This paper presents the influence of the nitrogen fluid phase on the surface heat transfer coefficient in cryogenic machining. A novel optical nitrogen phase sensor was developed for characterizing the cryogenic fluid phase. Surface heat transfer coefficients were established experimentally by using a new heat transfer model for cryogenic machining. A finite element model was developed utilizing experimental data for Inconel 718. Using it, the process behavior with varying nitrogen phases was simulated. Determining the minimal, but sufficient amount of coolant flow-rate, in combination with the desired fluid phase at the delivery, was found to be the key for achieving truly sustainable cryogenic machining.
COBISS.SI-ID: 14521883
In this paper, the tool deflection is first analyzedanalytically, based on a four-section cantilever beam representationof the tool. Contributions of different sections of the toolto total tool displacement are quantified. Then the influence ofcutting parameters (width of cut, depth of cut, and feed pertooth) on tool deflection are investigated experimentally usingprecise laser-based measuring technique. Empirical modelingof tool deflection curves is based on a mixed full factorial(2132) design of experiments to find a functional interrelationbetween the tool deflection close to the tip and the cuttingparameters. The results show that response surface model oftool deflection can be used to characterize the process behavioracross a useful range of cutting conditions. Analytical andempirical model predictions are compared with experimentalresults and a good agreement is observed.
COBISS.SI-ID: 13287195
The tool wear of cutting tools has a very strong impact on the product quality as well as on the efficiency of the machining processes. Despite the current high automation level in the machining industry, a few key issues prevent complete automation of the entire turning process. One of these issues is tool wear, which is usually measured off the machine tool. Therefore, its in-line characterization is crucial. This paper presents an innovative, robust and reliable direct measurment procedure for measuring spatial cutting tool wear in-line, using a laser profile sensor. This technique allows for the determination of 3D wear profiles, which is an advantage over the currently used 2D subjective techniques (microscopes, etc.). The use of the proposed measurement system removes the need for manual inspection and minimizes the time used for wear measurement. In this paper, the system is experimentally tested on a case study, with further in-depth analyses of spatial cutting tool wear performed. In addition to tool wear measurements, tool wear modelling and tool life characterization are also performed. Based on this, a new tool life criterion is proposed, which includes the spatial characteristics of the measured tool wear. The results of this work show that novel tool wear and tool life diagnostics yield an objective and robust methodology allowing tool wear progression to be tracked, without interruptions in the machining process or in the performance of the machining process. This work shows that such an automation of tool wear diagnostics, on a machine tool, can positively influence the productivity and quality of the machining process.
COBISS.SI-ID: 14181403
Slovenia is lagging behind most developed economies in innovation and economic results. In order to alleviate this problem, we have developed a model for the sustainable promotion of creativity, innovation and entrepreneurship among primary school students. In this article, we present three forms of the proposed educational activities. Each of the three sets of activities has work guidelines founded on our concept. We believe that the introduction of these guidelines will help improve the creative, innovative and entrepreneurial competencies of young people. Although the proposal does not envision immediate financial effects, it represents an important and socially responsible long-term investment.
COBISS.SI-ID: 21982950
The surface roughness of the machined parts is one of the most important factors that have considerable influence on the quality and functional properties of products. The objective of this study is development of a surface roughness prediction model for machining Inconel 718 in high-pressure jet assisted turning using the fuzzy expert system, where the fuzzy system is optimized using two bioinspired algorithms: genetic algorithm and particle swarm optimization. The effect of various influential machining parameters, such as diameter of the nozzle, pressure of the jet, cutting speed, feed rate, and distance between the impact point of the jet and cutting edge were taken into consideration in this study. The predicted surface roughness values obtained from developed fuzzy expert systems were compared with the experimental data, and the results indicate that proposed systems can be effectively used to estimate the surface roughness in high-pressure jet assisted turning.
COBISS.SI-ID: 14081307