Pavel Kovač

Application of fuzzy logic and regression analysis for modeling surface roughness in face milliing

The objective of this study is to examine the influence of machining parameters on surface finish in face milling. A new approach in modeling surface roughness which uses artificial intelligence tools is described in this paper. This paper focuses on developing empirical models using fuzzy logic and regression analysis. The values of surface roughness predicted by these models are then compared. The results showed that the proposed system can significantly increase the accuracy of the product profile when compared to the conventional approaches, like regression analysis. The results indicate that the fuzzy logic modeling technique can be effectively used for the prediction of surface roughness in dry machining.

Investigation of chip formation during milling

Abstract Metal cutting process is actually a process of chip formation. Most of the information, for evaluation of the cutting process, such as surface finish, cutting temperature, tool life and cutting forces are closely related to the chip geometry. This paper shows that the influence of discontinuities in microstructure plays an important role in cutting. The quick-stop technique of tool in machining, light microscope and SEM were used to study chip formation and surface integrity association with voids and crack formation and its propagation mechanism.

The influence of honing process parameters on surface quality, productivity, cutting angle and coefficients of friction

Purpose – The purpose of this paper is to explore the influence of tool kinematics parameters on surface roughness, productivity and cutting angle for grey cast iron cylinder liners machined by normal honing.Design/methodology/approach – For experimental investigation, a long stroke honing system was used. Diamond and SiC tools were used, for pre‐ and finishing, respectively. The values of cutting parameters were varied within the following limits: cutting speed vs=0.931‐1.11 m/s; specific pressure of pre‐honing process pd=1.0‐1.4 N/mm2 and specific pressure of finishing honing process pz=0.2‐0.5 N/mm2. The analysis of dispersion was conducted for determining the mathematical model for cutting parameter influence on surface roughness and productivity.Findings – Dispersion analysis proved that the most influential parameters on maximum roughness depth are cutting speed for D181 tool and specific pressure of finishing honing for D151 tool. The most influential parameter on productivity in the honing process...

The influence of tool kinematics on surface texture, productivity, power and torque of normal honing

Purpose – In this paper, the influence of cutting regimes on surface texture and productivity of cylinder liner is shown, obtained by normal honing. Furthermore, normal honing power and torque is discussed. The paper aims to discuss these issues. Design/methodology/approach – Normal honing was done on grey cast iron cylinder liners. Both pre- and finishing honing were done by mechanical means, to achieve the required level of surface texture quality, traditionally achieved by a combination of electrochemical pre-honing and mechanical finishing honing. To determine the most influential parameter on normal honing, statistical mathematical model was devised. Surface texture was controlled by light microscope and scanning electron microscope. Findings – Mathematical model describing the influence of cutting speed, feed rate and depth of cut has shown that the most influential parameter on maximum peak height, productivity and specific volume productivity was shown. The most influential parameter has shown to ...

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.

Development of a Modular Flood Barrier Concept Made from Recycled Plastic

This contribution describes developments in the area of plastic recycling aimed at the reuse of waste materials for the manufacture of a useful product. Throughout development of the concept “modular flood barrier structure”, the recovery of plastic materials was assessed, primarily, plastic waste from automobiles and industry. The investigators filed a utility model application, no. SK 5847 Y12, entitled “Modular flood barrier structure”. Terms of the type and composition of source material, polypropylene (PP) and polyethylene (PE) were the materials chosen. The article describes the theoretical analysis of the proposed barriers as a starting point for a design and technological design.

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

PAPERMAKING SLUDGE PROPERTIES FOR USE IN CONSTRUCTION AND BUILDING

1719 PAPERMAKING SLUDGE PROPERTIES FOR USE IN CONSTRUCTION AND BUILDING LUBOMIR SOOS1, MILOS MATUS1, MARCELA POKUSOVA1, PETER KRIZAN1, PAVEL KOVAC2 1Slovak University of Technology in Bratislava, Faculty of Mechanical Engineering, Institute of manufacturing systems, environmental technology and quality, Bratislava, Slovak Republic 2University of Novi Sad, Faculty of Technical Sciences, Institute for Production Engineering, Novi Sad, Serbia DOI: 10.17973/MMSJ.2017_02_2016149 e-mail : lubomir.soos@stuba.sk