In this study, a preliminary evaluation was made of the applicability ofthe signalsof the cutting forces, vibration and acoustic emission in diagnosis of the hardness and microstructure of ausferritic ductile iron and tool edge wear rate during its machining. Tests were performed on pearlitic-ferritic ductile iron and on three types of ausferritic ductile iron obtained by austempering at 400, 370 and 320⁰C for 180 minutes. Signals of the cutting forces (F), vibration (V) and acoustic emission (AE) were registered while milling each type of the cast iron with a milling cutter at different degrees of wear. Based on individual signals from all the sensors, numerous measures were determined such as e.g. the average or maximum signal value. It was found that different measures from all the sensors tested depended on the microstructure and hardness of the examined material, and on the tool condition. Knowing hardness of the material and the cutting tool edge condition, it is possible to determine the structure of the material .Simultaneous diagnosis of microstructure, hardness, and the tool condition is probably feasible, but it would require the application of a diagnostic strategy based on the integration of numerous measures, e.g. using neural networks.
The prediction of machined surface parameters is an important factor in machining centre development. There is a great need to elaborate a method for on-line surface roughness estimation [1-7]. Among various measurement techniques, optical methods are considered suitable for in-process measurement of machined surface roughness. These techniques are non-contact, fast, flexible and tree-dimensional in nature. The optical method suggested in this paper is based on the vision system created to acquire an image of the machined surface during the cutting process. The acquired image is analyzed to correlate its parameters with surface parameters. In the application of machined surface image analysis, the wavelet methods were introduced. A digital image of a machined surface was described using the one-dimensional Digital Wavelet Transform with the basic wavelet as Coiflet. The statistical description of wavelet components made it possible to develop the quality measure and correlate it with surface roughness [8-11]. For an estimation of surface roughness a neural network estimator was applied [12-16]. The estimator was built to work in a recurrent way. The current value of the Ra estimation and the measured change in surface image features were used for forecasting the surface roughness Ra parameter. The results of the analysis confirmed the usability of the application of the proposed method in systems for surface roughness monitoring.
The article presents the issue of calibration and verification of an original module, which is a part of the robotic turbojet engines elements processing station. The task of the module is to measure turbojet engine compressor blades geometric parameters. These type of devices are used in the automotive and the machine industry, but here we present their application in the aviation industry. The article presents the idea of the module, operation algorithm and communication structure with elements of a robot station. The module uses Keyence GT2-A32 contact sensors. The presented information has an application nature. Functioning of the module and the developed algorithm has been tested, the obtained results are satisfactory and ensure sufficient process accuracy. Other station elements include a robot with force control, elements connected to grinding such as electrospindles, and security systems.
Development of mineral deposits located at significant depth may be carried out by means of vertical shafts. Shaft sinking technology usually requires a number of works to be carried out, including the selection of appropriate excavating techniques adapted to geological and hydrological conditions, including natural hazards. The production technology and the machines used determine the level of sinking costs and execution period. The article discusses the excavating technologies currently used across the world. Then the assumptions, concept and construction of a new generation of shaft sinking system were presented. The proposed new solution of the system and the excavating technology allow for parallel execution of key processes related to winning, loading, transport and shaft wall-side lining, which significantly increases the progress of sinking. The shaft sinking system was created by scientists from AGH in cooperation with KOPEX – Przedsiębiorstwo Budowy Szybów S. A. and Instytut Techniki Górniczej KOMAG.
The article presents the results of research on the finishing of M63 Z4 brass by vibratory machining. Brass alloy was used for the research due to the common use of ammunition elements, cartridge case and good cold forming properties on the construction. Until now, the authors have not met with the results of research to determine the impact of abrasive pastes in container processing. It was found that the additive for container abrasive treatment of abrasive paste causes larger mass losses and faster surface smoothing effects. The treatment was carried out in two stages: in the first stage, the workpieces were deburred and then polished. Considerations were given to the impact of mass of workpieces, machining time and its type on mass loss and changes in the geometric structure of the surface. The surface roughness of machining samples was measured with the Talysurf CCI Lite optical profiler. The suggestions for future research may be to carry out tests using abrasive pastes with a larger granulation of abrasive grains, and to carry out tests for longer processing times and to determine the time after which the parameters of SGP change is unnoticeable.
