Control of thermal damage in grinding by digital signal processing of raw acoustic emission

Grinding process is usually the last finishing process of a precision component in the manufacturing industries. This process is utilized for manufacturing parts of different materials, so it demands results such as low roughness, dimensional and shape error control, optimum tool-life, with minimum cost and time. Damages on the parts are very expensive since the previous processes and the grinding itself are useless when the part is damaged in this stage. This work aims to investigate the efficiency of digital signal processing tools of acoustic emission signals in order to detect thermal damages in grinding process. To accomplish such a goal, an experimental work was carried out for 15 runs in a surface grinding machine operating with an aluminum oxide grinding wheel and ABNT 1045 e VC131 steels. The acoustic emission signals were acquired from a fixed sensor placed on the workpiece holder. A high sampling rate acquisition system at 2.5 MHz was used to collect the raw acoustic emission instead of root mean square value usually employed. In each test AE data was analyzed off-line, with results compared to inspection of each workpiece for burn and other metallurgical anomaly. A number of statistical signal processing tools have been evaluated.

Study on the behavior of the Minimum quantity lubricant - MQL technique under different lubricating and cooling conditions when grinding ABNT 4340 steel

The behavior of the minimum quantity lubricant (MQL) technique was analyzed under different lubricating and cooling conditions when grinding ABNT 4340 steel. The comparative analysis of the residual stress values showed that residual compressive stresses were obtained under all the lubrication/cooling conditions and types of abrasive tools employed. The highest residual compressive stress obtained with the aluminum oxide grinding wheel with MQL under the condition of V= 30m/s for air and V= 40ml/h for lubricant was -376MPa against the -160MPa attained with conventional cooling, representing a 135% increase in residual compressive stress. The results show that method and quantity of lubricant and cooling are factors that influence the grinding process.

Global analysis of aerodynamics deflectors efficiency in the grinding process

The conventional, grinding methods in some cases are not very efficient because the arising of thermal damages in the pieces is very common. Optimization methods of cutting fluid application in the grinding zone are essential to prevent thermal problems from interaction of the wheel grains with the workpiece. surface. The optimization can happen through the correct selection of the cut parameters and development of devices that eliminate air layer effects generated around the grinding wheel. This article will collaborate with the development of an experimentation methodology which allows evaluating, comparatively, the performance of the deflectors in the cutting region to minimize the air layer effect of the high speed of the grinding wheel. The air layers make the cutting fluid jet to dissipate in the machine. An optimized nozzle was used in order to compare the results with the conventional method (without baffles or deflectors) of cutting fluid application. The results showed the high eficciency of the deflectors or baffles in the finish results. Copyright © 2006 by ABCM.

Application of MLP and Kohonen networks for recognition of wear patterns of single-point dressers

Grinding is a parts finishing process for advanced products and surfaces. However, continuous friction between the workpiece and the grinding wheel causes the latter to lose its sharpness, thus impairing the grinding results. This is when the dressing process is required, which consists of sharpening the worn grains of the grinding wheel. The dressing conditions strongly affect the performance of the grinding operation; hence, monitoring them throughout the process can increase its efficiency. The objective of this study was to estimate the wear of a single-point dresser using intelligent systems whose inputs were obtained by the digital processing of acoustic emission signals. Two intelligent systems, the multilayer perceptron and the Kohonen neural network, were compared in terms of their classifying ability. The harmonic content of the acoustic emission signal was found to be influenced by the condition of dresser, and when used to feed the neural networks it is possible to classify the condition of the tool under study.

