Utilisation of artificial neural networks to rationalise processing windows in directed energy deposition applications
The application of Directed Energy Deposition (DED) when using new materials or new instruments, requires significant empirical testing to define a suitable or optimum process operation window. Determining the ideal DED parameters is challenging due to the complexity of the deposition process being...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-01-01
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| Series: | Materials & Design |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127520308789 |
| _version_ | 1818573734730006528 |
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| author | D.R. Feenstra A. Molotnikov N. Birbilis |
| author_facet | D.R. Feenstra A. Molotnikov N. Birbilis |
| author_sort | D.R. Feenstra |
| collection | DOAJ |
| description | The application of Directed Energy Deposition (DED) when using new materials or new instruments, requires significant empirical testing to define a suitable or optimum process operation window. Determining the ideal DED parameters is challenging due to the complexity of the deposition process being dynamic in nature, with a multitude of parameters being highly influential on the resultant melt pool dimensions and the subsequent evolution of solidification. The present study seeks to rationalise the notion of a processing window by using artificial neural networks (ANN) to elucidate the complex interaction between the input parameters - specifically the relationship between the energy density of the laser and material deposition rate on the shape of single-track deposits. Herein, cross-sectional data was collected from single tracks of Inconel 625, Hastelloy X and stainless steel 316 L deposited onto mild steel substrates; whilst using a matrix of process parameters. The ANN was used to model the interplay between laser power, scan speed, laser beam diameter, material deposition rate and material type. The network was then used to visualize a theoretical relationship between the volumetric energy density and the energy required to melt a specific amount of the supplied powder. |
| first_indexed | 2024-12-15T00:16:52Z |
| format | Article |
| id | doaj.art-99afb204e2084593958e64b244db0233 |
| institution | Directory Open Access Journal |
| issn | 0264-1275 |
| language | English |
| last_indexed | 2024-12-15T00:16:52Z |
| publishDate | 2021-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj.art-99afb204e2084593958e64b244db02332022-12-21T22:42:27ZengElsevierMaterials & Design0264-12752021-01-01198109342Utilisation of artificial neural networks to rationalise processing windows in directed energy deposition applicationsD.R. Feenstra0A. Molotnikov1N. Birbilis2Department of Materials Science and Engineering, Monash University, 3800, VIC, Australia; Woodside Innovation Centre, Monash University, 3800, VIC, Australia; Corresponding author at: Department of Materials Science and Engineering, Monash University, 3800, VIC, Australia.RMIT Centre for Additive Manufacturing, School of Engineering, Royal Melbourne Institute of Technology, Melbourne 3053, VIC, AustraliaCollege of Engineering & Computer Science, The Australian National University, 2601, ACT, AustraliaThe application of Directed Energy Deposition (DED) when using new materials or new instruments, requires significant empirical testing to define a suitable or optimum process operation window. Determining the ideal DED parameters is challenging due to the complexity of the deposition process being dynamic in nature, with a multitude of parameters being highly influential on the resultant melt pool dimensions and the subsequent evolution of solidification. The present study seeks to rationalise the notion of a processing window by using artificial neural networks (ANN) to elucidate the complex interaction between the input parameters - specifically the relationship between the energy density of the laser and material deposition rate on the shape of single-track deposits. Herein, cross-sectional data was collected from single tracks of Inconel 625, Hastelloy X and stainless steel 316 L deposited onto mild steel substrates; whilst using a matrix of process parameters. The ANN was used to model the interplay between laser power, scan speed, laser beam diameter, material deposition rate and material type. The network was then used to visualize a theoretical relationship between the volumetric energy density and the energy required to melt a specific amount of the supplied powder.http://www.sciencedirect.com/science/article/pii/S0264127520308789Directed energy depositionNeural networkProcess optimisationSS316LHastelloy XInconel 625 |
| spellingShingle | D.R. Feenstra A. Molotnikov N. Birbilis Utilisation of artificial neural networks to rationalise processing windows in directed energy deposition applications Materials & Design Directed energy deposition Neural network Process optimisation SS316L Hastelloy X Inconel 625 |
| title | Utilisation of artificial neural networks to rationalise processing windows in directed energy deposition applications |
| title_full | Utilisation of artificial neural networks to rationalise processing windows in directed energy deposition applications |
| title_fullStr | Utilisation of artificial neural networks to rationalise processing windows in directed energy deposition applications |
| title_full_unstemmed | Utilisation of artificial neural networks to rationalise processing windows in directed energy deposition applications |
| title_short | Utilisation of artificial neural networks to rationalise processing windows in directed energy deposition applications |
| title_sort | utilisation of artificial neural networks to rationalise processing windows in directed energy deposition applications |
| topic | Directed energy deposition Neural network Process optimisation SS316L Hastelloy X Inconel 625 |
| url | http://www.sciencedirect.com/science/article/pii/S0264127520308789 |
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