Full length articleOptimized build orientation of additive manufactured parts for improved surface quality and build time
Graphical abstract
Introduction
Additive Manufacturing (AM) processes directly produce a real part from a computer-aided design (CAD) file without the need of a tool. The CAD part has only to be saved as a STL (standard triangulation language) file and depending on machine and process sliced into two dimensional layers of about 20 μm to 300 μm. The assignment of the spatial position in the building chamber and support structures, if needed, are done with machine specific software. In general, raw material is treated or deposited layer-by-layer at the cross section of a part and the building platform lowers each cycle by one layer thickness and material is allocated again. The first commercial process stereolithography (SLA) utilizes photopolymers by curing it with ultraviolet laser. Raw materials can also be powders of polymers, metals and ceramics for processes like selective laser sintering (SLS), selective laser melting (SLM) and electron beam melting (EBM), where the powder is melted by a laser or electron beam. Different from these processes the fused deposition modeling (FDM) uses extruded filaments to deposit layers [1], [2].
The layered structure of all AM processes leads involuntarily to a stair-stepping effect on part surfaces which are curved or tilted in respect to the building platform. Additionally, some processes need support structures to attach the part to the building platform, sustain overhangs or dissipate heat, where residues of removed support structures on the surface are present and deteriorate the surface quality. These effects strongly depend on the orientation of a part surface inside the building process. Hence, the orientation of a whole part has a big influence not only on the resulting surface quality. Also the accuracy of part details and the building time and costs due to the build height are strongly influenced by the build orientation, too [3].
Some work was done in the past years to determine optimized build orientations regarding various target values for different AM processes [4], [5], [6], [7]. Reeves and Cobb presented a surface roughness prediction for layered manufacturing (LM) to optimize the building direction in terms of overall surface roughness for given parts [8]. In particular, they focused on the SLA technology, which is based on the photopolymerization of a liquid resin, therefore having smaller surface roughness as in SLS. Hence, they could express the mean roughness index (Ra) as a function of surface angles only resulting from the stair-stepping effect. For SLS parts, no reasonable correlation between the theoretical and the measured roughness for surface angles between 90° and 180° (i.e. surfaces whose surface normal are directed to the bottom) could be found. The independence of the stair-stepping effect in this case is caused by the filleting effect Bacchewar et al. described [9].
Although a few attempts were done in the past on automation tools for an optimized build orientation for AM, the standard procedure is still dominated by manually packing and rotating parts for build jobs. A basic orientation optimization tool is included in the newest versions of the Magics program, but this tool considers primarily the packing density of parts and not individual build restrictions evoked by the used AM process.
This work starts with a surface topography simulation of SLS parts, which is more complex than the other aforementioned processes FDM, SLA and LOM as the raw material is a powder. This prediction of surface roughness is then used to find the optimal build orientation regarding surface quality.
Section snippets
Theoretical description of the models
In the early years of AM, Frank and Fadel set up the first simple tool for selecting an optimal orientation for parts in the SLA process [13]. They already identified a lot of factors which are influenced by changing the build orientation of a part in AM processes. The surface finish is one of the most important properties which can be optimized and is considered in nearly any work. The stair-stepping effect occurs for all AM processes and can be described with the building direction of a
Case study
For the validation of the model a real part was investigated with the algorithm as described in the previous section. The part, which is a monitor bracket, is shown in Fig. 5 as the CAD model and its implementation as a production part of EOS GmbH. This part was chosen because it has a complex structure but only a few simply structured areas are visible in its application. Hence, specific surface areas for surface quality improvement can be determined and also measured experimentally.
In the
Results and discussion
The comparison of the roughness values predicted by the orientation optimizer and measured from the real parts is shown in the diagram of Fig. 9. The error bars shown here are an approximation of the lower bound defined as 7% of the roughness value. Error bars of roughness depths are normally about 10% but naturally depend strongly on the statistical amount of measurements.
But even a plot with the lower bound approximation shows a very good agreement between the simulated roughness values and
Summary and outlook
An orientation optimization tool for AM processes which uses surface roughness and build height as optimization objectives was presented. The previous surface topography simulation of the LS parts [18] sets up the surface roughness database for the optimization tool. Using the example of a production part of EOS GmbH the function of the tool is demonstrated and the resulting optimization potential regarding surface quality and build height can be recognized. A significant improvement of the
Acknowledgements
The authors want to thank all industry partners of the DMRC as well as the federal state of North Rhine-Westphalia and the University of Paderborn for the financial and operational support within the project “STEP: Surface Topography Analysis and Enhancement of Laser Sintered Parts”. Special thanks go to EOS GmbH for contributing the case study part.
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