Neural networks models for wear patterns recognition of single-point dresser

Grinding is a workpiece finishing process for advanced products and surfaces. However, the constant friction between workpiece and grinding wheel causes the latter to lose its sharpness, thereby impairing the result of the grinding process. When this occurs, the dressing process is essential to sharpen the worn grains of the grinding wheel. The dressing conditions strongly influence the performance of the grinding operation; hence, monitoring them throughout the process can increase its efficiency. The purpose of this study was to classify the wear condition of a single-point dresser using intelligent systems whose inputs were obtained by digitally processing acoustic emission signals. Two multilayer perceptron (MLP) neural networks were compared for their classification ability, one using the root mean square (RMS) statistics and another the ratio of power (ROP) statistics as input. In this study, it was found that the harmonic content of the acoustic emission signal is influenced by the condition of the dresser, and that the condition of the tool under study can be classified by using the aforementioned statistics to feed a neural network. © IFAC.

Análise da influência da velocidade de mergulho na retificação de aços endurecidos utilizando refrigeração otimizada

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Utilização da técnica da mínima quantidade de lubrificante (MQL) com adição de água no processo de retificação cilíndrica externa de aços endurecidos com rebolos de CBN

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Aprimoramento da técnica da mínima quantidade de lubrificante (MQL) no processo de retificação cilíndrica externa de mergulho de aços endurecidos

Pós-graduação em Ciência e Tecnologia de Materiais - FC

Otimização da retificação tangencial plana de compósitos plásticos reforçados com fibras de carbono

Pós-graduação em Ciência e Tecnologia de Materiais - FC

Influência dos métodos de lubri-refrigeração na retificação do aço ABNT 4340 usando rebolo de CBN

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Otimização das condições de refrigeração / lubrificação no processo de retificação cilíndrica de mergulho

Pós-graduação em Engenharia Mecânica - FEG

INFLUENCE OF HVOF COATING ON THE FATIGUE STRENGTH OF 15-5 PH STAINLESS STEEL

Structural component failures due to cyclic loading are associated to surface damage of materials and its interaction with environment. Fatigue failure occurs with stresses below the yield strength of each material and is a result of crack initiation and propagation. In aeronautical components is an important parameter to be considered in project, as well as the corrosion and wear resistance. Thermally sprayed HVOF coatings have been considered to replace galvanic chromium deposits with comparable performance for wear and corrosion resistance. The aim of present research is to study the influence of WC-13Co-4Cr applied by HVOF, on the axial fatigue strength of 15-5 PH stainless steel. The shot peening treatment was used to restore fatigue performance.

Neural tool condition estimation in the grinding of advanced ceramics

Ceramic parts are increasingly replacing metal parts due to their excellent physical, chemical and mechanical properties, however they also make them difficult to manufacture by traditional machining methods. The developments carried out in this work are used to estimate tool wear during the grinding of advanced ceramics. The learning process was fed with data collected from a surface grinding machine with tangential diamond wheel and alumina ceramic test specimens, in three cutting configurations: with depths of cut of 120 mu m, 70 mu m and 20 mu m. The grinding wheel speed was 35m/s and the table speed 2.3m/s. Four neural models were evaluated, namely: Multilayer Perceptron, Radial Basis Function, Generalized Regression Neural Networks and the Adaptive Neuro-Fuzzy Inference System. The models'performance evaluation routines were executed automatically, testing all the possible combinations of inputs, number of neurons, number of layers, and spreading. The computational results reveal that the neural models were highly successful in estimating tool wear, since the errors were lower than 4%.

Grinding process monitoring based on electromechanical impedance measurements

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Friction and wear of performance of MoDTC and ester-containing lubrificants over steel surfaces under reciprocating conditions

Since the earliest developments of human history, friction has been a major issue. From the invention of the wheel and the use of the first lubricants to the studiesof coated and microtexturized surfaces, significant effort has been put on improvements that couldovercome the resistance to motion. Areview by Holmberg, Andersson and Erdemir[1] shows that, in an average passenger car, about one third of the total energy consumptionis due to friction losses. Of these, another one third is consumed in the engine system. The optimization of the lubricating oil formulation used ininternal combustion enginesis an important way to reduce friction, therefore improving energeticefficiencyand controllingemissions.Lubrication is also a way to assure the required protection to the system by maintaining wear rates in an adequate level, which helps to minimize maintenance costs.