Studies on the cooling minimum quantity and conventional cooling at hardened steels in grinding process

The purpose of this work is to explain the concept of cutting fluids reasonable usage through the fluid minimum quantity in grinding processes. on that purpose, the development of a new nozzle and an own and adequate methodology should be required in order to obtain good results and compare them to the conventional methods. The analysis of the grinding wheel/cutting fluid performance was accomplished from the following input parameters: flow rate variation by nozzle diameter changes (three diameters values: 3mm, 4mm and 5mm), besides the conventional round nozzle already within the machine. Integral oil and a synthetic emulsion were used as cutting fluids and a conventional grinding wheel was employed. The workpieces were made of steel VC 131, tempered and quenched with 60HRc. Thus, as the flow rate and the nozzle diameter changes, keeping steady fluid jet velocity (equal to cutting velocity), attempted to find the best machining conditions, with the purpose to obtain a decrease on the cutting fluid volume, taking into consideration the analysis of the process output variables such as cutting strength, cutting specific energy, grinding wheel wear and surface roughness. It was verified that the 3mm diameter optimized nozzle and the integral oil, in general, was the best combination among all proposed.

Automatic detection of thermal damage in grinding process by artificial neural network

Esse trabalho tem por objetivo o desenvolvimento de um sistema inteligente para detecção da queima no processo de retificação tangencial plana através da utilização de uma rede neural perceptron multi camadas, treinada para generalizar o processo e, conseqüentemente, obter o limiar de queima. em geral, a ocorrência da queima no processo de retificação pode ser detectada pelos parâmetros DPO e FKS. Porém esses parâmetros não são eficientes nas condições de usinagem usadas nesse trabalho. Os sinais de emissão acústica e potência elétrica do motor de acionamento do rebolo são variáveis de entrada e a variável de saída é a ocorrência da queima. No trabalho experimental, foram empregados um tipo de aço (ABNT 1045 temperado) e um tipo de rebolo denominado TARGA, modelo ART 3TG80.3 NVHB.

Analysis of the Influence of Sparkout Time on Grinding Using Several Lubrication/Cooling Methods

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

Performance of aerodynamic baffles in cylindrical grinding analyzed on the basis of air layer pressure and speed

Over the years, grinding has been considered one of the most important manufacturing processes. Grinding is a high precision process, and the loss of a single workpiece in this stage of the production is unacceptable, fir the value added to the material is very high due to many processes it has already undergone prior to grinding. This study aims to contribute toward the development of an experimental methodology whereby the pressure and speed of the air layer produced by the high rotation of the grinding wheel is evaluated with and without baffles, i.e., in an optimized grinding operation and in a traditional one. Tests were also carried out with steel samples to check the difference in grinding wheel wear with and without the use of baffles.

Production grinding burn detection using acoustic emission and electric power signals

This work uses a monitoring system based on a PC platform, where the acoustic emission and electric power signals generated during the grinding process are used to investigate superficial burning occurrence in a surface grinding operation using two types of steel, three grinding conditions and an Al203 vitrified grinding wheel. Acoustic emission signals on the workpiece and grinding power were measured during a surface plunge operation until the grinding burn happened. From the results the standard deviation of the acoustic emission signal and the maximum electric power were calculated for each grinding pass. The proposed DPO parameter is the product between the power level and acoustic emission standard deviation. The results show that both signals can be used for burning detection, and the parameter DPO is the best indicator for the burning studied in this work. This can be explained by the high dispersion of the acoustic emission RMS level associated to the high power consumption when the grinding wheel lose its sharpness.

The improvement in grinding of inlet engine valves by the adoption of the most effective cuting fluid type

In this paper is presented an experimental research in which the grinding of seating surfaces of inlet engine valves was improved by the adoption of the most effective cutting fluid type, matching the new requirements of cutting fluid application. Four different types of cutting fluids (straight oil and three different types of soluble oils) were analyzed. As qualitative and quantitative evaluation parameters of the performance of the cutting fluids, the roughness, the grinding wheel wear, the cutting force and the workpiece residual stress were determined. As a conclusion, the straight oil was the cutting fluid that presented the best results in all of the parameters analyzed. Copyright © 2000 Society of Automotive Engineers, Inc.

Estudo comparativo do desempenho de rebolos com grãos superabrasivos e convencionais

Grinding is a precision machining process which is widely used in the manufacture of components requiring fine tolerances and smooth surfaces. There are several imput parameters (cutting conditions, cutting fluid and grinding wheel type used, dressing conditions etc.) which can affect the process variables (tangential and normal cutting forces, roughness, grinding temperatures, G ratio, etc.) leading to differences in the roughness, in the surface integrity and in the mechanical strength of the ground component. Consequently, the imput parameters must be controlled in order to insure the workpiece final quality. This paper presents a comparative evaluation of the performance of two types of grinding wheels [a conventional (Al2O3) and a superabrasive (CBN)] when grinding a VC131 steel, by the analysis of specific process variables when varying the cutting conditions. Highest values of G ratio and lowest workpiece roughness was observed when using CBN grinding wheels. This confirms the global trend of replacement of alumina grinding wheels by CBN, when grinding DTG (difficult to grind) materials.

