Drill Wear Research Articles

Steady and periodical heat-mass transfer behavior of mixed convection nanofluid with reduced gravity, radiation and activation energy effects

Significance of this study is to explore Arrhenius activation energy, microgravity, chemical reaction and porous medium effects on Darcy nanofluid over a stretching sheet with nonlinear thermal radiations. Purpose of this study is to enhance the lubrication efficiency, excessive heat reduction, surface roughness and tool wear in drilling, milling, grinding and cutting tools of machining procedures. Use of Brownian and thermophoretic nanoparticles in base liquid is very effective to reduce friction in tool-chip and tool-work interfaces, tool life ability and cooling ability through nanofluid minimum quantity lubrication in machining operations. A refined mathematical model is solved using dimensionless variables; oscillatory stokes conditions and primitive variables. The implicit form of finite difference method is applied for numerical results using Gaussian elimination approach. The numerical and physical outputs are secured using Gaussian elimination methodology in FORTRAN tool and TecPlot-360. Graphs are displayed by using numerical data in Tecplot-360 program. Impact of activation energy (AE), solar radiation (Rd), microgravity (RG), porosity (Ω), thermophoresis (NT), chemical reaction (Kr), Prandtl (Pr) and Schmidt factor (Sc) on oscillating frequency of heat and mass transport is depicted. Amplitude in fluid velocity, surface temperature and volume of concentration is enhanced as radiation, microgravity and activation energy increases. It is noted that the increasing amplitude in fluid velocity, temperature and fluid concentration is found with activation energy, radiation and buoyancy force effects. Steady behavior of skin friction and mass transport decreases as Darcy porous medium and reaction rate decreases. Frequency of oscillating skin friction and oscillating heat transfer increases as Schmidt and Prandtl coefficient increases. Fluctuations and amplitude in the frequency of heat and mass transport enhances as thermophoretic nanoparticle enhances.

Deep learning based drill wear segmentation and analysis of the wear progress

Deep learning based drill wear segmentation and analysis of the wear progress

Analysis of drilling response under ultra-high-speed diamond drilling: Theory and experiment

The ultra-high-speed diamond drilling (UHSDD) technology represents a groundbreaking and effective approach to deep hard rock drilling. This innovative method addresses the critical need for reduced weight on bit (WOB) while simultaneously achieving remarkable enhancements in drilling velocities. However, the absence of a comprehensive fundamental theory poses challenges, such as drill wear and inconsistent rock crushing efficiency. In this paper, the experimental data obtained from UHSDD was processed and analyzed by leveraging the drilling response model under standard rotational speeds (D-D model). The interface laws of diamond bit based on rate-relatedness were proposed. Notably, the depth of cut per revolution d of the bit is influenced by a complex interplay of rotational speed and weight on bit variables. A new variable Γ was introduced to measure the force required per revolution for d at ultra-high speeds. Within the proposed model framework, the quantitative information from drilling data was extracted, encompassing the wear state of the bit, drilling parameters, and rock properties. To validate the accuracy of the new model, a series of extensive laboratory experiments on the UHSDD test experimental platform was conducted. The reliability of the model was preliminary verified. The results of this study enhanced our understanding of the drilling response of UHSDD in practical drilling applications.

Influences of bit button wear on performance of rotary-percussive drilling: MBD-DEM coupling simulation and verification

This paper focuses on the use of rotary-percussive drilling for hard rocks. In order to improve efficiency and reduce costs, it is essential to understand how operational parameters, bit wear, and drilling performance are related. A model is presented therein that combines multibody dynamics and discrete element method (DEM) to investigate the influences of operational parameters and bit wear on the rate of penetration and wear characteristics. The model accurately captures the motion of the bit and recreates rock using the cutting sieving result. Field experimental results validate the rod dynamic behavior, rock recreating model, and coupling model in the simulation. The findings indicate that hammer pressure significantly influences the rate of penetration and wear depth of the bit, and there is an optimal range for economical hammer pressure. The wear coefficient has a major effect on the rate of penetration, when wear coefficient is between 1/3 and 2/3. Increasing the wear coefficient can reduce drill bit button pressure and wear depth at the same drill distance. Gauge button loss increases the rate of penetration due to higher pressure on the remaining buttons, which also accelerates destruction of the bit. Furthermore, a more evenly distributed button on the bit enhances the rate of penetration (ROP) when the same number of buttons is lost.

