Multivariate process capability analysis applied to AISI 52100 hardened steel turning

Hard turning operations have been extensively investigated owing to their ability to reduce process cycle time, increase process flexibility, ensure high-dimensional accuracy, and enable machining without a cutting fluid. These processes are rather common for dealing with multiple quality characteri...

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Bibliographic Details
Published in:International journal of advanced manufacturing technology 2018-04, Vol.95 (9-12), p.3513-3522
Main Authors: Peruchi, R. S., Rotela Junior, P., Brito, T. G., Largo, J. J. J., Balestrassi, P. P.
Format: Article
Language:English
Subjects:
Bearing steels
CAE) and Design
Capability indices
Chromium steels
Computer-Aided Engineering (CAD
Cutting fluids
Cycle time
Engineering
Estimation
Industrial and Production Engineering
Manufacturing
Mechanical Engineering
Media Management
Multivariate analysis
Original Article
Principal components analysis
Process capability analysis
Surface finishing
Turning (machining)
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Summary:Hard turning operations have been extensively investigated owing to their ability to reduce process cycle time, increase process flexibility, ensure high-dimensional accuracy, and enable machining without a cutting fluid. These processes are rather common for dealing with multiple quality characteristics. To evaluate the process ability and meet customer needs, multivariate statistical techniques are recommended for estimating the capability indices. Principal component analysis can be applied to reducing the problem dimension and estimate process capability indices. The aim of this study was to assess the capability of AISI 52100 hardened steel turning operations and achieve process specifications. Multivariate process capability indices were calculated to assess five roughness parameters of surface finishing. By using a weighted approach of principal component analysis, a new method is proposed for estimating the process capability indices. The results highlight not only the relevance of conducting a multivariate capability analysis in the case of actual machining but also how successfully the proposed method was performed.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-017-1458-8