The present paper aims at presenting a short study of the prefixed forms of the Polish verb pić (‘to drink’) (napić, wypić, popić, przepić, opić, zapić, etc.) and their French equivalents found in two parallel corpora: Glosbe and Reverso Context. In the first part, selected theoretical approaches concerning the verbal prefixation in Polish are discussed, with particular attention to the hypothesis of “perfective hypercategory” by Włodarczyk and Włodarczyk (2001b). The second part focuses on the results of the contrastive Polish-French analysis. The research is carried out in the general framework of the Aktionsarten theory and tries to discover by which linguistic means (grammatical and/or lexical) the French language expresses different semantic values conveyed by the Polish prefixes. The results of the analysis are appropriately formalized according to the principles of the object-oriented approach by Banyś (2002a, b), i.e. described by the syntactic-semantic schemes (which, after several changes of specifi cation, can be applied in the machine translation programs). The purpose of the investigation is, therefore, twofold: theoretical, since it is the matter of discovering certain relations between two languages expressing differently a given linguistic phenomenon, and practice, which consists in formulating interlinguistic correspondence rules for the purpose of the Polish-French translation.
This paper presents a comprehensive methodology for measuring and characterizing the surface topographies on machined steel parts produced by precision machining operations. The performed case studies concern a wide spectrum of topographic features of surfaces with different geometrical structures but the same values of the arithmetic mean height Sa. The tested machining operations included hard turning operations performed with CBN tools, grinding operations with Al2O3 ceramic and CBN wheels and superfinish using ceramic stones. As a result, several characteristic surface textures with the Sa roughness parameter value of about 0.2 μm were thoroughly characterized and compared regarding their potential functional capabilities. Apart from the standard 2D and 3D roughness parameters, the fractal, motif and frequency parameters were taken in the consideration.
Automation of machining operations, being result of mass volume production of components, imposes more restrictive requirements concerning mechanical properties of starting materials, inclusive of machinability mainly. In stage of preparation of material, the machinability is influenced by such factors as chemical composition, structure, mechanical properties, plastic working and heat treatment, as well as a factors present during machining operations, as machining type, cutting parameters, material and geometry of cutting tools, stiffness of the system: workpiece – machine tool – fixture and cutting tool. In the paper are presented investigations concerning machinability of the EN AC-AlSi9Cu3(Fe) silumin put to refining, modification and heat treatment. As the parameter to describe starting condition of the alloy was used its tensile strength Rm. Measurement of the machining properties of the investigated alloy was performed using a reboring method with measurement of cutting force, cutting torque and cutting power. It has been determined an effect of the starting condition of the alloy on its machining properties in terms of the cutting power, being indication of machinability of the investigated alloy. The best machining properties (minimal cutting power - Pc=48,3W) were obtained for the refined alloy, without heat treatment, for which the tensile strength Rm=250 MPa. The worst machinability (maximal cutting power Pc=89,0W) was obtained for the alloy after refining, solutioning at temperature 510 o C for 1,5 hour and aged for 5 hours at temperature 175 o C. A further investigations should be connected with selection of optimal parameters of solutioning and ageing treatments, and with their effect on the starting condition of the alloy in terms of improvement of both mechanical properties of the alloy and its machining properties, taking into consideration obtained surface roughness.
This paper deals with production of safety inlay for steam locomotive valve by the Patternless Process method. For the moulds creation was used moulding mixtures of II. generation, whereas binder was used a water glass. CNC miller was used for creation of mould cavity. Core was created also by milling into block made of moulding compound. In this article will be presented also making of 3D model, setting of milling tool paths and parameters for milling.
The paper presents the production problems related to casting using precision casting methods. The essential adverse effect of the casting process is the presence of burrs understood as oversize material necessary to remove the next finishing operations. In addition, the surfaces of the cast often characterized by a porous structure. One of the methods to improve the smoothness of the area proposed by the authors is the use of vibro-abrasive finishing. This type of treatment is widely used in the treatment of finishing small objects as well as complex shapes. Objects in the form of casting in the first step was treated with aggressive deburring polyester matrix abrasive media. The second stage was polishing, with using smoothing porcelain media. The study evaluated the effect of vibro-abrasive machining typical cast on the basic parameters of the geometric structure of the surface. Observations using optical microscope Nicon Eclipse MA 200 compared changes in surface microstructure and the effect of deburring. Clearly we can say that vibro-abrasive machining an effective way of reducing the size of burrs, smoothing and lightening the surface of objects made by casting.
The article presents an example of finishing treatment for aluminum alloys with the use of vibration machining, with loose abrasive media in a closed tumbler. For the analysis of selected properties of the surface layer prepared flat samples of aluminum alloy PA6/2017 in the state after recrystallization. The samples in the first stage were subjected to a treatment of deburring using ceramic media. In a second step polishing process performed with a strengthening metal media. In addition, for comparative purposes was considered. only the case of metal polishing. The prepared samples were subjected to hardness tests and a tangential tensile test. As a result of finishing with vibratory machining, it was possible to remove burrs, flash, rounding sharp edges, smoothing and lightening the surface of objects made. The basic parameters of the surface geometry were obtained using the Talysurf CCI Lite - Taylor Hobson optical profiler. As a result of the tests it can be stated that the greatest reduction of surface roughness and mass loss occurs in the first minutes of the process. Mechanical tests have shown that the most advantageous high values of tensile strength and hardness are obtained with two-stage vibration treatment, - combination of deburring and polishing. Moreover the use of metal media resulted in the strengthening of the surface by pressure deburring with metal media.
