Borislav Savković

Application of Neuro-Fuzzy Systems for Modeling Surface Roughness Parameters for Difficult-to-Cut-Steel

The objective of this study is to examine the influence of machining parameters on surface finish in turning difficult-to-cut-steel. A new approach in modeling surface roughness which uses design of experiments is described in this paper. The values of surface roughness predicted by different models are then compared. Adaptive-neuro-fuzzy-inference system (ANFIS) was used. The results showed that the proposed system can significantly increase the accuracy of the product profile when compared to the conventional approaches. The results indicate that the design of experiments with central composition plan modeling technique can be effectively used for the prediction of the surface roughness for difficult-to-cut-steel.

An Analytical Study of Energy Partition in Grinding

This paper indicates the occurrence of high thermal energy in the surface layer of workpiece material during the process of grinding. To help identify the share of heat which affects the workpiece, firstly the analysis of past research in the field of heat transfer during grinding was conducted. Further there is a proposition of analytical expression for heat distribution factor to the workpiece in the grinding process. Goal of this analytical dependence is to achieve more efficient production of mechanical parts without thermal defects in the surface layer of the part. Presented equation for energy partition has a characteristic of widespread practical use.

RSM and Neural Network Modeling of Surface Roughness During Turning Hard Steel

In the paper examined was the influence of the cutting regime parameters on surface roughness parameters during turning of hard steel with cubic boron nitrite cutting insert. In this study for modeling of surface finish parameters was used central compositional design of experiment and artificial neural network. The values of surface roughness parameters Ra and Rt were predicted by this two-modeling methodology and determined models were then compared. The results showed that the proposed systems can significantly increase the accuracy of the product profile when compared to the conventional approaches. The results indicate that the design of experiments with central composition plan modeling technique and artificial neural network can be effectively used for the prediction of the surface roughness for hard steel and determined significand cutting regime parameters.

THE ECONOMIC SUSTAINABILITY OF A MODEL THAT BALANCES ECOLOGICAL AND POWER SUPPLY NEEDS

SNEZANA KIRIN1, PAVEL KOVAC2, BORISLAV SAVKOVIC2, SLADJANA MIRJANIC3, LUBOMIR SOOS4 1University of Belgrade, Innovation Center Faculty of Mechanical Engineering, Belgrade, Serbia 2University of Novi Sad, Faculty of Technical Sciences, Novi Sad, Serbia 3University of Banja Luka, Faculty of Science, Banja Luka, Bosnia and Herzegovina 4Slovak University of Technology in Bratislava, Faculty of Mechanical Engineering, Bratislava, Slovak Republic

ADAPTIVE NEURO-FUZZY MODELING OF THERMAL VOLTAGE PARAMETERS FOR TOOL LIFE ASSESSMENT IN FACE MILLING

The focus of this paper is to develop a reliable procedure to predict tool life during face milling process. This procedure involves a combination of Method of Least Squares and Neuro Fuzzy system. The factorial designs combined with the ANFIS techniques were applied to perform the prediction of thermal voltage. A least-squares linear regression is applied to perform the prediction of tool life from thermal-voltage signals. In this contribution we also discussed the construction of an ANFIS system that tends to provide a linguistic model for the estimation of thermal voltage obtained with different membership functions. This research focuses on developing ANFIS models using triangular and Gaussian membership functions. The work shows that the membership functions have the dominant effect among the on the accuracy model. The results indicate that the training of ANFIS with the Gaussian membership function obtains a higher accuracy rate in the prediction of thermal voltage, respectively tool life.

MODEL APPROCH OF AUTOMATION OF BOTTLING AND PACKAGING FOR INDUSTRIAL PROCESS OF BOTTLES

Industry automation becomes the global trend in manufacturing, packaging process and is one of the most uses in industry; more and more companies are switching to automation. This reasearch is devoted to the use of automatic control system in process machine system; the control system will play a major role in control on all parts of this reasearch. This study report is about design and fabricate an automated packaging machine system The process of bottling and packaging of bottles is only partial part of automate process in one industrial process. Maked was a model using PetriNets for monitoring the process of bottling and packaging and we simulate a model for automation the step of bottling and packaging in the industrial process.

Cutting Force during Grinding Determined by Regression Analysis and Genetic Algorithms

This paper presents an investigation of possibilities of using regression analysis and genetic algorithms in modelling the cutting force values in cylindrical grinding. The process included measurement of cutting forces during cylindrical grinding and later calculating their values using abovementioned techniques. It was concluded that both techniques can be used for cutting forces modelling with genetic algorithms having a slight advantage.