2026-04-23T15:44:56Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/57142024-03-05T13:58:04Zcom_20.500.11984_473col_20.500.11984_478

00925njm 22002777a 4500 dc Soler Mallol, Daniel author Telleria, Martin author CUESTA ZABALAJAUREGUI, MIKEL author ARRAZOLA, PEDRO JOSE author 2022 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%. 2504-4494 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=168250 https://hdl.handle.net/20.500.11984/5714 surface roughness additive manufacturing SLM Artificial Neural Networks blasting electropolishing Prediction of Surface Roughness of SLM Built Parts after Finishing Processes Using an Artificial Neural Network