An artificial neural network approach to investigate surface roughness and vibration of workpiece in boring of AISI1040 steels. Issue 5 (March 2016)

• Record Type: Journal Article
• Authors: Rao, K.; Vidhu, K.; Kumar, T.; Rao, N.; Murthy, P.; Balaji, M.
• Is Part Of: International journal of advanced manufacturing technology. Volume 83:Issue 5/8(2016)
• Page Start: 919
• Page End: 927
• Publication Date: 2016-03
• Subjects: ANN -- Surface roughness -- Tool vibration -- Acousto-optic emission -- Tool wear; Manufacturing processes -- Periodicals; Production engineering -- Periodicals; 670.427
• DOI: 10.1007/s00170-015-7621-1
• Languages: English
• ISSNs: 0268-3768

Abstract

Abstract In metal cutting, tool failure and surface roughness are the important aspects that affect product quality and production cost, and these are affected mainly by vibration of workpiece. Current techniques do not have a proper method to measure vibration of a rotating workpiece so as to use it as a parameter to replace a cutting tool at an appropriate time. The purpose of the present work is therefore to use of laser Doppler vibrometer (LDV) to measure the vibration of workpiece without interfering the machining. Subsequent to obtaining the workpiece vibration data, artificial neural network (ANN) method was adopted to predict surface roughness and root mean square (RMS) velocity of workpiece vibration. According to Taguchi design of experiments, 18 experiments were prepared with two levels of nose radius and three levels of cutting speed and feed rate. Experiments were conducted on CNC lathe to obtain data of surface roughness and RMS of workpiece vibration velocity in boring of AISI 1040. A multilayer feedforward ANN model was developed and trained with the experimental data using back propagation algorithm. Further, the ANN was used to predict surface roughness and RMS velocity of workpiece vibration. The predicted values were compared with the collected experimental data and percentage error was computed. Less percentage of error was found between the experimental and predicted values.