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The paper aims to analyze chip formation during machining process since it can be a very important indicator for the quality of the machining process, as some chip forms can be undesirable.

It is essential to determine the sensitivity of the chip formation on the basis of different machining parameters. The main goal of the study was to analyze the sensitivity of the chip formation during the machining process by using adaptive neuro-fuzzy inference system (ANFIS).

According to the results, the chip formation is the most sensitive to feed rate.

Different cutting tests were performed to monitor the chip formation on the basis of the cutting forces and the cutting displacement. ANFIS was used to estimate the sensitivity of the chip formation during the cutting process on the basis of different parameters.

The purpose of the paper is to evaluate a lake lavel prediction model. Lake-level prediction is very important task for different crucial issues like planning of water resource, controlled drainage, etc. Therefore, in this study, a soft computing approach was applied to predict the lake levels based on the different prediction horizons.

The main focus was to establish a sensorless estimation of the lake level based on different prediction horizons. Support vector regression approach was implemented for the lake-level prediction, as this approach is suitable for highly nonlinear prediction problems.

Ludoš Lake in Serbia was used for analysis.

According the results, the soft computing models can be used confidently for the lake-level prediction based on the prediction accuracy.

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.

This paper aims to explore detection of heating load of building by machine learning. Detection of heating load of building is very important in design of buildings due to efficient energy consumption.

In this study, detection of heating load of building based on effects of dry-bulb temperature, dew-point temperature, radiation, diffuse radiation and wind speed was analyzed. Machine learning approach was implemented for such a purpose.

The obtained results could be useful for future planning of heating load of buildings. Because the heating load of building is a very nonlinear phenomenon, it is suitable to use machine learning approach to avoid the nonlinearity of the system.

The obtained results could be used effectively in detection of heating load of buildings.

The paper aims to present an investigation of wood bonding strength as a very important indicator for effective using in further manufacturing processes.

In this study, the wood bonding strength sensitivity was estimated based on grain orientation, feed speed, heating time and temperature, temperature and type of adhesive. Artificial intelligence methods were applied for this analysis because it is strongly a nonlinear process.

It was shown that the artificial intelligence tools could be useful, reliable and effective for the wood bonding strength sensitivity estimation. Afterwards the power consumption in in solid wood machining process is analyzed and estimated by the artificial intelligence tools.

Results shown that the wood bonding strength is the most sensitive for type of adhesive. Thus, the results of the present research can be successfully applied in the wood industry to reduce the time, energy and high experimental costs.

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.

This paper aims to show the implementation in the terrestrial trunked radio (TETRA)-based sensor network. The publicly available data show that, in Serbia, the annual damage caused by hailstorms in the past seven years has been estimated almost at an average level of 40m of euros. As the amount of hail was not changed, the hail suppression system of the Republic of Serbia has to be improved, both technically and organizationally, to get better efficiency and protection and to reduce the damage.

In this paper, the authors show the implementation and improvements in the modern terrestrial trunked radio (TETRA)-based sensor network, and they propose the scientific use of sensors for remote control of automatic hail suppression rocket stations.

The authors’ idea is that TETRA should be used as an operational and official telecommunicating system for hail suppression activities units. A number of sensors, connected in a network, are used to maintain a high-quality functioning of this digital radio system, managed remotely and controlled either by operators or automatically.

The presented study with a real example attempts to explain as to how the system functions and how it can improve hail suppression activities.

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.