dc.contributor.author |
Maodzeka, DK
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dc.contributor.author |
Olakanmi, EO
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dc.contributor.author |
Mosalagae, M
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dc.contributor.author |
Hagedorn-Hansen, D
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dc.contributor.author |
Pityana, Sisa L
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dc.date.accessioned |
2022-12-05T06:32:46Z |
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dc.date.available |
2022-12-05T06:32:46Z |
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dc.date.issued |
2022-08 |
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dc.identifier.citation |
Maodzeka, D., Olakanmi, E., Mosalagae, M., Hagedorn-Hansen, D. & Pityana, S.L. 2022. Hybrid optimisation studies on the microstructural properties and wear resistance of maraging steel 1.2709 parts produced. <i>Laser Powder Bed Fusion.</i> http://hdl.handle.net/10204/12555 |
en_ZA |
dc.identifier.uri |
http://dx.doi.org/10.2139/ssrn.4192936
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dc.identifier.uri |
http://hdl.handle.net/10204/12555
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dc.description.abstract |
Improper selection of laser powder bed fusion (LPBF) process parameters tends to result in poor quality parts which imposes limitations with respect to the mechanical performance due to process induced defects. To address this LPBF processing challenge, this study employs a hybrid optimisation technique which combines artificial neural network (ANN) and response surface methodology (RSM) models. The models were employed for predicting the microstructural properties (porosity, microhardness and amount of martensite phase composition) and mechanical characteristic (wear resistance) of LPBF manufactured maraging steel 1.2709 parts as a function of a combination of process parameters (scan speed, laser power and hatch spacing). Both ANN and RSM models had a high tracking ability. However, ANN showed better prediction accuracy than RSM. The most desirable optimum LPBF processing parameters for minimum wear volume and porosity while maintaining maximum microhardness and martensite phase composition were found at volumetric energy density (VED) of 77 J/mm 3 (laser power = 165 W, scan speed = 784 mm/s and hatch spacing = 91 µm). Optimum quality properties predicted by the RSM and ANN models were consistent with confirmatory experiment results. |
en_US |
dc.format |
Fulltext |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.uri |
https://ssrn.com/abstract=4192936 |
en_US |
dc.source |
Laser Powder Bed Fusion |
en_US |
dc.subject |
Artificial Neural Network |
en_US |
dc.subject |
ANN |
en_US |
dc.subject |
Laser powder bed fusion |
en_US |
dc.subject |
LPBF |
en_US |
dc.subject |
Maraging steel 1.2709 |
en_US |
dc.subject |
Response surface methodology |
en_US |
dc.subject |
RSM |
en_US |
dc.subject |
Wear resistance |
en_US |
dc.title |
Hybrid optimisation studies on the microstructural properties and wear resistance of maraging steel 1.2709 parts produced |
en_US |
dc.type |
Article |
en_US |
dc.description.pages |
51 |
en_US |
dc.description.note |
Due to copyright restrictions, the attached PDF file only contains the preprint version of the article. For access to the published version, please consult the publisher's website: http://dx.doi.org/10.2139/ssrn.4192936 |
en_US |
dc.description.cluster |
Manufacturing |
en_US |
dc.description.impactarea |
Laser Enabled Manufacturing |
en_US |
dc.identifier.apacitation |
Maodzeka, D., Olakanmi, E., Mosalagae, M., Hagedorn-Hansen, D., & Pityana, S. L. (2022). Hybrid optimisation studies on the microstructural properties and wear resistance of maraging steel 1.2709 parts produced. <i>Laser Powder Bed Fusion</i>, http://hdl.handle.net/10204/12555 |
en_ZA |
dc.identifier.chicagocitation |
Maodzeka, DK, EO Olakanmi, M Mosalagae, D Hagedorn-Hansen, and Sisa L Pityana "Hybrid optimisation studies on the microstructural properties and wear resistance of maraging steel 1.2709 parts produced." <i>Laser Powder Bed Fusion</i> (2022) http://hdl.handle.net/10204/12555 |
en_ZA |
dc.identifier.vancouvercitation |
Maodzeka D, Olakanmi E, Mosalagae M, Hagedorn-Hansen D, Pityana SL. Hybrid optimisation studies on the microstructural properties and wear resistance of maraging steel 1.2709 parts produced. Laser Powder Bed Fusion. 2022; http://hdl.handle.net/10204/12555. |
en_ZA |
dc.identifier.ris |
TY - Article
AU - Maodzeka, DK
AU - Olakanmi, EO
AU - Mosalagae, M
AU - Hagedorn-Hansen, D
AU - Pityana, Sisa L
AB - Improper selection of laser powder bed fusion (LPBF) process parameters tends to result in poor quality parts which imposes limitations with respect to the mechanical performance due to process induced defects. To address this LPBF processing challenge, this study employs a hybrid optimisation technique which combines artificial neural network (ANN) and response surface methodology (RSM) models. The models were employed for predicting the microstructural properties (porosity, microhardness and amount of martensite phase composition) and mechanical characteristic (wear resistance) of LPBF manufactured maraging steel 1.2709 parts as a function of a combination of process parameters (scan speed, laser power and hatch spacing). Both ANN and RSM models had a high tracking ability. However, ANN showed better prediction accuracy than RSM. The most desirable optimum LPBF processing parameters for minimum wear volume and porosity while maintaining maximum microhardness and martensite phase composition were found at volumetric energy density (VED) of 77 J/mm 3 (laser power = 165 W, scan speed = 784 mm/s and hatch spacing = 91 µm). Optimum quality properties predicted by the RSM and ANN models were consistent with confirmatory experiment results.
DA - 2022-08
DB - ResearchSpace
DO - 10.2139/ssrn.4192936
DP - CSIR
J1 - Laser Powder Bed Fusion
KW - Artificial Neural Network
KW - ANN
KW - Laser powder bed fusion
KW - LPBF
KW - Maraging steel 1.2709
KW - Response surface methodology
KW - RSM
KW - Wear resistance
LK - https://researchspace.csir.co.za
PY - 2022
T1 - Hybrid optimisation studies on the microstructural properties and wear resistance of maraging steel 1.2709 parts produced
TI - Hybrid optimisation studies on the microstructural properties and wear resistance of maraging steel 1.2709 parts produced
UR - http://hdl.handle.net/10204/12555
ER -
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en_ZA |
dc.identifier.worklist |
26202 |
en_US |