Prediction of Surface Roughness of SLM Built Parts after Finishing Processes Using an Artificial Neural Network

被引：11

作者：

Soler, Daniel ^{[1
]}

Telleria, Martin ^{[1
]}

Garcia-Blanco, M. Belen ^{[2
]}

Espinosa, Elixabete ^{[2
]}

Cuesta, Mikel ^{[1
]}

Jose Arrazola, Pedro ^{[1
]}

机构：

[1] Mondragon Unibertsitatea, Mfg Dept, Fac Engn, Arrasate Mondragon 20500, Spain

[2] Basque Res & Technol Alliance BRTA, CIDETEC, Po Miramon 196, Donostia San Sebastian 20014, Spain

来源：

JOURNAL OF MANUFACTURING AND MATERIALS PROCESSING | 2022年 / 6卷 / 04期

关键词：

surface roughness; additive manufacturing; SLM; artificial neural network; blasting; electropolishing; OPTIMIZATION; ALLOYS;

D O I：

10.3390/jmmp6040082

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

A known problem of additive manufactured parts is their poor surface quality, which influences product performance. There are different surface treatments to improve surface quality: blasting is commonly employed to improve mechanical properties and reduce surface roughness, and electropolishing to clean shot peened surfaces and improve the surface roughness. However, the final surface roughness is conditioned by multiple parameters related to these techniques. This paper presents a prediction model of surface roughness (Ra) using an Artificial Neural Network considering two parameters of the SLM manufacturing process and seven blasting and electropolishing processes. This model is proven to be in agreement with 429 experimental results. Moreover, this model is then used to find the optimal conditions to be applied during the blasting and the electropolishing in order to improve the surface roughness by roughly 60%.

引用

页数：12

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