УПРОЧНЕНИЕ МЕТАЛЛОРЕЖУЩЕГО ИНСТРУМЕНТА ЭЛЕКТРОИСКРОВОЙ ОБРАБОТКОЙ

Metal-cutting tools during the machining of parts by cutting experience high strength and thermal loads, causing wear of the cutting edges. Increasing the wear resistance of the cutting edges of a metal-cutting tool is an urgent task. (Research purpose) The research purpose is analyzing the causes of wear of end mills and drills and proposing the most rational method of hardening metal-cutting tools. (Materials and methods) End mills and drills were chosen as the object of research. The BIG-5 installation with the use of electrodes made of hard alloys VK8 and T15K6 was used to harden metal-cutting tools. It was pointed out that relatively mild electrical modes with pulse energy from 0.05 to 0.2 joules are used to harden drills and end mills, the thickness of the applied coating layer in such modes is 10-30 micrometers. (Results and discussion) It was noted that the performed analysis of the applied hardening methods confirmed the feasibility of using electric spark processing for these purposes. The optimal modes of electric spark processing were selected on the basis of previous studies. Comparative tests for wear resistance of samples from materials of end mills (P18) and drills (P6M5) were performed. It was shown that when using electrodes made of T15K6, the wear resistance of the samples increases by 1.85 times for P18 and by 8 times for P6M5. Comparative tests of hardened end mills and drills for wear resistance were carried out. The indicator was the number of processed parts. (Conclusions) The result of using the electric spark processing method to harden the cutting tool was an increase in the life of end mills by 3.8 times, drills by 2.25 times. The productivity of manufacturing parts has increased.

In Situ Evaluation of Drill Wear Using Tool Image Captured on Machining Center

Owing to the rise in demand for electric devices, there has been an increase in the need for manufacturing equipment that produces internal control board parts. To operate this machinery, several ceramic components, such as a chuck table and fastening parts, are required. Consequently, the need for efficiently and precisely machining ceramics has increased. However, ceramics are known for their high hardness, which can lead to tool breakage when using a small tool. This is often influenced by the state of the tool wear. If the drill tip breaks off and becomes embedded in the workpiece, it could take time to remove or destroy the workpiece. To prevent such problems, drills are replaced after a certain number of machining processes, or the operator visually inspects the drill’s wear condition. Unfortunately, these methods reduce machining efficiency. Therefore, we propose a device that captures drill images on a machine tool and measures the amount of drill wear to evaluate the drill’s condition. We fabricated a device to acquire drill images and attempted to quantify the drill wear condition, such as the area and width of the worn part, by analyzing the worn shape from an image of the bottom surface of the drill.

Special Issue on Abrasive Technology for High-Precision and High-Efficiency Machining of High-Performance Materials

The demand for high-precision and high-efficient machining of high-performance materials and components has increased in various industries such as optical, automotive, communication, life sciences, and medical sciences. Certain difficult-to-machine materials can be reliably machined using deterministic precision cutting processes. However, hard and brittle materials such as ceramics, carbides, hardened steel molds, glassy materials, or semiconductor materials, require precision abrasive technologies with super abrasives like diamond, cBN, or new tool materials for machining. However, machining high-precision components and their molds/dies using abrasive processes is considerably more difficult due to their complex and non-deterministic nature and textured surfaces. Furthermore, high-energy processes such as laser technology can assist abrasive technologies in ensuring higher precision and efficiency. Precision grinding and polishing processes are primarily used to generate high-quality and functional components, typically made of difficult-to-machine materials. Thus, the surface quality achievable by precision grinding and polishing processes becomes more important for reducing machining time and costs. This special issue features 10 papers on the most recent advances in precision abrasive technologies. These papers cover the following topics: - Ultrasonic grinding of micro holes using cemented WC tools - Drilling holes in CFRP aircraft using cBN electroplated ball end mill - In situ evaluation of drill wear using tool images - Grinding belt based on modified information entropy - Radial directional vibration-assisted grinding of Al2O3 ceramics - Chatter vibration suppression using fixed superabrasive polishing stone - Free abrasive finishing of internal channels with different cross-sectional geometries - High-quality machining of cemented carbide using PCD ball end mills - Fixed-abrasive machining with magnetic brush for Ti-6Al-4V ELI alloy - High-efficient polishing of polymer surfaces using catalyst-referred etching This issue will provide an understanding of the recent developments in abrasive technologies, leading to further research. We deeply appreciate the careful work of all the authors, and we thank the reviewers for their incisive efforts.