The study aimed to apply the protection from damage to engineering facilities located near a planned underwater aggregate extraction. The analysis was conducted in compliance with mining regulations and expert opinions. The study also aimed to assess the precision and correctness of the extraction, due to economic aspects. To reach the goals, in-situ research of the mining area was conducted, with the help of an advanced bathymetric device, based on the USV methodology. The instrument – named by the author as Smart-Sonar-Boat – was especially designed for underwater surveys in open-pit aggregate mines. The study analyzed the “Dwory” open-pit mine, located in southern Poland in the city of Oświęcim. The bathymetric results obtained contributed to improving the observation of changes in the bottom during the extraction. The applied USV method allowed for conducting the reliable evaluation of the mining work.
Quality evaluation is very important for haptic rendering. In this paper, an objective evaluation method for a haptic rendering system based on haptic perception features is proposed. In the method, the haptic rendering process is compared to the real world perception process in a simple standardized procedure based on feature extraction and data analysis. A complete evaluation process for a simple haptic rendering task of pressing a virtual spring is presented as an example to explain the method in detail. Compared with the traditional objective method based on error statistics, the method is more concerned about the consistency of human subjective feelings rather than physical parameters, which makes the evaluation process more consistent with the haptic perception mechanism. The results of comparative analysis show that the method presented in this paper is simple, gives reliable results reflecting the consistency with subjective feeling and has a better discrimination ability for different kinds of devices and algorithms compared with the traditional evaluation methods.
This paper presents a study of control strategies for 5-phase permanent magnet synchronous motors (PMSMs) supplied by a five-leg voltage source inverter. Based on the vectorial decomposition of the multi-phase machine, fictitious machines, magnetically decoupled, allow a more adequate control. In this paper, our study focuses on the vector control of a multi-phase machine using a linear proportional-integral-derivative (PID) current regulator in the cases of sinusoidal and trapezoidal back-electromotive force (EMF) waveforms. In order to determine currents’ references, two strategies are adopted. First one aims to minimize copper losses under constant torque, while the second one targets to increase torque for a given copper losses. These techniques are tested under a variable speed control strategy based on a proportional-integral (PI) regulator and experimentally validated.
To avoid of manipulating search engines results by web spam, anti spam system use machine learning techniques to detect spam. However, if the learning set for the system is out of date the quality of classification falls rapidly. We present the web spam recognition system that periodically refreshes the learning set to create an adequate classifier. A new classifier is trained exclusively on data collected during the last period. We have proved that such strategy is better than an incrementation of the learning set. The system solves the starting–up issues of lacks in learning set by minimisation of learning examples and utilization of external data sets. The system was tested on real data from the spam traps and common known web services: Quora, Reddit, and Stack Overflow. The test performed among ten months shows stability of the system and improvement of the results up to 60 percent at the end of the examined period.
Freeform surfaces have wider engineering applications. Designers use B-splines, Non-Uniform Rational B-splines, etc. to represent the freeform surfaces in CAD, while the manufacturers employ machines with controllers based on approximating functions or splines. Different errors also creep in during machining operations. Therefore the manufactured freeform surfaces have to be verified for conformance to design specification. Different points on the surface are probed using a coordinate measuring machine and substitute geometry of surface established from the measured points is compared with the design surface. The sampling points are distributed according to different strategies. In the present work, two new strategies of distributing the points on the basis of uniform surface area and dominant points are proposed, considering the geometrical nature of the surfaces. Metrological aspects such as probe contact and margins to be provided along the sides have also been included. The results are discussed in terms of deviation between measured points and substitute surface as well as between design and substitute surfaces, and compared with those obtained with the methods reported in the literature.
The machining technology of electrochemical micromachining with ultra short voltage pulses (μPECM) is based on the already well-established fundamentals of common electrochemical manufacturing technologies. The enormous advantage of the highest manufacturing precision underlies the fact of the extremely small working gaps achievable through ultra short voltage pulses in nanosecond duration. This describes the main difference with common electrochemical technologies. With the theoretical resolution of 10 nm, this technology enables high precision manufacturing.
