Abstract
Additive manufacturing (AM) is an emerging type of production technology to create three-dimensional objects layer-by-layer directly from a 3D CAD model. AM is being extensively used in several areas by engineers and designers. Build orientation is a critical issue in AM since it is associated with the part accuracy, the number of supports required and the processing time to produce the object. This paper presents an optimization approach to solve the part build orientation problem taking into account some characteristics or measures that can affect the accuracy of the part, namely the volumetric error, the support area, the staircase effect, the build time, the surface roughness and the surface quality. A global optimization method, the Electromagnetism-like algorithm, is used to solve the part build orientation problem.
Highlights
Rapid prototyping (RP) is a current technology that manufactures models in less time than any other current method
Over the last two decades, several processes have been introduced in the layered manufacturing, in addition to SLA, based on powder, solids and liquids [5], such as fused deposition modeling (FDM), selective laser sintering, laser cladding, laminated object manufacturing and laser vapour deposition [6]
The type of additive manufacturing technology involved in this work is the FDM, which is defined as a process that creates a three-dimensional object using a thermoplastic filament, which is heated to its melting point and extruded, layer by layer
Summary
Rapid prototyping (RP) is a current technology that manufactures models in less time than any other current method. The work of Zhang et al [20] presented a demonstration to obtain the best orientation considering the minimum volume value of the part supports They used specific restrictions and characteristics by applying different materials to the construction of the part. This work aims to determine the optimal build orientation of 3D CAD models in order to improve the surface finish accuracy, reducing the number of supports generated and the build time, and decreasing the final costs. The characteristics of the model accuracy will be evaluated using six different measures: the volumetric error, the support area, the staircase effect, the build time, the surface roughness and the surface quality of the model.
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