Micro drilling characterization of the carbon and carbon–aramid (hybrid) composites

AbstractIn this study, micro‐drilling tests were carried out on composite laminates reinforced with hybrid (aramid‐carbon) and carbon fibers. Thrust force, hole quality, tool wear, and temperature parameters were measured and assessed following each drilling test. The quality of holes includes circularity, overcut, hole damage factor (HDF), and taper ratio. Micro‐drilling was performed on a CNC milling machine using 0.6 mm drill diameter, 7500 rpm, and 0.5 mm/min feed parameters. The plates are 3.05 mm (carbon fiber) and 3.45 mm (hybrid) thick. According to the results, the maximum thrust force (29.8 N) was measured in the hybrid composite due to the high fracture toughness of the aramid. In addition, it was determined that the thrust force and drill wear were related. The increase in thrust force during drilling caused an increase in drill wear values. While the average wear of the drill in carbon composite was 0.013 mm, the average wear of the drill in hybrid composite was 0.021 mm. Thrust force and tool temperatures were found to have a similar trend. The highest tool temperature was measured as 86.7°C in the hybrid composite. Hole quality results showed that drilling in carbon composite was more successful than in hybrid composite. Holes drilled in carbon composite have lower values for overcut (0.017 mm), HDF (1.028), and taper ratio (0.008). It has been understood that hole quality depends on thrust force and damage formation.Highlights Carbon and hybrid (carbon‐aramid) composites were produced with a vacuum infusion process (VIP). Micro‐drilling of composites was investigated focusing on thrust force, tool temperature, and hole quality. Hybrid composites exhibited greater thrust forces due to their higher toughness compared to carbon composites. Hybrid composites showed higher maximum tool temperatures compared to carbon composites. The carbon composites exhibited better hole quality compared to the hybrid composites.

A New Architecture Paradigm for Tool Wear Prediction during AISI 9840 Drilling Operation

In conventional machining processes based on the chip removal mechanism, the progressive wear of the tool determines a change of the geometric characteristics of the cutting edge. Tool wear is a complex phenomenon and related tool life depends on several factors, such as cutting parameters, lubrication, and tool-workpiece relative trajectories. Tool wear progression affects the quality of the machined parts, making the tool replacement necessary even before its breakage. Moreover, in industrial practice, tool replacement cannot depend on the subjectivity of the operator, thus, the definition of an optimized strategy for cutting tool wear monitoring before tool failure is mandatory. This work compares Random Forest and Neural Network models for predicting tool wear in drilling. For the development of predictive models, tool life tests were performed by drilling through holes on AISI 9840 steel parts, with a coated tungsten carbide drill of 8 mm of diameter and by using constant cutting parameters. Flank wear of the tool was monitored. A set of statistical features computed from the vibration signals, the acoustic emission signals, the power signals and the torque signals constitute the input of the algorithms. The classification accuracy was 88% and 91% for Neural Network and Random Forest models respectively. In order to correctly define the tool replacement policy during production, the developed Random Forest model has been implemented in an industry, through production management software, achieving promising results.

Correlation between intraosseous thermal change and drilling impulse data during osteotomy within autonomous dental implant robotic system: An invitro study.

This study aims at examining the correlation of intraosseous temperature change with drilling impulse data during osteotomy and establishing real-time temperature prediction models. A combination of invitro bovine rib model and Autonomous Dental Implant Robotic System (ADIR) was set up, in which intraosseous temperature and drilling impulse data were measured using an infrared camera and a six-axis force/torque sensor respectively. A total of 800 drills with different parameters (e.g., drill diameter, drill wear, drilling speed, and thickness of cortical bone) were experimented, along with an independent test set of 200 drills. Pearson correlation analysis was done for linear relationship. Four machining learning (ML) algorithms (e.g., support vector regression [SVR], ridge regression [RR], extreme gradient boosting [XGboost], and artificial neural network [ANN]) were run for building prediction models. By incorporating different parameters, it was found that lower drilling speed, smaller drill diameter, more severe wear, and thicker cortical bone were associated with higher intraosseous temperature changes and longer time exposure and were accompanied with alterations in drilling impulse data. Pearson correlation analysis further identified highly linear correlation between drilling impulse data and thermal changes. Finally, four ML prediction models were established, among which XGboost model showed the best performance with the minimum error measurements in test set. The proof-of-concept study highlighted close correlation of drilling impulse data with intraosseous temperature change during osteotomy. The ML prediction models may inspire future improvement on prevention of thermal bone injury and intelligent design of robot-assisted implant surgery.

