Experimental and theoretical study of workpiece temperature when end milling hardened steels using (TiAl)N-coated and PcBN-tipped tools

The present work shows an experimental and theoretical study on heat flow when end milling, at high-speed, hardened steels applied to moulds and dies. AISI H13 and AISI D2 steels were machined with two types of ball nose end mills: coated with (TiAl)N and tipped with PcBN. The workpiece geometry was designed to simulate tool-workpiece interaction in real situations found in mould industries, in which complex surfaces and thin walls are commonly machined. The compressed and cold air cooling systems were compared to dry machining Results indicated a relatively small temperature variation, with higher range when machining AISI D2 with PcBN-tipped end mill. All cooling systems used demonstrated good capacity to remove heat from the machined surface, especially the cold air. Compressed air was the most indicated to keep workpiece at relatively stable temperature. A theoretical model was also proposed to estimate the energy transferred to the workpiece (Q) and the average convection coefficient ((h) over bar) for the cooling systems used. The model used a FEM simulation and a steepest decent method to find the best values for both variables. (c) 2007 Elsevier B.V. All rights reserved.

Inverter-fed linear asynchronous machine

The behaviors of an arc-shaped stator induction machine (the sector-motor) and a disc-secondary linear induction motor are analyzed in this work for different values of the frequency. Variable frequency is produced by a voltage source controlled-current inverter which keeps constant the r.m.s. value of the phase current, also assuring a sinusoidal waveform. For the simulations of the machine developed thrust, an equivalent circuit is used. It is obtained through the application of the one-dimensional theory to the modeling. The circuit parameters take into account the end effects, always present is these kind of machines. The phase current waveforms are analyzed for their harmonic contents. Experimental measurements were carried out in laboratory and are presented with the simulations, for comparison.

New approach of induction machine drive using an adaptive digital PID controller with gain planning based on artificial neural networks

The present work introduces a new strategy of induction machines speed adjustment using an adaptive PID (Proportional Integral Derivative) digital controller with gain planning based on the artificial neural networks. This digital controller uses an auxiliary variable to determine the ideal induction machine operating conditions and to establish the closed loop gain of the system. The auxiliary variable value can be estimated from the information stored in a general-purpose artificial neural network based on CMAC (Cerebellar Model Articulation Controller).

Line based camera calibration in machine vision dynamic applications

The problem of dynamic camera calibration considering moving objects in close range environments using straight lines as references is addressed. A mathematical model for the correspondence of a straight line in the object and image spaces is discussed. This model is based on the equivalence between the vector normal to the interpretation plane in the image space and the vector normal to the rotated interpretation plane in the object space. In order to solve the dynamic camera calibration, Kalman Filtering is applied; an iterative process based on the recursive property of the Kalman Filter is defined, using the sequentially estimated camera orientation parameters to feedback the feature extraction process in the image. For the dynamic case, e.g. an image sequence of a moving object, a state prediction and a covariance matrix for the next instant is obtained using the available estimates and the system model. Filtered state estimates can be computed from these predicted estimates using the Kalman Filtering approach and based on the system model parameters with good quality, for each instant of an image sequence. The proposed approach was tested with simulated and real data. Experiments with real data were carried out in a controlled environment, considering a sequence of images of a moving cube in a linear trajectory over a flat surface.

Effect of powder milling and dopant addition on the current-voltage characteristics of SnO2-based ceramics

Non-linear electrical properties of SnO2-based ceramics were investigated as a function of powder agglomeration condition and as a function of dopant addition. All doped powders presented a single phase, cassiterite, as evidenced by X-ray diffraction analysis. The effect of milling was quite evident, with non-milled powder showing higher agglomerated particle size than milled powder. Cr addition seemed to increase the non-linear coefficient. Cu and Mn rendered dense ceramics, but α values for systems with Mn were higher than for systems with Cu.

Identification of synchronous machine parameters using load rejection test data

This work shows a computational methodology for the determination of synchronous machines parameters using load rejection test data. By machine modeling one can obtain the quadrature parameters through a load rejection under an arbitrary reference, reducing the present difficulties. The proposed method is applied to a real machine.

