Analysis of the different forms of application and types of cutting fluid used in plunge cylindrical grinding using conventional and superabrasive CBN grinding wheels

The work reported here involved an investigation into the grinding process, one of the last finishing processes carried out on a production line. Although several input parameters are involved in this process, attention today focuses strongly on the form and amount of cutting fluid employed, since these substances may be seriously pernicious to human health and to the environment, and involve high purchasing and maintenance costs when utilized and stored incorrectly. The type and amount of cutting fluid used directly affect some of the main output variables of the grinding process which are analyzed here, such as tangential cutting force, specific grinding energy, acoustic emission, diametrical wear, roughness, residual stress and scanning electron microscopy. To analyze the influence of these variables, an optimised fluid application methodology was developed (involving rounded 5, 4 and 3 turn diameter nozzles and high fluid application pressures) to reduce the amount of fluid used in the grinding process and improve its performance in comparison with the conventional fluid application method (of diffuser nozzles and lower fluid application pressure). To this end, two types of cutting fluid (a 5% synthetic emulsion and neat oil) and two abrasive tools (an aluminium oxide and a superabrasive CBN grinding wheel) were used. The results revealed that, in every situation, the optimised application of cutting fluid significantly improved the efficiency of the process, particularly the combined use of neat oil and CBN grinding wheel. (c) 2005 Elsevier Ltd. All rights reserved.

Analysis of surface integrity for minimum quantity lubricant - MQL in grinding

The quality of machined components is currently of high interest, for the market demands mechanical components of increasingly high performance, not only from the standpoint of functionality but also from that of safety. Components produced through operations involving the removal of material display surface irregularities resulting not only from the action of the tool itself, but also from other factors that contribute to their superficial texture. This texture can exert a decisive influence on the application and performance of the machined component. This article analyzes the behavior of the minimum quantity lubricant (MQL) technique and compares it with the conventional cooling method. To this end, an optimized fluid application method was devised using a specially designed nozzle, by the authors, through which a minimum amount of oil is sprayed in a compressed air flow, thus meeting environmental requirements. This paper, therefore, explores and discusses the concept of the MQL in the grinding process. The performance of the MQL technique in the grinding process was evaluated based on an analysis of the surface integrity (roughness, residual stress, microstructure and microhardness). The results presented here are expected to lead to technological and ecological gains in the grinding process using MQL. (c) 2006 Elsevier Ltd. All rights reserved.

Influence of low and high levels of grain defects on maize wet milling

Yields and starch pasting characteristics obtained from wet milling of maize samples with low and high levels of defect grains were compared to those from sound samples. Defect grain groups ere established taking into account the defect degree. Thus the first group consisted of fermented, molded, heated and sprouted grains and the second of insect damaged. hollow, fermented (up to 1/4) grains and those injured by other causes. The grain groups, if present at low levels in the samples, 10% for first group and 17% for second group did not affect the chemical composition of starch and its pasting properties. obtained by the rapid visco analyser. Samples with high levels of grain groups (up to 100%). affected wet milling yields and starch viscosity. Samples with 100% of grains in the first group decreased starch, germ yield and peak viscosity and increased gluten yield. Samples with 100% of grains in the second group decreased germ and fiber yield but increased starch yield. (C) 2002 Elsevier B.V. Ltd. All rights reserved.

The minimum quantity of lubricant (MQL) technique in the surface grinding of steel

This research aimed to analyze the viability of the minimum quantity of lubricant (MQL) technique towards different methods of lubri-refrigeration in surface grinding of steel, considering process quality, wheel life and the viability of using cutting fluids The proposal methods were the conventional (abundant fluid flow), the minimum quantity lubrication (MQL) and the optimized method with Webster nozzle (rounded) This analysis was carried out in equal machining conditions, through the assessment of variables such as grinding force, surface roughness, G ratio (volume of removed material/volume of wheel wear), and microhardness The results showed the possibility of improvement of the grinding process Besides, there is the opportunity for production of high quality workpieces with lower costs The MQL technique showed efficiency in machining with lower depths of cut The optimized method with Webster nozzle applies the fluid in a rational way, without considerable waste Hence, the results show that industry can rationalize and optimize the application of cutting fluids, avoiding inappropriate disposal, inadequate use and consequently environment pollution

The behaviour of resin-bond diamond wheels in the grinding of advanced ceramics

Results are reported of the behaviour of the plane tangential grinding process using diamond grinding wheels. Grinding performance is analysed in terms of the wear behaviour of the wheel in the grinding of ceramic. Discussion of the results concentrates on the wear mechanism of the diamond wheel and the process of material removal.

