Real Time Monitoring of Surface Roughness by Acoustic Emissions in CNC Turning

Abstract

Machining is the most important part of the manufacturing processes. Machining deals with the process of removing material from a work piece in the form of chips. Machining is necessary where tight tolerances on dimensions and finishes are required. The common feature is the use of a cutting tool to form a chip that is removed from the work part, called Swarf. Every tool is subjected to wear in machining. The wear of the tool is gradual and reaches certain limit of life which is identified when the tool no longer produce the parts to required quality. There are various types of wear a single point cutting tool may be subjected to in turning. Of these, flank wear on the tool significantly affects surface roughness. The other types of tool wears are generally avoided by proper selection of tool material and cutting conditions. On-line surface roughness measurements gained significant importance in manufacturing systems to provide accurate machining. The Acoustic Emission (AE) analysis is one of the most promising techniques for on-line surface roughness monitoring. The AE signals are very sensitive to changes in cutting process conditions. The gradual flank wear of the tool in turning causes changes in AE signal parameters. In the present work investigations are carried for turning operation on mild steel material using HSS tool. The AE signals are measured by highly sensitive piezoelectric element; the on-line signals are suitably amplified using a high gain pre-amplifier. The amplified signals then recorded on to a computer and then analyzed using MAT LAB. A program is developed to measure AE signal parameters like Ring down count (RDC), Signal Rise Time and RMS voltage. The surface roughness is measured by roller ended linear variable probe, fitted and moved along with tool post on a CNC lathe machine. The linear movements of probe are converted in the form of continuous signals and are displayed on-line in the computer. The results thus plotted show a significant relation between Surface Roughness and AE signal parameters. The conclusions are made for predicting surface roughness by suggesting consistent values and ranges for on-line monitoring AE signal parameters.

Stick-Slip Phenomena and Acoustic Emission in the Hertzian Linear Contact

“…The active monitoring and diagnosis of various machine components, such as bearings, gears, pumps, and motors, are assessed by AE evaluation over time [17,18]. In addition, the generation of AE at different pressures and sliding speeds has been evaluated by basic methods for rough/finish turning [19,20], detection of the breakdown of a machine tool device [21,22], or in the case of disc brake friction couple components [23].…”

Predicting Surface Roughness in Turning Complex-Structured Workpieces Using Vibration-Signal-Based Gaussian Process Regression

“…Researchers have also refined input variables to enhance prediction accuracy. For example, in 2010, T. Reddy and C. Reddy [4] correlated acoustic emission (AE) with surface roughness, Marani et al [5] evaluated feed rate and cutting speed in adaptive neuro-fuzzy inference systems (ANFIS), and Lin et al [6] demonstrated that ANNs could improve prediction accuracy by incorporating vibration signals. Vasanth et al [7] fused cutting force, tool wear, displacement of tool vibration, and three cutting parameters to predict the roughness in ANN.…”

Tool wear monitoring using an online, automatic and low cost system based on local texture

“…Other works are based on vibrations such as the one developed by Painuli et al [7] in which descriptive statistical features from vibration signals are used in an online cutting tool condition monitoring system, or the work by Wafaa et al [8] in which the vibratory signatures produced during a turning process were measured by using a three-axis accelerometer to monitor the wear on the tool. Acoustic emissions have been also shown to be sensitive to changes in cutting process conditions [9]. However, indirect methods present an important drawback: all these signals can be seriously affected by the inherent noise in industrial environments, which reduces their performance.…”