Surface roughness analysis in finishing end-milling of duplex stainless steel UNS S32205
Duplex stainless steel (DSS) UNS S32205 presents high resistance to oxidation, corrosion, and corrosion under stress associated with good mechanical properties, thus being widely used in oil exploration and chemical treatment systems. Due to these characteristics, studies on surface quality are just...
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Veröffentlicht in: | International journal of advanced manufacturing technology 2018-09, Vol.98 (5-8), p.1617-1625 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Duplex stainless steel (DSS) UNS S32205 presents high resistance to oxidation, corrosion, and corrosion under stress associated with good mechanical properties, thus being widely used in oil exploration and chemical treatment systems. Due to these characteristics, studies on surface quality are justified in the machining of such materials. In this work, dry machining end-milling tests were performed with positive geometry cutting tools (coated carbide inserts with tip radius of 0.4 mm) with different cutting parameters. Each cutting parameter was evaluated in three levels, combined through Box-Behnken Design of experiments (BBD): cutting speed (30, 45, 60 m/min), feed per tooth (0.05, 0.1, 0.15 mm/tooth), and depth of cut (0.2, 0.4, 0.6 mm). Two surface roughness parameters (
R
a
and
R
z
) were measured in the machined surfaces under different cutting combinations. Roughness profiles were analyzed for the highest and lowest measured values of surface roughness, and Abbott-Firestone Curve (AFC) and amplitude-probability distribution (APD) represented its statistical profile. In this way, appropriate combination of cutting parameters for minimum
R
a
and
R
z
parameters obtained after finishing end-milling of UNS S32205 were determined, as well as AFC and APD of these values, thus allowing the characterization of the performance of tool-workpiece pair in DSS cutting, resulting in
R
a
and
R
z
values of 0.182 and 1.302 μm, respectively. Low surface roughness values were validated using BBD, representation of the statistical profile, and optimization of cutting parameters improved the little bibliographical material in DSS milling. |
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ISSN: | 0268-3768 1433-3015 |
DOI: | 10.1007/s00170-018-2356-4 |