Hybrid adaptive control of CNC drilling for enhancement of tool life and surface quality

Intelligent machining requires the online adaptation of the machining parameters to improve tool life and product quality and to reduce machining costs. This article presents a novel hybrid adaptive control (HAC) system for a drilling process. The HAC system is a combination of two adaptive controls: geometric adaptive control (GAC) and adaptive control by optimisation (ACO). It keeps the roughness of the holes within tolerance without compromising tool life. A response surface model (RSM) is used for modelling the drill wear and surface roughness with speed, feed, acceleration and force signals as inputs. The model predicts the wear and roughness with prediction accuracies of 97.1% and 93.6%, respectively. The roughness control is achieved through a Massachusetts Institute of Technology rule and tool wear is minimised by genetic algorithm optimisation. The adaptive algorithms are simulated and validated for the machining conditions given by the adaptive algorithms. The results show an improved tool life of 7% and surface roughness of 11%.

The Effect of Dental Implant Drills Materials on Heat Generation in Osteotomy Sites: A Systematic Review.

The aim of this review was to examine the impact of dental implant drill materials and wear profiles on heat generation in the osteotomy sites as reported in experimental studies and to critically appraise these studies. The research question was formulated based on predefined patient, intervention, comparison, and outcome (PICO) elements. A comprehensive electronic search was undertaken in Medline/PubMed Central, Science Direct, and Google Scholar, using predetermined keywords, followed by a manual search of the bibliography of the selected articles. The selection of the studies for the critical appraisal part of our study was based on the criteria used to assess the study designs such as study aims, outcome measure, clarity of method, sample selection, randomization, allocation concealment, sample attrition, bias, method of data analysis, and external validity. Increased heat generation was observed with both ceramic and metal drills; the heat generation was proportional to drills' wear. The literature was inconclusive regarding the association between drill material and heat generation. However, drill materials had a significant influence on the overall temperature increase during osteotomy. The noncoated drills showed a higher wear resistance, and it has been observed that using worn drills leads to more friction contact, reduced drill cutting efficiency, and increased heat generation. Eleven in vitro studies met the inclusion criteria, and showed a considerable methodological heterogeneity and confounding factors, including drill geometry, speed and load, depth and diameter, number of uses, irrigation protocol, study specimens, and the heat measuring device. Besides, most of the studies have a potential operator and assessor bias, and some have sponsorship bias. It is possible to conclude that the literature is not conclusive on the effect of drill materials on heat generation during osteotomy. Lack of standardization and uniformity in the study design, along with potential bias in the study methodology can be the reason for the heterogeneity of the results.

Wear factors and mechanisms of L-80 steel casings

Casing wear in directional drilling is inevitable and may result in catastrophic failure of the casing column. It is thus essential to understand its mechanisms and quantify its extent by estimating the casing wear factor. In this research, actual field casing samples, drilling pipe joints and muds were considered. An in-house built testing facility was used to test several L-80 casing samples by considering three rotational speeds of the drill pipe tool joint (DP-TJ) (115, 154, and 207 rpm) and three side loads (1000 N, 1200 N and 1400 N). The influence of the water- and oil-based muds on the coefficient of friction, wear volume, wear factor and wear mechanisms were investigated. The results revealed that under water-based mud (WBM) lubrication casing wear volume and wear factors were more than twice that of oil-based mud (OBM) lubrication. Moreover, it was observed that as the side load increased under both OBM and WBM lubrication at a constant rpm, both wear volume and wear factor increased. However, increasing the rotational speed while maintaining the side load constant decreased the wear factor, owing to the localized softening effect caused by the high heat generation at the contact area and the possible hydrodynamic lubrication regime change at higher speeds. The analysis of the digital microscopic images taken at the wear region shows that abrasive mechanism was dominant in the case of OBM lubrication. On the other hand, both abrasive and adhesive wear mechanisms were present under WBM lubrication.