Maize germ recovery and quality using the intermittent milling and dynamic steeping process

The intermittent milling and dynamic steeping (IMDS) process is an alternative method developed for wet milling of maize. In this process, the steeping stage can be reduced to 5 h by soaking maize in water at 60°C for 2 h and cracking the kernels to remove solution components diffusional barriers with minimum germ damage. Maize was dynamically steeped in solutions with 0.0, 0.1, and 0.2% sulphur dioxide (SO2) and 0.00, 0.55% lactic acid. Germ recovery, germ damage, fibre in germ, oil content and uncracked kernels were determined. A conventional steeping procedure was also performed. Germ recovery was higher for all tests using both SO2 and lactic acid than for the others with best germ yield for concentrations of 0.2% SO2 and 0.55% lactic acid. Germ damage ranged from 7.4 to 18.2% for all tests. The presence of lactic acid in the steeping solution decreased the amount of fibre in germ fraction. Germ oil content ranged from 39.3% (0-0% SO2, 0.55% lactic acid) to 44.0% (0.2% SO2, 0.55% lactic acid) for all treatments using IMDS. The smallest difference was 5.5% between IMDS (0.2% SO2, 0.55% lactic acid) and the conventional 36 h steeping process. An average of 1.3% of kernels remained uncracked after IMDS process. © 2002 Silsoe Research Institute. Published by Elsevier Science Ltd. All rights reserved.

Life with a hemodialysis machine

The aim of the present study was to describe the experience of patients undergoing haemodialysis starting from their own perception. A qualitative perspective using Merleau Ponty's Existential Phenomenology was considered to be the most appropriate methodology for this study. Fifteen patients were interviewed in a haemodialysis unit at a Brazilian teaching hospital. Interviews were based on the question 'What does the experience of living with a haemodialysis machine mean?' Convergences in speeches were grouped into three categories: the machine, improvement in quality of life, reflection on patients' experience. These findings show the existential reality patients experience. A haemodialysis machine dictates their lives: they have to accept strict rules controlled by a team of healthcare providers. They realize it has to be so and there is no way out. It is the only way to get some relief from the symptoms of the disease. The feeling is mostly acceptance of the condition. Healthcare providers' dedication is recognized. Some participants complain bout painful procedures, others deny them, others fantasize the reality. An essential piece of information is the lack of future perspectives; few patients mentioned the possibility of a transplant or the possibility of carrying out their own care. The study may contribute in outlining new perspectives for nurses to understand the needs of patients undergoing haemodialysis. An approach accepting patients' views will probably bring awareness to patients as to the possibilities of helping with their own treatment.

Classification of petroleum well drilling operations using Support Vector Machine (SVM)

During the petroleum well drilling operation many mechanical and hydraulic parameters are monitored by an instrumentation system installed in the rig called a mud-logging system. These sensors, distributed in the rig, monitor different operation parameters such as weight on the hook and drillstring rotation. These measurements are known as mud-logging records and allow the online following of all the drilling process with well monitoring purposes. However, in most of the cases, these data are stored without taking advantage of all their potential. On the other hand, to make use of the mud-logging data, an analysis and interpretationt is required. That is not an easy task because of the large volume of information involved. This paper presents a Support Vector Machine (SVM) used to automatically classify the drilling operation stages through the analysis of some mud-logging parameters. In order to validate the results of SVM technique, it was compared to a classification elaborated by a Petroleum Engineering expert. © 2006 IEEE.

Estimation of electrical machine speed using sensorless technology and neural networks

The use of sensorless technologies is an increasing tendency on industrial drivers for electrical machines. The estimation of electrical and mechanical parameters involved with the electrical machine control is used very frequently in order to avoid measurement of all variables related to this process. The cost reduction may also be considered in industrial drivers, besides the increasing robustness of the system, as an advantage of the use of sensorless technologies. This work proposes the use of a recurrent artificial neural network to estimate the speed of induction motor for sensorless control schemes using one single current sensor. Simulation and experimental results are presented to validate the proposed approach. ©2008 IEEE.

