ELECTROMOTIVE FORCE AND TOOL WEAR IN DRY TURNING

Introduction

The major goal of manufacturing systems is to integrate the machine tools having the intelligence to look after themselves and their peripheral devices. Due to various technological problems it is becoming difficult to make the metal cutting process fully automated, tool wear being one of them. Various techniques proposed for monitoring the tool wear are generally classified into two broad categories: direct and indirect methods [1 - 4]. In direct methods the actual wear on the tool is measured while in indirect methods, a suitable parameter is selected to provide the information about the tool state. The present work is dedicated to on-line monitoring of tool wear by means of EMF in dry turning.

Experimentation

Workpiece of steel 20 of 400 mm length was turned by a 10 percent cobalt tool. The tool with electric isolation and tool without electric isolation were used. The cutting conditions such as cutting speed, feed and depth of cut were constant with each experiment.

Figure 1 shows the comparison of the experimental potentials EFM of tool with electric isolation and tool without electric isolation. Each EMF diagram was divided on several sectors (1, 2, 3, 4, 1*, 2*, 5*, 8*). Each sector had statistical investigation. It is possible to see that tool life with electric isolation more than without isolation. Moreover, the type functions of EMF are different. Fig.2 and Fig.3 shows statistical investigations of those signals. Statistical investigation shows that use only traditional statistical characteristics for monitoring of tool wear is not enough. Thus, the correlation and spectral analysis had been used.

For calculation of the autocorrelation function was used equation:

where N – the time interval corresponding to duration of the site realization EMF;

Um - mean of EMF potential;

U_i - current value of EMF potential;

U_i+t - value of EMF potential in moment (i+t);

For calculation of the spectral density was used equation:

where - mean frequency of EMF potential;

- mean amplitude of EMF potential.

Fig.1. The EMF potential by metal cutting:

a) - the tool without electric isolation, b) - the tool with electric isolation

1) tool life with electric isolation more than without isolation;

2) traditional statistical characteristics do not display in the full measure of the tool wear;

3) autocorrelation function and spectral density are more successful characteristics of the tool wear;

4) as the tool wear increases the spectral density increases too.

Fig.2. Statistical characteristics of the EMF by cutting with tool without electric isolation:

a) autocorrelation function; b) - spectral function

Fig.3. Statistical characteristics of the EMF by cutting with tool with electric isolation:

a) autocorrelation function; b) - spectral function