In-process thermal damage detection in grinding with monitoring via Internet

This work aims the development of a dedicated system for detection of burning in surface grinding process, where the process will constantly be monitored through the acoustic emission and electric power of the induction motor drive. Acquired by an analog-digital converter, algorithms process the signals and a control signal is generated to inform the operator or interrupt the process in case of burning occurrence. Moreover, the system makes possible the process monitoring via Internet. Additionally, a comparative study between parameters DPO and FKS is carried through. In the experimental work one type of. steel (ABNT-1020 annealed) and one type of grinding wheel referred to as TARGA, model ART 3TG80.3 NVHB, were employed.

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 of thresholds to burning in surface grinding process

This work aims at finding out the threshold to burning in surface grinding process. Acoustic emission and electric power signals are acquired from an analog-digital converter and processed through algorithms in order to generate a control signal to inform the operator or interrupt the process in the case of burning occurrence. The thresholds that dictate the situation of burn and non-burn were studied as well as a comparison between the two parameters was carried out. In the experimental work one type of steel (ABNT-1045 annealed) and one type of grinding wheel referred to as TARGA model 3TG80.3-NV were employed. Copyright © 2005 by ABCM.

In-process grinding monitoring through acoustic emission

This work aims to investigate the efficiency of digital signal processing tools of acoustic emission signals in order to detect thermal damages in grinding processes. 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 Steel as work material. The acoustic emission signals were acquired from a fixed sensor placed on the workpiece holder. A high sampling rate data acquisition system working at 2.5 MHz was used to collect the raw acoustic emission instead of the root mean square value usually employed. Many statistical analyses have shown to be effective to detect burn, such as the root mean square (RMS), correlation of the AE, constant false alarm rate (CFAR), ratio of power (ROP) and mean-value deviance (MVD). However, the CFAR, ROP, Kurtosis and correlation of the AE have been presented more sensitive than the RMS. Copyright © 2006 by ABCM.

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.

Neural network approach for surface roughness prediction in surface grinding

Several systems are currently tested in order to obtain a feasible and safe method for automation and control of grinding process. This work aims to predict the surface roughness of the parts of SAE 1020 steel ground in a surface grinding machine. Acoustic emission and electrical power signals were acquired by a commercial data acquisition system. The former from a fixed sensor placed near the workpiece and the latter from the electric induction motor that drives the grinding wheel. Both signals were digitally processed through known statistics, which with the depth of cut composed three data sets implemented to the artificial neural networks. The neural network through its mathematical logical system interpreted the signals and successful predicted the workpiece roughness. The results from the neural networks were compared to the roughness values taken from the worpieces, showing high efficiency and applicability on monitoring and controlling the grinding process. Also, a comparison among the three data sets was carried out.

Monitoring the dressing operation in the grinding process

This work was based on a methodology of development and experimentation, and involved monitoring the dressing operation by processing the acoustic emission and electric power signals to detect the optimal dressing moment. Dressing tests were performed in a surface grinding machine with an aluminium grinding wheel. Dressing analysis software was developed and used to process the signals collected earlier in order to analyse not only the dressing parameters but also the software's ability to indicate the instant when the dressing operation could be concluded. Parameters used in the study of burn in grinding were implemented in order to ascertain if they would also prove efficient in monitoring dressing. A comparative study revealed that some parameters are capable of monitoring the dressing operation. It was possible to verify the parameters effectiveness that today are utilised in burning to monitor dressing as well as to create new parameters for monitoring this operation. Copyright © 2009, Inderscience Publishers.

Anfis applied to the prediction of surface roughness in grinding of advanced ceramics

This paper introduces a methodology for predicting the surface roughness of advanced ceramics using Adaptive Neuro-Fuzzy Inference System (ANFIS). To this end, a grinding machine was used, equipped with an acoustic emission sensor and a power transducer connected to the electric motor rotating the diamond grinding wheel. The alumina workpieces used in this work were pressed and sintered into rectangular bars. Acoustic emission and cutting power signals were collected during the tests and digitally processed to calculate the mean, standard deviation, and two other statistical data. These statistics, as well the root mean square of the acoustic emission and cutting power signals were used as input data for ANFIS. The output values of surface roughness (measured during the tests) were implemented for training and validation of the model. The results indicated that an ANFIS network is an excellent tool when applied to predict the surface roughness of ceramic workpieces in the grinding process.