation space. The validity and efficiency of the algorithm are evaluated on several examples. Results demonstrate that proposed algorithm consumes less than 32 % of computation load and adaptively obtains the optimal slicing orientation.

Error Analysis: How Precise is Fused Deposition Modeling in Fabrication of Bone Models in Comparison to the Parent Bones?

“…25 Research toward minimizing the errors in RP might improve the accuracy of the generated 3D models. 26 27 …”

Determination of optimal build orientation for additive manufacturing using Muirhead mean and prioritised average operators

“…Used MOO techniques Optimised objectives Cheng et al (1995) Weighted sum function Part accuracy; build time Lan et al (1997) Self-developed algorithm Surface quality; build time; complexity of support structure Alexander et al (1998) Self-developed algorithm Cost; support volume; contact area with support; surface accuracy McClurkin and Rosen (1998) Self-developed algorithm Build time; accuracy; surface finish Hur and Lee (1998) Genetic algorithm Part accuracy; build time; support structure volume Xu et al (1999) Self-developed algorithm Build cost; build time; building inaccuracy; surface finish Hur et al (2001) Genetic algorithm Build time; surface quality Masood et al (2003) Generic mathematical algorithm Volumetric error Thrimurthulu et al (2004) Genetic algorithm Surface finish; build time Pandey et al 2004Genetic algorithm Surface roughness; build time Kim and Lee (2005) Genetic algorithm Post-processing time and cost Ahn et al (2007) Genetic algorithm Post-machining time Canellidis et al (2009) Genetic algorithm Build time; surface roughness; post-processing time Padhye and Deb (2011) Genetic algorithm and particle swarm algorithm Surface roughness; build time Strano et al (2011) Genetic algorithm Surface roughness; energy consumption Zhang and Li (2013) Genetic algorithm Volumetric error Paul and Anand (2015) Genetic algorithm Cylindricity and flatness errors; support structure volume Ezair et al (2015) Self-developed algorithm Support structure volume Delfs et al (2016) Self-developed algorithm Surface quality; build time Luo and Wang (2016) Principal component analysis Volumetric error Zhang et al (2017) Genetic algorithm Build time; build cost; production quality Brika et al (2017) Genetic algorithm Surface roughness; build time; build cost; yield strength; tensile strength; elongation; vickers hardness; amount of support structure Chowdhury et al (2018) Genetic algorithm Support structure volume; support structure accessibility; surface area contacting support; number of build layers; number of small openings; number of sharp corners; mean cusp height Mi et al (2018) Point clustering algorithm Number of material changes Jaiswal et al (2018) Surrogate model Material error; geometric error Al-...…”

“…attributes of build orientations), such as design requirements, build cost, build time, part accuracy, mechanical properties, and thermodynamic properties, to be optimal from an infinite number of theoretical orientations or a certain number of alternative orientations. The methods based on this principle mainly include the methods proposed by Cheng et al (1995), Lan et al (1997), Alexander et al (1998), McClurkin and Rosen (1998), Hur and Lee (1998), Xu et al (1999), Hur et al (2001), Masood et al (2003), Thrimurthulu et al (2004), Pandey et al (2004), Kim and Lee (2005), Ahn et al (2007), Canellidis et al (2009), Padhye and Deb (2011), Strano et al (2011), Zhang and Li (2013), Paul and Anand (2015), Ezair et al (2015), Delfs et al (2016), Luo and Wang (2016), Zhang et al (2017), Brika et al (2017), Chowdhury et al (2018), Mi et al (2018), Jaiswal et al (2018), Al-Ahmari et al (2018), Huang et al (2018), Raju et al (2018), and Golmohammadi and Khodaygan (2019). A brief summarisation of these methods is provided in Table 1.…”

Automatic determination of part build orientation for laser powder bed fusion

“…Representative examples are the estimation models in Masood, Rattanawong, and Iovenitti (2003), Zhang and Li (2013), and Luo and Wang (2016). In the proposed method, the model in Luo and Wang (2016) is used to estimate the total volumetric error of an LPBF part in a given build orientation O. Firstly, the volumetric error of each facet in the STL model of an LPBF part in O is estimated via geometric analysis. The total volumetric error of the part in O is then obtained by calculating the sum of the volumetric error of all facets.…”