<p>Powder bed fusion (PBF)
process is a metal additive manufacturing process, which can build parts with
any complexity from a wide range of metallic materials. Research in the PBF
process predominantly focuses on the impact of a few parameters on the ultimate
properties of the printed part. The lack of a systematic approach to optimizing
the process parameters for a better performance of given material results in a
sub-optimal process limiting the potentialof the application. This process
needs a comprehensive study of all the influential parameters and their impact
on the mechanical and microstructural properties of a fabricated part.
Furthermore, there is a need to develop a quantitative system for mapping the
material properties and process parameters with the ultimate quality of the
fabricated part to achieve improvement in the manufacturing cycle as well as
the quality of the final part produced by the PBF process. To address the
aforementioned challenges, this research proposes a framework to optimize the process
for 316L stainless steel material. This framework characterizes the influence
of process parameters on the microstructure and mechanical properties of the
fabricated part using a series of experiments. These experiments study the
significance of process parameters and their variance as well as study the microstructure
and mechanical properties of fabricated parts by conducting tensile, impact,
hardness, surface roughness, and densification tests, and ultimately obtain the
optimum range of parameters. This would result in a more complete understanding
of the correlation between process parameters and part quality. Furthermore,
the data acquired from the experimentsare employed to develop an intelligent
parameter suggestion multi-layer feedforward (FF) backpropagation (BP)
artificial neural network (ANN). This network estimates the fabrication time
and suggests the parameter setting accordingly to the user/manufacturers
desired characteristics of the end-product. Further, research is in progress to
evaluate the framework for assemblies and complex part designs and incorporate
the results in the network for achieving process repeatability and consistency.</p><br>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/9036818 |
| Date | 15 August 2019 |
| Creators | Mallikharjun Marrey (7037645) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/A_FRAMEWORK_FOR_OPTIMIZING_PROCESS_PARAMETERS_IN_POWDER_BED_FUSION_PBF_PROCESS_USING_ARTIFICIAL_NEURAL_NETWORK_ANN_/9036818 |
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