Correlation Analysis between Tool Wear, Roughness and Cutting Vibration in Turning of Hardened Steel
Hard machining is a process that has become highly recommended for replacing grinding in the manufacturing industry . This is due to its ability to machine complex shapes with reduced production costs by reducing the machining time and being an ecological process. Three technological parameters determine the quality and productivity generated from this process: cutting vibration, surface roughness and tool wear. Therefore, the analysis of the correlation between them is very important.
In the present investigation, the analysis of the correlation between cutting vibration, surface roughness and tool wear during a dry machining of hardened steel with a mixed ceramic tool is conducted in order to control these parameters online. This analysis is validated by developing predictive mathematical models.
To neutralize the effect of cutting parameters, a combination of parameters such as cutting speed, feed rate and depth of cut to be used in the experimental tests is selected from the literature based on a quality-productivity optimum performance. In the early stage, the effect of machining time on the three technological parameters is studied, then assessed by developing predictive mathematical models. In the second stage, an experimental and statistical analyses such as the Pearson and Spearman correlation methods are employed to determine correlations between tool wear, surface roughness and cutting vibration. Each parameter is compared with the other two. . The models and their validations are developed using the Minitab 16 tool, and the predictions are obtained with desirable deviations.
The examination of the outcomes from the first stage reveals that the machining time has a significant effect on the three parameters. The regression models are found to be satisfactory in predicting each technological parameter. In the second stage, the results show a strong correlation between tool wear and cutting vibration, confirmed by the high Pearson and Spearman coefficients. The correlations between surface roughness and tool wear or the cutting vibration are strong only when the flank wear Vb is inferior 0.3 mm (which is required by the ISO standard). The regression models are developed with a desirable coefficient of regression (R2). The novelty of this work lies in the fact that we consider the cutting vibration as a response generated the during cutting process and not as a variable affecting the other technological parameters. This was rarely studied in previous researches.
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