Diagnosis of machining outcomes based on machine learning with Logical Analysis of Data

DOIResolve DOI: http://doi.org/10.1109/IEOM.2015.7093752
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TypeArticle
Proceedings titleIEOM 2015 - 5th International Conference on Industrial Engineering and Operations Management, Proceeding
Conference5th International Conference on Industrial Engineering and Operations Management, IEOM 2015, 3 March 2015 through 5 March 2015
ISBN9781479960651
Article number7093752
SubjectArtificial intelligence; Carbon; Carbon fiber reinforced plastics; Combinatorial optimization; Failure analysis; Fault detection; Fiber reinforced plastics; Information analysis; Machining; Pattern recognition; Carbon fiber reinforced polymer; CFRP plates; Force and torques; Geometric profile; Logical analysis of data; Machining conditions; Machining Process; Nonconforming products; Quality control
AbstractForce is considered to be one of the indicators that best describe the machining process. Measured force can be used to evaluate the quality and geometric profile of the machined part. In this paper, a combinatorial optimization approach is used to characterize the effect of force on the quality of a machined part made of Carbon Fiber Reinforced Polymers (CFRP) material. The approach is called Logical Analysis of Data (LAD) and is based on machine learning and pattern recognition. LAD is used in order to map the machining conditions, in terms of force and torque that lead to conforming products and those which lead to nonconforming products. In this paper, the LAD technique is applied to the drilling of CFRP plates, and the results, based on data obtained experimentally, are reported. A discussion of the potential use of LAD in manufacturing concludes the paper.
Publication date
PublisherIEEE
LanguageEnglish
AffiliationNational Research Council Canada (NRC-CNRC); Aerospace
Peer reviewedYes
NPARC number21275749
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Record identifier66db3eb9-9b9d-4026-b2a9-0f1aa63334aa
Record created2015-07-14
Record modified2016-05-09
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