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The Response Surface Methodology for Optimizing the Process Parameters of Selective Laser Melting
Mathieu Terner, Thibaud Ricordel, Jae-Hung Cho, Jeong-Seok Lee
JWJ. 2019;37(1):27-39.   Published online 2019 January 18    DOI: https://doi.org/10.5781/JWJ.2019.37.1.4

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The Response Surface Methodology for Optimizing the Process Parameters of Selective Laser Melting
Journal of Welding and Joining. 2019;37(1):27-39   Crossref logo
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Response-surface methodology for optimizing citric acid fermentation by Aspergillus foetidus
Process Biochemistry. 1994;29(5):399-405   Crossref logo
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Optimizing pulsed Nd:YAG laser beam welding process parameters to attain maximum ultimate tensile strength for thin AISI316L sheet using response surface methodology and simulated annealing algorithm
Optics & Laser Technology. 2018;103:300-310   Crossref logo
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Effect of process parameters on the surface characteristics of AlSi12 samples made via Selective Laser Melting
Materials Today: Proceedings. 2017;4(8):8724-8730   Crossref logo
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Effect of Process Parameters on the Generated Surface Roughness of Down-Facing Surfaces in Selective Laser Melting
Applied Sciences. 2019;9(6):1256   Crossref logo
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Effect of Selective Laser Melting Process Parameters on the Quality of Al Alloy Parts: Powder Characterization, Density, Surface Roughness, and Dimensional Accuracy
Materials. 2018;11(12):2343   Crossref logo
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Influence of Process Parameters on Surface Temperature during Laser Assisted Preheating of SKD 11 Steel based on Response Surface Methodology
Materials Today: Proceedings. 2018;5(5):13451-13458   Crossref logo
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Surface roughness optimisation for selective laser melting (SLM)
Laser Additive Manufacturing. 2017;99-118   Crossref logo
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Optimization of process parameters during laser cutting of Ni-based superalloy thin sheet using response surface methodology
Materials Today: Proceedings. 2018;5(11):24231-24239   Crossref logo
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Optimizing nano TiO2 assisted decoloration process for industrial date syrup utilizing response surface methodology
Journal of Food Process Engineering. 2017;40(5):e12537   Crossref logo
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