Comparative study of drilling super austenitic stainless steel with external applications of reduced quantity nanofluid and flooded bio-lubricant

AISI 904L is a super austenitic stainless steel (SASS) with excellent corrosion resistance and good mechanical strength at high temperatures that can be applied in extreme operational conditions. Consequently, it has low machinability. Research involving SASS drilling is scarce in the literature. Thus, this work aims to evaluate the TiAlN-coated carbide drill life associated with thrust force and torque in the full-hole drilling of the Ultra® 904L under external lubrication with a 100-mm/min feed rate. Nanofluid-based reduced quantity lubricant (2.0 L/h) at 840-rpm spindle speed and flood bio-lubricant conditions (540 L/h) at 840- and 1260-rpm spindle speeds were applied, totaling three tool-life tests. Digital image analysis showed that the failures in the main cutting edges occurred due to chipping resulting from the rupture of the built-up edge. The drill wear did not reach expressive values. Low tool life was observed with nanofluid in reduced quantity (E1 test, 0.67 min) and flood condition with 1260 rpm (E3 test, 1.34 min). The longest drill life was registered for 840 rpm with flood biofluid (E2 test, 8.04 min). Lastly, a growth of 11% in thrust force and 9% in torque were noted when increasing the lubricooling flow from the E1 to E2 test; however, there was a reduction of 31% and 42% when raising the spindle speed from E2 to E3 test. The flood condition resulted in a longer drill life and smaller thrust force and torque values (at least 20% lower), which are associated with chip deformation, hardness, and friction coefficient reductions due to increased cutting speed and temperature. Moreover, built-up edges were formed during the drilling of all the holes.

Microstructure and properties of composite coatings by laser cladding Inconel 625 and reinforced WC particles on non-magnetic steel

Due to the extremely complex and strict service conditions of non-magnetic drill collars, the friction, wear and corrosion will lead to their damage and failure. Therefore, the surface modification technology can be used to substantially improve the surface performance and effectively extend the service life of non-magnetic drill collars. Under this engineering background, the laser cladding technology was employed to fabricate pure Inconel 625 and Inconel 625/WC coatings on the surface of TWZ-2 non-magnetic steel substrate, and the influences of WC content on the microstructure, phase composition, element distribution, microhardness and wear and corrosion resistance of these coatings were analyzed. The experimental results show that the microstructures of pure Inconel 625 and Inconel 625/WC coatings are all composed of planar grain, cellular grain, columnar grain and equiaxed grain successively. The phases of pure Inconel 625 coating are composed of γ-Ni phase and Laves phase. With WC particles mixed into Inconel 625 powder, various carbide phases appear in the composite coatings. The higher the WC content, the more the precipitated carbides formed by the reaction, thus aggravating the segregation of the solid solution strengthening elements. Besides, W and C elements present severe local segregation due to the undissolved WC particles. In terms of the hardening and strengthening effects of WC particles, the microhardness of the coatings gradually increases with the rise of WC content. The wear mechanisms of substrate and coatings are all compound wears, and different wear forms have different priorities. Accordingly, the wear mechanisms would change with the WC content. The higher the WC content, the lower the wear loss of coating, and the better the wear and friction resistance. Moreover, the corrosion resistance of coatings is better than that of substrate. With the increase of WC content, the corrosion resistance of the composite coatings decreases gradually.

Tool wear assessment and life prediction model based on image processing and deep learning

Drilling is one of the most classical machining operations. Real-time monitoring of drill wear can effectively testify whether the product fails to meet the specifications due to drill failure. This paper proposes a tool wear assessment and life prediction model based on image processing and deep learning methods, which works effectively for small sample datasets and for low-quality images. The normal areas and worn areas of the drill bits are extracted using the U-Net network and traditional image processing methods, respectively. Moreover, the original dataset is classified using the migration learning technique. The wear level of a drill bit can be accurately evaluated through experimental tests. Testing results show that the proposed method is more convenient and efficient than previous methods using manual measurements. These results can be applied to real-time drill wear monitoring, thus reducing part damage caused by tool wear.