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.

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.

Analysis of diametrical wear of grinding wheel and roundness errors in the machining of steel VC 131

Due to the high industrial competitiveness, the rigorous laws of environmental protection, the necessary reduction of costs, the mechanical industry sees itself forced to worry more and more with the refinement of your processes and products. In this context, can be mentioned the need to eliminate the roundness errors that appear after the grinding process. This work has the objective of verifying if optimized nozzles for the application of cutting fluid in the grinding process can minimize the formation of the roundness errors and the diametrical wear of grinding wheel in the machining of the steel VC 131 with 60 HRc, when compared to the conventional nozzles. These nozzles were analyzed using two types of grinding wheels and two different cutting fluids. Was verified that the nozzle of 3mm of diameter, integral oil and the CBN grinding wheel, were the best options to obtain smaller roundness errors and the lowest diametrical wears of grinding wheels.

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.

Automatic system for thermal damage detection in manufacturing process with Internet monitoring

This work involved the development of a smart system dedicated to surface burning detection in the grinding process through constant monitoring of the process by acoustic emission and electrical power signals. A program in Visual Basic® for Windows® was developed, which collects the signals through an analog-digital converter and further processes them using burning detection algorithms already known. Three other parameters are proposed here and a comparative study carried out. When burning occurs, the newly developed software program sends a control signal warning the operator or interrupting the process, and delivers process information via the Internet. Parallel to this, the user can also interfere in the process via Internet, changing parameters and/or monitoring the grinding process. The findings of a comparative study of the various parameters are also discussed here. Copyright © 2006 by ABCM.

Methodology for automatic selection of passes in surface grinding

The main purpose of this work is the development of computational tools in order to assist the on-line automatic detection of burn in the surface grinding process. Most of the parameters currently employed in the burning recognition (DPO, FKS, DPKS, DIFP, among others) do not incorporate routines for automatic selection of the grinding passes, therefore, requiring the user's interference for the choice of the active region. Several methods were employed in the passes extraction; however, those with the best results are presented in this article. Tests carried out in a surface-grinding machine have shown the success of the algorithms developed for pass extraction. Copyright © 2007 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.

Grinding of hardened steels using optimized cooling

Grinding - the final machining process of a workpiece - requires large amounts of cutting fluids for the lubrication, cooling and removal of chips. These fluids are highly aggressive to the environment. With the technological advances of recent years, the worldwide trend is to produce increasingly sophisticated components with very strict geometric and dimensional tolerances, good surface finish, at low costs, and particularly without damaging the environment. The latter requirement can be achieved by recycling cutting fluids, which is a costly solution, or by drastically reducing the amount of cutting fluids employed in the grinding process. This alternative was investigated here by varying the plunge velocity in the plunge cylindrical grinding of ABNT D6 steel, rationalizing the application of two cutting fluids and using a superabrasive CBN (cubic boron nitride) grinding wheel with vitrified binder to evaluate the output parameters of tangential cutting force, acoustic emission, roughness, roundness, tool wear, residual stress and surface integrity, using scanning electron microscopy (SEM) to examine the test specimens. The performance of the cutting fluid, grinding wheel and plunge velocity were analyzed to identify the best machining conditions which allowed for a reduction of the cutting fluid volume, reducing the machining time without impairing the geometric and dimensional parameters, and the surface finish and integrity of the machined components.

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.

Grinding of AISI 4340 steel with interrupted cutting by aluminum oxide grinding wheel

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