Enhancing the Surface Quality of Additively Manufactured 316 Stainless Steel Revolving Parts through Electrochemical Polishing

Metal additive manufacturing (AM) of revolving parts has wide application across industries such as food, medical, automobile. However, high surface roughness and roundness errors on the outer surface of additively manufactured revolving components limit their performance and commercial viability. Electrochemical polishing (ECP) is a promising post-processing technology that allow achieving smooth and high-quality surfaces. However, conventional ECP relies on polluting and corrosive acid-based solutions with low current densities, limiting the efficiency of polishing. In this study, an eco-friendly NaNO 3 electrolyte is proposed to finish the outer surface of AM 316 stainless steel revolving parts with a sector-shaped tool and at high current density. A simulation model was developed to clarify the underlying polishing mechanism, incorporating critical parameters such as the tool geometry, current density, and the reconstructed 3D part profile. Simulation results provided insights into the finishing efficiency and quality, highlighting the evolution of surface roughness along the axis and roundness error around the circumference. Experimental ECP trials confirmed the simulation findings, producing a smooth, shiny surface with performance comparable to the original parts. This approach effectively reduced the average surface roughness Ra to 1.36 μm and roundness error 0.03 mm, demonstrating a homogeneous polishing outcome.

Authors

Institutions

Publication Details

Journal
Procedia CIRP
Published
2025-01-01
DOI
https://doi.org/10.1016/j.procir.2025.02.105
Citations
2
Primary Topic
Additive Manufacturing Materials and Processes
Type
article
Field-Weighted Citation Impact
1.43

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Enhancing the Surface Quality of Additively Manufactured 316 Stainless Steel Revolving Parts through Electrochemical Polishing

Andrea Ghiotti, Wenjian Cao, Stefania Bruschi
2 citations
Procedia CIRP
Additive Manufacturing Materials and Processes
1.43
article

Enhancing the Surface Quality of Additively Manufactured 316 Stainless Steel Revolving Parts through Electrochemical Polishing

Andrea Ghiotti, Wenjian Cao, Stefania Bruschi
article en
2 citations

Abstract

Metal additive manufacturing (AM) of revolving parts has wide application across industries such as food, medical, automobile. However, high surface roughness and roundness errors on the outer surface of additively manufactured revolving components limit their performance and commercial viability. Electrochemical polishing (ECP) is a promising post-processing technology that allow achieving smooth and high-quality surfaces. However, conventional ECP relies on polluting and corrosive acid-based solutions with low current densities, limiting the efficiency of polishing. In this study, an eco-friendly NaNO 3 electrolyte is proposed to finish the outer surface of AM 316 stainless steel revolving parts with a sector-shaped tool and at high current density. A simulation model was developed to clarify the underlying polishing mechanism, incorporating critical parameters such as the tool geometry, current density, and the reconstructed 3D part profile. Simulation results provided insights into the finishing efficiency and quality, highlighting the evolution of surface roughness along the axis and roundness error around the circumference. Experimental ECP trials confirmed the simulation findings, producing a smooth, shiny surface with performance comparable to the original parts. This approach effectively reduced the average surface roughness Ra to 1.36 μm and roundness error 0.03 mm, demonstrating a homogeneous polishing outcome.

Procedia CIRPVol. 133
University of Padua (IT), Henan University of Technology (CN), Nanjing University of Aeronautics and Astronautics (CN)
China Scholarship Council
Industry, innovation and infrastructure
Openalex Percentile: Top 21%
Additive Manufacturing Materials and Processes
1.43
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.