Machinability analysis in Drilling Composites and drilling woven GFR/epoxy composites using the SPSS Method

Drilling processes in fiber-reinforced polymer composites Composite structures are essential for assembly and fabrication of parts. The economic impact of rejecting the drilled area, when reaching the assembly stage, it is important to consider the associated loss. Therefore, the motivations in drilling E-Class Fiber Reinforced Epoxy (GFRE) composites, this explains cutting conditions on torque and wear Feed, speed and pre-drill wear values. Four feeds (0.056, 0.112, 0.22, 0.315, 0.45 mm/rev) and three speeds (6.41, 12.71, 20.25, 32.03, and 50.63 m/min) and five pre-drill wear values and four artificially introduced wears) were used. Values; W = 7, 19, 26, 34 All samples are 8 mm diameter holes Drilled using a cemented carbide drill bit. Current In work, Multi-linear Regression models were used were used, Parameters of mechanical properties are related to: Thrust, torsion, peel-up, delamination, push-out delamination, Drill wear and machining parameters such as surface roughness before: feed and speed. Perforated model has high resolution; Scanning is done using flatbed color scanner, then to estimate the delamination factor, Image analysis was performed using Corel DRAW software. Multi-variable regression analysis significant coefficients of each variable, contribution is made to promotion and elimination. Laminate thickness on torque and displacement factors the results illustrate that there are significant effects. Cronbach's alpha value for the model is 0.924. Speed, Wear, Feed, Ft, T, Del.Peel, Del.Push and Ra. Results: the Cronbach's Alpha Reliability result. The overall Cronbach's Alpha value for the model is 0.924 which indicates 92% reliability. From the literature review, the above 50% Cronbach's Alpha value model can be considered for analysis

Investigation of wear mechanism in drilling of PPA composites for automotive industry

Drilling is one of the most widely used processes in manufacturing and assembling parts used in the automotive industry.Drill bits wear out due to increased forces and temperatures during the drilling process. Drill wear is one of the most important problems when drilling conventional or composite materials. This reduces the quality of the hole and degrades the drill. In this study, the wear mechanism during drilling of PPA composites used in the automotive industry was investigated. Therefore, TiN-coated HSS drills were utilized in the tests. A TiN-coated HSS drill with a diameter of 5 mm and a tip angle of 118° was used to perform the drilling test. Drilling tests were performed in dry conditions on a 3-axis CNC milling machine. Three cutting speeds of 15, 20 and 25 m/min and feed rates of 0.05, 1.0 and 1.5 mm/rev were selected for the drilling conditions. Scanning Electron Microscopic (SEM) images and Energy Dispersive Spectrum (EDS) analysis were used to study the wear mechanism. As a result of the investigation, the mechanisms of abrasive wear and adhesive wear were identified. Drills also showed flank wear, corner rounding, chipping, and coating flaking.

Automatic Estimation of Drill Wear Based on Images of Holes Drilled in Melamine Faced Chipboard with Machine Learning Algorithms

In this article, an approach to drill wear evaluation is presented. Tool condition monitoring is an important problem in furniture manufacturing and similar industries. At the same time, approaches that rely on sets of sensors, often tend to be to robust or complex for the production environment. Instead of signals acquired from dedicated sensors, presented approach uses images of drilled holes as input data. Initial pictures are processed and enhanced in order to highlight the crucial properties. A set of selected features is then calculated on the resulting images, and later used during the training of 5 state-of-the-art classifiers. Presented research also evaluates number of images for consecutive drillings that needs to be taken into account in order to produce accurate results. From the selected set, the best performing classifier was Random Forest and it achieved close to 100% accuracy.

Advanced Feature Extraction Methods from Images of Drillings in Melamine Faced Chipboard for Automatic Diagnosis of Drill Wear.

In this paper, a novel approach to evaluation of feature extraction methodologies is presented. In the case of machine learning algorithms, extracting and using the most efficient features is one of the key problems that can significantly influence overall performance. It is especially the case with parameter-heavy problems, such as tool condition monitoring. In the presented case, images of drilled holes are considered, where state of the edge and the overall size of imperfections have high influence on product quality. Finding and using a set of features that accurately describes the differences between the edge that is acceptable or too damaged is not always straightforward. The presented approach focuses on detailed evaluation of various feature extraction approaches. Each chosen method produced a set of features, which was then used to train a selected set of classifiers. Five initial feature sets were obtained, and additional ones were derived from them. Different voting methods were used for ensemble approaches. In total, 38 versions of the classifiers were created and evaluated. Best accuracy was obtained by the ensemble approach based on Weighted Voting methodology. A significant difference was shown between different feature extraction methods, with a total difference of 11.14% between the worst and best feature set, as well as a further 0.2% improvement achieved by using the best voting approach.