A comparative study of two indirect methods to monitor surface integrity of ground components

Abstract

One of the major challenges in grinding is to conciliate the material removal rate with components free from damages. Due to the poor thermal conductivity of conventional grinding wheels, most of the heat generated during grinding is transferred to the workpiece surface and subsurface, which can cause thermal damages and impair the performance in service of machined components. Hence, it is very important to monitor the grinding process to ensure the quality of the machined parts. Thus, this work presents an innovative study comparing two indirect monitoring methods in monitoring surface integrity of steel during grinding: the acoustic emission technique and the electromechanical impedance technique. Worktable speed and radial depth of cut were used as input parameters. Visual inspection and scanning electron microscopy images of ground surfaces as well as microhardness were the output variables used to assess surface integrity and to establish a relationship with the acoustic emission and electromechanical impedance techniques. Since the acoustic emission signals are non-stationary in nature, these signals were analyzed in the time–frequency domain by applying the short-time Fourier transform and the continuous wavelet transform. The root mean square deviation index was extracted as feature from the acoustic emission and electromechanical impedance signals. Results showed that both techniques presented similar results. The root mean square deviation index showed a good correlation with alterations in surface integrity under the conditions investigated.

Improvements of the MQL Cooling-Lubrication Condition by the Addition of Multilayer Graphene Platelets in Peripheral Grinding of SAE 52100 Steel

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Al₂O₃ Nanofluids: An Experimental Study for MQL Grinding

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Improved Estimation of End-Milling Parameters from Acoustic Emission Signals Using a Microphone Array Assisted by AI Modelling

“…In this context, techniques based on the sensing of acoustic emission (AE) phenomena have gained increasing interest during the last few years due to their simplicity of use and the ability to monitor a wide variety of features, such as teeth breakage [ 7 , 8 ], runout [ 9 , 10 ] and chattering [ 11 , 12 ]. In particular, those techniques based on the use of noncontact sensors in the range of audible sound, such as different types of microphones [ 13 , 14 ], are particularly attractive due to the reduced intrusiveness, so they have been used for detecting the chattering phenomenon [ 15 , 16 , 17 ] and tool condition monitoring in conventional machining [ 18 , 19 ], high-speed machining [ 20 ] and abrasion processes [ 21 ].…”