Recurrence quantification analysis to estimating surface roughness in finish turning processes. Issue 1 (October 2016)

• Record Type: Journal Article
• Authors: Kamarthi, Sagar; Sultornsanee, Sivarit; Zeid, Abe
• Is Part Of: International journal of advanced manufacturing technology. Volume 87:Issue 1/4(2016)
• Page Start: 451
• Page End: 460
• Publication Date: 2016-10
• Subjects: Finish turning -- Acoustic emission -- Surface roughness estimation -- Recurrence quantification analysis -- Neural networks; Manufacturing processes -- Periodicals; Production engineering -- Periodicals; 670.427
• DOI: 10.1007/s00170-016-8516-5
• Languages: English
• ISSNs: 0268-3768

Abstract

Abstract Surface roughness is one of the important factors in all areas of tribology and in evaluation of the quality of machining operations such as turning, milling, or grinding. This paper describes a method for in-process estimation of workpiece surface roughness in a turning process from acoustic emission signals generated by the sliding friction between a graphite probe and the workpiece. Acoustic emission signals are first transformed into recurrence plots and from these plots computed are a set of recurrence statistics using the recurrence quantification analysis. The surface roughness parameters are estimated using an artificial neural network, taking the recurrence statistics of the acoustic emission signals as inputs. This method is verified by conducting an extensive set of experiments on AISI 1054 steel workpiece and K420 grade uncoated carbide inserts. Three surface roughness parameters, namely arithmetic mean (R a ), maximum peak-to-valley roughness (R max ), and mean roughness depth (R z ) are estimated. The estimation accuracy of the proposed method is in the range of 90.13 to 91.26 %. The merit of the proposed method lies in its ability to circumvent the limitations of the existing optical and induction-based techniques which are hampered by the rotation of the workpiece, presence of coolants, and interference of chips. Furthermore, these accurate results indicate that the proposed surface roughness estimation method is very effective and amenable for practical implementation.