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.

Effect of ball milling on structural and physicochemical characteristics of cassava and Peruvian carrot starches

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Multi-choice mixed integer goal programming optimization for real problems in a sugar and ethanol milling company

Goal Programming (GP) is an important analytical approach devised to solve many realworld problems. The first GP model is known as Weighted Goal Programming (WGP). However, Multi-Choice Aspirations Level (MCAL) problems cannot be solved by current GP techniques. In this paper, we propose a Multi-Choice Mixed Integer Goal Programming model (MCMI-GP) for the aggregate production planning of a Brazilian sugar and ethanol milling company. The MC-MIGP model was based on traditional selection and process methods for the design of lots, representing the production system of sugar, alcohol, molasses and derivatives. The research covers decisions on the agricultural and cutting stages, sugarcane loading and transportation by suppliers and, especially, energy cogeneration decisions; that is, the choice of production process, including storage stages and distribution. The MCMIGP allows decision-makers to set multiple aspiration levels for their problems in which the more/higher, the better and the less/lower, the better in the aspiration levels are addressed. An application of the proposed model for real problems in a Brazilian sugar and ethanol mill was conducted; producing interesting results that are herein reported and commented upon. Also, it was made a comparison between MCMI GP and WGP models using these real cases. © 2013 Elsevier Inc.

Artificial neural networks for surface roughness prediction when face milling Al 7075-T7351

In this work, different artificial neural networks (ANN) are developed for the prediction of surface roughness (R a) values in Al alloy 7075-T7351 after face milling machining process. The radial base (RBNN), feed forward (FFNN), and generalized regression (GRNN) networks were selected, and the data used for training these networks were derived from experiments conducted using a high-speed milling machine. The Taguchi design of experiment was applied to reduce the time and cost of the experiments. From this study, the performance of each ANN used in this research was measured with the mean square error percentage and it was observed that FFNN achieved the best results. Also the Pearson correlation coefficient was calculated to analyze the correlation between the five inputs (cutting speed, feed per tooth, axial depth of cut, chip°s width, and chip°s thickness) selected for the network with the selected output (surface roughness). Results showed a strong correlation between the chip thickness and the surface roughness followed by the cutting speed. © ASM International.

Contribución al desarrollo del proceso de selección de centros de mecanizado de alta velocidad, basado en parámetros tecnológicos y de productividad

La selección de centros de mecanizado de alta velocidad es un proceso complejo que requiere de mucha experiencia, puesto que en él intervienen un gran número de variables, tanto tecnológicas como económicas. Existen metodologías orientadas a seleccionar el centro de mecanizado óptimo, considerando únicamente una de estas dos tipologías de variables, sin embargo, esta tesis propone una metodología que contempla ambos tipos. Para ello se identifican las variables que tienen mayor influencia sobre los resultados del proceso de mecanizado, tanto desde un punto de vista de calidad de las piezas fabricadas como de la economía de la fabricación, y se propone un modelo de selección basado en los resultados de un trabajo experimental realizado sobre piezas de aluminio. Dicho modelo se implementa mediante redes neurales, cuyo entrenamiento se realiza en base a los resultados del trabajo experimental mencionado.

Influence of the tool edge geometry on specific cutting energy at high-speed cutting

This paper presents specific cutting energy measurements as a function of the cutting speed and tool cutting edge geometry. The experimental work was carried out on a vertical CNC machining center with 7,500 rpm spindle rotation and 7.5 kW power. Hardened steels ASTM H13 (50 HRC) were machined at conventional cutting speed and high-speed cutting (HSC). TiN coated carbides with seven different geometries of chip breaker were applied on dry tests. A special milling tool holder with only one cutting edge was developed and the machining forces needed to calculate the specific cutting energy were recorded using a piezoelectric 4-component dynamometer. Workpiece roughness and chip formation process were also evaluated. The results showed that the specific cutting energy decreased 15.5% when cutting speed was increased up to 700%. An increase of 1 °in tool chip breaker chamfer angle lead to a reduction in the specific cutting energy about 13.7% and 28.6% when machining at HSC and conventional cutting speed respectively. Furthermore the workpiece roughness values evaluated in all test conditions were very low, closer to those of typical grinding operations (∼0.20 μm). Probable adiabatic shear occurred on chip segmentation at HSC Copyright © 2007 by ABCM.

