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This study aims to detect the temperature distribution in the cutting zone during the machining process. Furthermore, temperature influence in the cutting zone on the forms of chip shapes during the turning of Steel 30CrNiMo8 was evaluated. It is very important to use optimal machining parameters to get the best production results or for high control of the machining process.

Temperature distribution in the cutting zone during the machining process could affect the forms of chip shapes. Forms of chip shapes could be considered as the most important indicator for the quality of the machining process.

Therefore, in this study, the forms of chip shapes based on the temperature distribution in the cutting zone were examined.

It was found that the snarled chip type and the loose chip type have the highest temperature variation during the machining process.

The main purpose of the study was to analyze the relationship between cutting forces and tool wear during turning of steel 30CrNiMo8.

It is very important to find the optimal machining conditions to increase the tool life and to improve product quality. Width of tool wear was measured by universal microscope.

During experimental procedure, one chip shape was obtained for the given machining parameters. Results showed negligible tool wear for the given experimental conditions. In other words, the tool wear is negligible for one chip shape.

To increase tool wear, there are different chip shapes.

The main aim of the study was to measure the cutting forces and chip shapes based on different machining parameters.

To get the best optimal machining conditions, it is essential to use the best combination of machining parameters. Although some machining parameters are not important for the process, there are machining parameters which are very important for the machining process.

It is essential to determine which machining parameters are the most dominant to make the optimal machining conditions.

Six different chip shapes are obtained according to ISO standardization. It was determined that the different cutting forces occurred for the different chip shapes.

Acoustic emission (AE) could be used for prevention and detection of tool errors in Computer Numerical Control (CNC) machining. The purpose of this study is to analyze the AE form of CNC machining operations.

Experimental measurements were performed with three sensors on the CNC lathe to collect the data of the CNC machining. Adaptive neuro-fuzzy inference system (ANFIS) was applied for the fusion from the sensors’ signals to determine the strength of the signal periodic component among the sensors.

There were three inputs, namely, spindle speed, feed rate and depth of cut. ANFIS was also used to determine the inputs’ influence on the prediction of strength of the signal periodic component. Variable selection process was used to select the most dominant factors which affect the prediction of strength of the signal periodic component.

Results were shown that the spindle speed has the most dominant effect on the strength of the signal periodic component.