Analysis of electric torque in single machine-infinite bus system using phase shifters

ResumoThe main idea of this work is based on the analysis of the electric torque through the acting of the PS in the power system, provided of a control for the compensation degree (PSC). A linear model of the single machine-infinite bus system is used with a PS installed (SMIB/PS system). The variable that represents the presence of PS in the net is associated to the phase displacement introduced in the terminal voltage of the synchronous machine by PS. For the input signals of the PSC are evaluated variations of the angular speed of the rotor, the current magnitude and the active power through the line where the PS is located. The simulations are accomplished to analyze the influence of the PS in the torque formation (synchronizing and damping), of the SMIB/PS system. The analysis are developed in the time and frequency domain.

Use of high energy ball milling on the sintering optimization of alumina ceramics

In this work, the effect of the milling time on the densification of the alumina ceramics with or without 5wt.%Y 2O 3, is evaluated, using high-energy ball milling. The milling was performed with different times of 0, 2, 5 or 10 hours. All powders, milled at different times, were characterized by X-Ray Diffraction presenting a reduction of the crystalline degree and crystallite size as function of the milling time increasing. The powders were compacted by cold uniaxial pressing and sintered at 1550°C-60min. Green density of the compacts presented an increasing as function of the milling time and sintered samples presented evolution on the densification as function of the reduction of the crystallite size of the milled powders. © (2010) Trans Tech Publications.

Efficiency evaluation of ZrB2 incorporation in the MgB 2 matrix phase using high-energy milling

The present study suggests the use of high energy ball milling to mix (to dope) the phase MgB2 with the AlB2 crystalline structure compound, ZrB2, with the same C32 hexagonal structure than MgB 2, in different concentrations, enabling the maintenance of the crystalline phase structures practically unaffected and the efficient mixture with the dopant. The high energy ball milling was performed with different ball-to-powder ratios. The analysis of the transformation and formation of phases was accomplished by X-ray diffractometry (XRD), using the Rietveld method, and scanning electron microscopy. As the high energy ball milling reduced the crystallinity of the milled compounds, also reducing the size of the particles, the XRD analysis were influenced, and they could be used as comparative and control method of the milling. Aiming the recovery of crystallinity, homogenization and final phase formation, heat treatments were performed, enabling that crystalline phases, changed during milling, could be obtained again in the final product. © (2010) Trans Tech Publications.

High-yield direct synthesis of Mg 2FeH 6 from the elements by reactive milling

Magnesium complex hydrides as Mg 2FeH 6 are interesting phases for hydrogen storage in the solid state, mainly due to its high gravimetric and volumetric densities of H2. However, the synthesis of this hydride is not trivial because the intermetallic phase Mg2Fe does not exist and Mg and Fe are virtually immiscible under equilibrium conditions. In this study, we have systematically studied the influence of the most important processing parameters in reactive milling under hydrogen (RM) for Mg 2FeH 6 synthesis: milling time, ball-to-powder weight ratio (BPR), hydrogen pressure and type of mill. Low cost 2Mg-Fe mixtures were used as raw materials. An important control of the Mg 2FeH 6 direct synthesis by RM was attained. In optimized combinations of the processing parameters, very high proportions of the complex hydride could be obtained. © (2011) Trans Tech Publications.

Precipitates segmentation from scanning electron microscope images through machine learning techniques

The presence of precipitates in metallic materials affects its durability, resistance and mechanical properties. Hence, its automatic identification by image processing and machine learning techniques may lead to reliable and efficient assessments on the materials. In this paper, we introduce four widely used supervised pattern recognition techniques to accomplish metallic precipitates segmentation in scanning electron microscope images from dissimilar welding on a Hastelloy C-276 alloy: Support Vector Machines, Optimum-Path Forest, Self Organizing Maps and a Bayesian classifier. Experimental results demonstrated that all classifiers achieved similar recognition rates with good results validated by an expert in metallographic image analysis. © 2011 Springer-Verlag Berlin Heidelberg.