Ismail Ucun

Effect of cooling liquids on cutting process using diamond segmented disc of natural stones

In this study, various cooling liquids considered as alternatives to water were investigated for cutting performance of diamond sawing disc utilized in cutting process of natural stones. In experiments, four various cooling liquids such as water, Ace-Cool, boron oil and liquid soap and two different natural stones (Blue Pearl and Nero Zimbabwe) were used. Down-cutting mode was selected for cutting experiments. In experiments, power consumption, cutting forces, specific energy, diamond segment wear and diamond crystal failures were determined. Power consumption of diamond sawing disc was measured through an energy analyzer and cutting forces through a three-directional dynamometer (ESIT). Specific energy values were calculated through an analytical method on the basis of the resulting power consumption and cutting forces. Besides, diamond segment wear was measured through a laser device (KEYENCE) and diamond crystal failure in cutting process was analyzed through scanning electron microscopy on the basis of cooling liquids. Results of the study show that various cooling liquids proposed as alternatives to water increase significantly cutting performance of the diamond sawing disc. While maximum cutting performance was achieved with a mixture of water and boron oil, minimum performance was achieved through use of water.

Determination of Cutting Disc Deformation Using Axial Forces Produced during the Cutting Process

The reliability of the cutting disc in a sawing process is of vital importance in industry. There exist a lot of reported accidents due to damaged disc usage. In most cases the damage on the disc is not visible. Therefore innovative techniques are required to determine the damages. For this aim an experimental setup is built in Afyon Kocatepe University. Different experiments are performed. While experiments different parameters are measured and calculated. In this paper axial forces produced while the cutting processes are studied. Each experiment is represented by a vector of three dimensional axial forces (Fx, Fy, Fz). Experiments are repeated using four different class of cutting disc (solid, less damaged, much damaged and broken). An Adaptive Neuro Fuzzy Inference System (ANFIS) structure is proposed to classify the deformations that occur on a cutting disc in sawing processes. The results indicate that proposed ANFIS structure is very effective on classification.

A Time-Frequency Analysis for Deflection of a Cutting Disc Used in Sawing Process

Cutting discs are affected by various forces which cause stresses and deflections on the discs. Lateral deflection is one of the most frequent. It is important to know that major lateral deflections can cause undesired failures on the disc. In this study, time/frequency analysis of deflection on a cutting disc used in metal sawing process has been performed. The deflections are measured by using a laser device called KEYENCE and data recorded during the cutting process. The data sequence is converted into frequency domain with Fourier transform techniques and frequency components are analyzed. It is observed that some frequency components carry important information about the process. Using this information, the starting and ending times of cutting are determined and presented.

Classification of disc damage status by discovering knowledge from experimental data in marble cutting process

In marble industry, it is of vital importance to determine the damaged discs on time to prevent possible industrial injuries. Therefore, in this study, it is proposed to classify the status of the cutting discs that are used while cutting the natural stones. To classify the deflections of the discs, 673 different experiments are performed. Cutting discs corresponding to four different damage classes (undamaged disc, less damaged disc, much damaged disc, and broken disc) are employed in the tests. Eight different parameters (cutting forces (Fx, Fy, Fz), noise, peripheral speed of the disc, current, voltage, power consumption) are measured and recorded in the experiments. For each experiment, mean values of different measured data are studied. Artificial neural networks are employed as classifiers. In the first stage, all of these mean values corresponding to eight parameters are selected as the input vectors of the artificial neural networks, whereas in the second stage, the dimension of input vector is decreased by leaving out the parameters one by one. In this stage, it is aimed to determine the most important parameter that caries much more information about the cutting process.