Mechanochemical synthesis of yttrium manganite

Yttrium manganite (YMnO3) is a multiferroic material, which means that it exhibits both ferromagnetic and ferroelectric properties, so making it interesting for a variety of technological applications. In this work, single-phase YMnO3 was prepared for the first time by mechanochemical synthesis in a planetary ball mill. The YMnO3 was formed directly from the highly activated constituent oxides, Y 2O3 and Mn2O3, after 60 min of milling time. During prolonged milling, the growth of the particles occurred. The cumulative energy introduced into the system during milling for 60 min was 86 kJ/g. The X-ray powder-diffraction analysis indicates that the as-prepared samples crystallize with an orthorhombic (Pnma) YMnO3 structure. The morphology and chemical composition of the powder were investigated by SEM and FESEM. The magnetic properties of the obtained YMnO3 powders were found to change as a function of the milling time in a manner consistent with the variation in the nanocomposite microstructure. © 2012 Elsevier B.V. All rights reserved.

Inteligencia artificial para la predicción y control del acabado superficial en procesos de fresado a alta velocidad

El objetivo principal de esta tesis doctoral es profundizar en el análisis y diseño de un sistema inteligente para la predicción y control del acabado superficial en un proceso de fresado a alta velocidad, basado fundamentalmente en clasificadores Bayesianos, con el prop´osito de desarrollar una metodolog´ıa que facilite el diseño de este tipo de sistemas. El sistema, cuyo propósito es posibilitar la predicción y control de la rugosidad superficial, se compone de un modelo aprendido a partir de datos experimentales con redes Bayesianas, que ayudar´a a comprender los procesos dinámicos involucrados en el mecanizado y las interacciones entre las variables relevantes. Dado que las redes neuronales artificiales son modelos ampliamente utilizados en procesos de corte de materiales, también se incluye un modelo para fresado usándolas, donde se introdujo la geometría y la dureza del material como variables novedosas hasta ahora no estudiadas en este contexto. Por lo tanto, una importante contribución en esta tesis son estos dos modelos para la predicción de la rugosidad superficial, que se comparan con respecto a diferentes aspectos: la influencia de las nuevas variables, los indicadores de evaluación del desempeño, interpretabilidad. Uno de los principales problemas en la modelización con clasificadores Bayesianos es la comprensión de las enormes tablas de probabilidad a posteriori producidas. Introducimos un m´etodo de explicación que genera un conjunto de reglas obtenidas de árboles de decisión. Estos árboles son inducidos a partir de un conjunto de datos simulados generados de las probabilidades a posteriori de la variable clase, calculadas con la red Bayesiana aprendida a partir de un conjunto de datos de entrenamiento. Por último, contribuimos en el campo multiobjetivo en el caso de que algunos de los objetivos no se puedan cuantificar en números reales, sino como funciones en intervalo de valores. Esto ocurre a menudo en aplicaciones de aprendizaje automático, especialmente las basadas en clasificación supervisada. En concreto, se extienden las ideas de dominancia y frontera de Pareto a esta situación. Su aplicación a los estudios de predicción de la rugosidad superficial en el caso de maximizar al mismo tiempo la sensibilidad y la especificidad del clasificador inducido de la red Bayesiana, y no solo maximizar la tasa de clasificación correcta. Los intervalos de estos dos objetivos provienen de un m´etodo de estimación honesta de ambos objetivos, como e.g. validación cruzada en k rodajas o bootstrap.---ABSTRACT---The main objective of this PhD Thesis is to go more deeply into the analysis and design of an intelligent system for surface roughness prediction and control in the end-milling machining process, based fundamentally on Bayesian network classifiers, with the aim of developing a methodology that makes easier the design of this type of systems. The system, whose purpose is to make possible the surface roughness prediction and control, consists of a model learnt from experimental data with the aid of Bayesian networks, that will help to understand the dynamic processes involved in the machining and the interactions among the relevant variables. Since artificial neural networks are models widely used in material cutting proceses, we include also an end-milling model using them, where the geometry and hardness of the piecework are introduced as novel variables not studied so far within this context. Thus, an important contribution in this thesis is these two models for surface roughness prediction, that are then compared with respecto to different aspects: influence of the new variables, performance evaluation metrics, interpretability. One of the main problems with Bayesian classifier-based modelling is the understanding of the enormous posterior probabilitiy tables produced. We introduce an explanation method that generates a set of rules obtained from decision trees. Such trees are induced from a simulated data set generated from the posterior probabilities of the class variable, calculated with the Bayesian network learned from a training data set. Finally, we contribute in the multi-objective field in the case that some of the objectives cannot be quantified as real numbers but as interval-valued functions. This often occurs in machine learning applications, especially those based on supervised classification. Specifically, the dominance and Pareto front ideas are extended to this setting. Its application to the surface roughness prediction studies the case of maximizing simultaneously the sensitivity and specificity of the induced Bayesian network classifier, rather than only maximizing the correct classification rate. Intervals in these two objectives come from a honest estimation method of both objectives, like e.g. k-fold cross-validation or bootstrap.

