Emre Akarslan

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.

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.

An application of MDLPF models for solar radiation forecasting

Electricity generation from renewable resources is a hot topic due to increasing environmental awareness of people and energy needs. Solar energy is one of the most important clean energy sources. Forecasting of solar radiation is one of the important stages in sizing and managing a PV power plant. Moreover since the smart grid applications are started, accurate forecasting of the energy output of PV system (hence the solar radiation) became a hot topic. In literature there are a huge number of studies tries to find more accurate forecasting models. Among them in this study Multi-Dimensional Linear Prediction Filter Models (MDLPF Models) are studied. To test the performance of MDLPF models, hourly solar radiation data of two different regions (Ankara and Çanakkale) are employed. In forecasting five different MDLPF Models are built. The accuracies of the forecasting results are compared and discussed.

Deformation classification of cutting discs using artificial neural networks

In this study, a classification application is realized to determine the deformation status of the cutting disc. 673 cutting experiments data obtained from a marble cutting machine suited in a laboratuvary of the Afyon Kocatepe University are evaluated for this purpose. During the cutting process, 8 different signals (Axial forces (Fx, Fy, Fz), Noise, Peripheral speed of the disc, Current, Voltage and Power) are measured and collected. The mean values of the each experiments are used as a 8 length feature vector. To determine the deformation class of the disc (undamaged, less damaged, very damaged and broken) these feature vectors are used. On the other hand Artificial Neural Networks (ANNs) are employed as classifiers. It is obtained that proposed method is able to classify the deformation status of the disc with 95,86 % accuracy.

An Experimental Setup Design to Evaluate Power Generation Performances of TECs under Different Temperatures

Thermoelectric Cooler (TEC) is a semiconductor based device that has ability to separate cold and hot temperatures once the rated voltage is applied. In this study, TECs are used as Thermoelectric Generator (TEG). For this aim an experimental setup is built. By the help of this experimental setup electricity generation performances of the TEC is tested under various temperature conditions. The setup includes two water tanks, loads, TEC modules, computer interface and a data acquisition system. Temperature difference required for electrical generation of the TEC module is provided by filling the tanks with water at different temperatures. A data acquisition system is designed for this specific setup. First the setup with data acquisition system is introduced then experimental results are presented and discussed. Keywords:Electrical energy generation, Thermoelectric Cooler, LabVIEW