Cutting tool condition monitoring by analyzing surface roughness, work piece vibration and volume of metal removed for AISI 1040 steel in boring

The vibration is one of the intensive problems in boring process. Machining and tool wear are affected more by vibration of tool due to length of boring bar. The present work is to
estimate the effect of cutting parameters on work piece vibration, roughness on machined surface and volume of metal removed in boring of steel (AISI1040). A laser Doppler vibrometer (LDV) was used for online data acquisition and a high-speed FFT analyzer used to process the AOE signals for work piece vibration. A design of experiments was prepared with
eight experiments with two levels of cutting parameters such as spindle rotational speed, feed rate and tool nose radius. Taguchi method has been used to optimize the cutting parameters and a multiple regression analysis is done to obtain the empirical relation of Tool life with roughness of machined surface, volume of metal removed and amplitude of work piece vibrations