Mechanistic approach to predict real machining time for milling free-form geometries applying high feed rate

An accurate estimate of machining time is very important for predicting delivery time, manufacturing costs, and also to help production process planning. Most commercial CAM software systems estimate the machining time in milling operations simply by dividing the entire tool path length by the programmed feed rate. This time estimate differs drastically from the real process time because the feed rate is not always constant due to machine and computer numerical controlled (CNC) limitations. This study presents a practical mechanistic method for milling time estimation when machining free-form geometries. The method considers a variable called machine response time (MRT) which characterizes the real CNC machine`s capacity to move in high feed rates in free-form geometries. MRT is a global performance feature which can be obtained for any type of CNC machine configuration by carrying out a simple test. For validating the methodology, a workpiece was used to generate NC programs for five different types of CNC machines. A practical industrial case study was also carried out to validate the method. The results indicated that MRT, and consequently, the real machining time, depends on the CNC machine`s potential: furthermore, the greater MRT, the larger the difference between predicted milling time and real milling time. The proposed method achieved an error range from 0.3% to 12% of the real machining time, whereas the CAM estimation achieved from 211% to 1244% error. The MRT-based process is also suggested as an instrument for helping in machine tool benchmarking.

Mechanistic approach to predict real machining time for milling free-form geometries applying high feed rate

An accurate estimate of machining time is very important for predicting delivery time, manufacturing costs, and also to help production process planning. Most commercial CAM software systems estimate the machining time in milling operations simply by dividing the entire tool path length by the programmed feed rate. This time estimate differs drastically from the real process time because the feed rate is not always constant due to machine and computer numerical controlled (CNC) limitations. This study presents a practical mechanistic method for milling time estimation when machining free-form geometries. The method considers a variable called machine response time (MRT) which characterizes the real CNC machine's capacity to move in high feed rates in free-form geometries. MRT is a global performance feature which can be obtained for any type of CNC machine configuration by carrying out a simple test. For validating the methodology, a workpiece was used to generate NC programs for five different types of CNC machines. A practical industrial case study was also carried out to validate the method. The results indicated that MRT, and consequently, the real machining time, depends on the CNC machine's potential: furthermore, the greater MRT, the larger the difference between predicted milling time and real milling time. The proposed method achieved an error range from 0.3% to 12% of the real machining time, whereas the CAM estimation achieved from 211% to 1244% error. The MRT-based process is also suggested as an instrument for helping in machine tool benchmarking.

Dynamic behavior analysis of drill-threading process when machining AISI Al-Si-Cu(4) alloy

Conventional threading operations involve two distinct machining processes: drilling and threading. Therefore, it is time consuming for the tools must be changed and the workpiece has to be moved to another machine. This paper presents an analysis of the combined process (drilling followed by threading) using a single tool for both operations: the tap-milling tool. Before presenting the methodology used to evaluate this hybrid tool, the ODS (operating deflection shapes) basics is shortly described. ODS and finite element modeling (FEM) were used during this research to optimize the process aiming to achieve higher stable machining conditions and increasing the tool life. Both methods allowed the determination of the natural frequencies and displacements of the machining center and optimize the workpiece fixture system. The results showed that there is an excellent correlation between the dynamic stability of the machining center-tool holder and the tool life, avoiding a tool premature catastrophic failure. Nevertheless, evidence showed that the tool is very sensitive to work conditions. Undoubtedly, the use of ODS and FEM eliminate empiric decisions concerning the optimization of machining conditions and increase drastically the tool life. After the ODS and FEM studies, it was possible to optimize the process and work material fixture system and machine more than 30,000 threaded holes without reaching the tool life limit and catastrophic fail.

A study of high-speed milling characteristics of nitinol

High-speed milling (HSM) has many advantages over conventional machining. Among these advantages, the lower cutting force associated with the machining process is of particular significance for Nitinol alloys because their machined surfaces show less strain hardening. In this article, a systematic study has been carried out to investigate the machining characteristics of a Ni50.6Ti49.4 alloy in HSM. The effects of cutting speed, feed rate, and depth of cut on machined surface characteristics and tool wear are studied. It is found that an increase in cutting speed has resulted in a better surface finish and less work hardening. This is attributed to the reduction of chip cross-sectional area or chip thickness, which thus leads to a lower cutting force or load.