The application of artificial intelligence (AI) in modeling of various machining processes has been the topic of immense interest among the researchers since several years. In this direction, the principle of fuzzy logic, a paradigm of AI technique, is effectively being utilized to predict various performance measures (responses) and control the parametric settings of those machining processes. This paper presents the application of fuzzy logic to model two non-traditional machining (NTM) processes, i.e. electrical discharge machining (EDM) and electrochemical machining (ECM) processes, while identifying the relationships present between the process parameters and the measured responses. Moreover, the interaction plots which are developed based on the past experimental observations depict the effects of changing values of different process parameters on the measured responses. The predicted response values derived from the developed models are observed to be in close agreement with those as investigated during the past experimental runs. The interaction plots also play significant roles in identifying the optimal parametric combinations so as to achieve the desired responses for the considered NTM processes.
Electrical Discharge Machining (EDM) process with copper tool electrode is used to investigate the machining characteristics of AISI D2 tool steel material. The multi-wall carbon nanotube is mixed with dielectric fluids and its end characteristics like surface roughness, fractal dimension and metal removal rate (MRR) are analysed. In this EDM process, regression model is developed to predict surface roughness. The collection of experimental data is by using L9 Orthogonal Array. This study investigates the optimization of EDM machining parameters for AISI D2 Tool steel using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Analysis of variance (ANOVA) and F-test are used to check the validity of the regression model and to determine the significant parameter affecting the surface roughness. Atomic Force Microscope (AFM) is used to capture the machined image at micro size and using spectroscopy software the surface roughness and fractal dimensions are analysed. Later, the parameters are optimized using MINITAB 15 software, and regression equation is compared with the actual measurements of machining process parameters. The developed mathematical model is further coupled with Genetic Algorithm (GA) to determine the optimum conditions leading to the minimum surface roughness value of the workpiece.
In the work results of research on electrodischarge machining (EDM) of titanium alloy Ti10V2Fe3Al with (α + β) structure were presented. Preliminary heat treatment of samples allows to obtain different morphology and volume fraction of the α phase. The main goal of research was to assessment of the material microstructure impact on EDM technological factors (ie. material removal rate, tool wear) and morphology of technological surface layer. Electrodischarge machining is alternative and increasingly used method of titanium alloys machining. Research allowed to indicate the possibilities and limitations of use EDM in this area. It is especially important in the aspect of parts produced for aircraft industry and related requirements for the technological surface layer quality.
The condition monitoring techniques like acoustic emission, vibration analysis, and infrared thermography, used for the failure diagnosis of bearings, require longer processing time, as they have to perform acoustical measurement followed by signal processing and further analysis using special software. However, for any bearing, its period of usage can be easily determined within an hour, by measuring the bearing sound, using sound level meter (SLM). In this paper the acoustical analysis of the spindle bearing of a radial drilling machine was performed using SLM, by measuring the sound pressure level of the bearing in decibels, for different frequencies, while muting all the other noises. Then using an experimental set up, two database readings were taken, one for new bearing and the other for completely damaged bearing, both are SKF6207, which itself is the spindle bearing. From these three sets of sound pressure level readings, the period of usage of the spindle bearing, was calculated using an interpolation equation, by substituting the life of the bearing from the manufacturer’s catalogue. Therefore, for any machine with a SKF6207 bearing, its usage time can be estimated using the database readings and one measurement on that machine, all with the same speed.
In this paper a scaling approach for the solution of 2D FE models of electric machines is proposed. This allows a geometrical and stator and rotor resistance scaling as well as a rewinding of a squirrel cage induction machine enabling an efficient numerical optimization. The 2D FEM solutions of a reference machine are calculated by a model based hybrid numeric induction machine simulation approach. In contrast to already known scaling procedures for synchronous machines the FEM solutions of the induction machine are scaled in the stator-current-rotor-frequency-plane and then transformed to the torque- speed-map. This gives the possibility to use a new time scaling factor that is necessary to keep a constant field distribution. The scaling procedure is validated by the finite element method and used in a numerical optimization process for the sizing of an electric vehicle traction drive considering the gear ratio. The results show that the scaling procedure is very accurate, computational very efficient and suitable for the use in machine design optimization.
Accurate network fault diagnosis in smart substations is key to strengthening grid security. To solve fault classification problems and enhance classification accuracy, we propose a hybrid optimization algorithm consisting of three parts: anti-noise processing (ANP), an improved separation interval method (ISIM), and a genetic algorithm-particle swarm optimization (GA-PSO) method. ANP cleans out the outliers and noise in the dataset. ISIM uses a support vector machine (SVM) architecture to optimize SVM kernel parameters. Finally, we propose the GA-PSO algorithm, which combines the advantages of both genetic and particle swarm optimization algorithms to optimize the penalty parameter. The experimental results show that our proposed hybrid optimization algorithm enhances the classification accuracy of smart substation network faults and shows stronger performance compared with existing methods.