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Numerous studies have shown that the choice of measurement strategy (number and position of measurement points) when measuring form error on a coordinate-measuring machine (CMM) depends on the characteristics of the machining process which was used to machine the examined surface. The accuracy of form error assessment is the primary goal of verification procedures and accuracy is considered perfect only in the case of the ideal verification operator. Since the ideal verification operator in the “point-by-point” measuring mode is almost never used in practice, the aim of this study was to examine a relationship which had not been examined in earlier studies, namely how the machining process, surface roughness and a reduced number of points in the measurement strategy affect the accuracy of flatness error assessment. The research included four most common cutting processes applied to flat surfaces divided into nine different classes of roughness. In order to determine functional dependency between the observed input variables and the output, statistical regression models and neuro-fuzzy logic (artificial intelligence tool) were used. The analyses confirmed the significance of all three input parameters, with surface roughness being the most significant one. Both the statistical regression models and neuro-fuzzy models proved to be adequate, matching the experimental results. The use of these models makes it possible to determine flatness error measured on a CMM if input variables considered in the paper are known.

Quality and productivity are two most important performances of electrical discharge machining (EDM). This paper presents the application of a fuzzy inference system (FIS) for prediction of machining quality in the EDM process. Specifically, the FIS conducted modeling of geometrical accuracy and surface finish of EDM machined parts. With the fuzzy inference system model, the input variables are discharge current and pulse duration, while the output parameters are gap distance between the electrodes and surface roughness of the workpiece. The performance of the proposed FIS provides a more effective selection of the EDM input values, which leads to better machining conditions and quality of the final product. The fuzzy inference system based modeling of the EDM process showed a very good agreement compared to the experimental data.

This study presents the development of a measurement acquisition system for force measurement and temperature cutting using virtual instrumentation. The developed measurement chain enables very efficient, accurate, reliable and fast data collection during the measurement process. The presented acquisition system was developed for the face milling process, but its application is also intended for turning or grinding processes. Emphasis is placed on the creation and description of a software solution in order to manage hardware measuring sensors. Two versions of the virtual instrument are presented. The first one is based on the measurement of three components of cutting forces, obtained by means of a piezoelectric dynamometer. And the second one is upgraded with another channel that represents the temperature change record obtained by the thermocouple. At the end, the experimental results for face milling are shown. A graphical diagram obtained based on some of the data generated by the innovative measuring chain applied to the milling process is presented. [ABSTRACT FROM AUTHOR]

In today's market-driven manufacturing industry, achieving high productivity, cost efficiency and flexibility without compromising product quality is of paramount importance. Conventional machining methods commonly used for material processing or component manufacturing are reaching their limits in terms of time efficiency and production costs. Therefore, the need for unconventional processes is becoming increasingly apparent, with laser processing emerging as a key alternative. However, there are still problems with the effective implementation and optimization of laser processing that stand in the way of widespread application. By using concentrated light energy, this technique ensures exceptional precision and quality, which depends on the right machine configuration and the use of optimal machining parameters. In this study, we applied the Taguchi method to carefully optimize the parameters of laser cutting to achieve higher processing quality. [ABSTRACT FROM AUTHOR]

To further improve the efficiency of electrical discharge machining of advanced materials, the possible technological improvement of the process is achieved by adding graphite powder to the dielectric. In this study, the Taguchi approach was applied to determine the effects of input parameters such as discharge current, pulse duration, duty cycle, and graphite powder concentration on surface roughness in machining titanium alloys. L9 orthogonal array, signal-to-noise ratio (S/N), and ANOVA were used to design and analyze the experiment. Discharge current was determined to be the factor that had the strongest influence on surface roughness. Based on ANOVA, pulse duration was the second influential parameter, followed by graphite powder and duty cycle. In addition, an optimum condition was found to improve surface roughness. A discharge current of 1.5 A, a pulse duration of 32 µs, a duty cycle of 30% and a powder concentration of 12 g/l resulted in minimum surface roughness. [ABSTRACT FROM AUTHOR]

This study deals with fuzzy logic based modeling and parametric analysis in powder mixed electrical discharge machining of titanium alloys. The central composition plan was used to design the experiments considering four parameters, namely discharge current, pulse duration, duty cycle as well as graphite powder concentration. All experiments were performed with different parameter combinations and the performance, i.e., surface roughness, was evaluated. The adaptive neuro-fuzzy inference system was used to understand and define the inputoutput relationship. The experimental results and the model results were compared and it was found that the results accurately predicted the reactions in the erosion of titanium alloys. In addition, the model was verified using data that had not participated in the training of the model, with an error of about 10 %. In addition, a fuzzy plot was used to analyze the influence of input parameters on surface roughness. It was found that the discharge current was the most important influencing parameter. Additional experiments proved the positive effect of graphite powder, which reduced the surface roughness by 27 %. [ABSTRACT FROM AUTHOR]

The development of mathematical models by using experimental data is of great importance for modelling and optimization of the laser cutting process. Motivated by the lack of research regarding the use of genetic programming (GP) for deriving empirical mathematical models that describe the laser cutting process, the present study discusses the application of GP to the development of a kerf taper angle mathematical model. The aim was to quantify the relationship between three selected input parameters (cutting speed, laser power and assist gas pressure) and kerf taper angle using GP in the CO2 laser cutting of aluminium alloy AlMg3. To obtain the experimental database for the GP model evolution process, a laser cutting experiment was planned as per standard full factorial design where all three selected parameters were varied at three levels. The fit between the experimental and the GP model prediction values of kerf taper angle was found to be appropriate. Finally, by using the derived GP mathematical model, the analysis of the effects of input parameters on the change in kerf taper angle values was performed by generating 3D surface plots. [ABSTRACT FROM AUTHOR]

In this study, cutting tools average temperature was investigated by using thermal imaging camera of FLIR E50-type. The cubic boron nitride inserts with zero and negative rake angles were taken as cutting tools and round bar of EN 90MnCrV8 hardened steel was used as the workpiece. Since the life of the cutting tool material strongly depends upon cutting temperature, it is important to predict heat generation in the tool with intelligent techniques. This paper proposes a method for the identification of cutting parameters using neural network. The model for determining the cutting temperature of hard steel, was trained and tested by using the experimental data. The test results showed that the proposed neural network model can be used successfully for machinability data selection. The effect on the cutting temperature of machining parameters and their interactions in machining were analyzed in detail and presented in this study. [ABSTRACT FROM AUTHOR]

This paper reports the development of two intelligent models for the electric discharge machining (EDM) process using adaptive-neuro-fuzzy-inference system (ANFIS) and genetic programming (GP). Experiments were conducted by varying the pulse duration and discharge current and the corresponding values of surface roughness (Ra) were measured. The values of surface roughness predicted by these models are then compared. Both models show good agreement with experimental results. The results indicate that the genetic programming technique gives slightly smaller deviation of the measured values of model than neuro-fuzzy model. [ABSTRACT FROM AUTHOR]