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Author

Eduardo Carlos Bianchi

Other affiliations: University of São Paulo
Bio: Eduardo Carlos Bianchi is an academic researcher from Sao Paulo State University. The author has contributed to research in topics: Grinding & Grinding wheel. The author has an hindex of 28, co-authored 222 publications receiving 2504 citations. Previous affiliations of Eduardo Carlos Bianchi include University of São Paulo.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the minimum quantity lubricant (MQL) technique was used in the machining process of a machined component to remove 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.
Abstract: 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.

214 citations

Journal ArticleDOI
TL;DR: In this paper, the behavior of the minimum quantity lubricant (MQL) machining technique in the grinding process has been evaluated using aluminum oxide and superabrasive CBN (cubic boron nitride) wheels.
Abstract: Energy consumption, air pollution and industrial waste have received special attention from public authorities in recent years. The environment has become one of the most important subjects in the context of modern life, for its deterioration impacts the quality of life populations. Driven by pressure from environmental agencies, politicians have drawn up ever stricter laws aimed at protecting the environment and preserving energy resources. All these factors have led industry, research centers and universities to focus their efforts on researching alternative production processes, creating technologies to minimize or avoid the production of environmentally aggressive residues. Up to a few years ago, the main objective of manufacturing plants was to produce goods aimed at satisfying technological and economic aspects. Green, or "dry" machining and Minimum Quantity Lubricant (MQL) machining have caught the attention of researchers and technicians in the area of machining as an alternative to traditional fluids. Thus, this work proposes to explore the MQL concept in the grinding operation. Although its advantages allow one to predict a growing range of applications for MQL, the variables of influence to be considered and the effects on the results of the process have so far been little studied. Grinding involves several input parameters but, to date, little attention has been focused on the form and quantity of cutting fluid applied to the process. The condition and rate of cutting fluid applied directly influences some of the process's output variables. This work, therefore, analyzes the behavior of the MQL technique under different lubrication and cooling conditions, developing an optimized fluid application methodology based on the creation of a special nozzle through which a minimum amount of oil is pulverized in a compressed air stream. The evaluation of the technical performance of MQL in grinding, using aluminum oxide and superabrasive CBN (cubic boron nitride) grinding wheels, consisted of an experimental analysis of the behavior of the tangential cutting force, G ratio, roughness and residual stress. The results presented herein allowed us to evaluate the behavior of the MQL technique in the grinding process, thus contributing toward an environmentally friendly technology.

111 citations

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TL;DR: In this article, the application of minimum quantity lubrication (MQL) in grinding has emerged as an alternative for reducing the abundant flow of cutting fluids, thus achieving cleaner production.

93 citations

Journal ArticleDOI
TL;DR: A method to characterize the dresser wear condition from acoustic emission (AE) signal is described and some neural network models are proposed that produced very good results and can ensure the ground part will be within project specifications.
Abstract: Identification and online monitoring of the dresser wear are necessary to guarantee a desired wheel surface and improve the effectiveness of grinding process to a satisfactory level. However, tool wear is a complex phenomenon occurring in several and different ways in cutting processes, and there is a lack of analytical models that can represent the tool condition. On the other hand, neural networks are considered as a good approach to resolve the absence of an analytical or empirical model. This paper describes a method to characterize the dresser wear condition from acoustic emission (AE) signal. To achieve this, some neural network models are proposed. Initially, a study on the frequency content of the raw AE signal was carried out to determine features that correlate the signal and dresser wear. The features of the signal were obtained from the root mean square and ratio of power statistics at nine frequency bands selected from AE spectra. Combinations of two frequency bands were evaluated as inputs to eight neural networks models, which have been compared with their classification ability. It could be verified that the combination of the frequency bands of 28-33 and 42-50 kHz best characterized the dresser wear condition. Some of the models produced very good results and can therefore ensure the ground part will be within project specifications.

82 citations

Journal ArticleDOI
TL;DR: Diamond tool wear was estimated during the grinding of advanced ceramics using intelligent systems composed of four types of neural networks and the results indicate that the models are highly successful in estimating tool wear.
Abstract: Tool condition monitoring in grinding of advanced ceramics using neural networks.Acoustic emission and power signals were used in several statistical parameters.Results showed that the ANN were highly successful in estimating tool wear.Errors was less than 4%.The models will help to improve product quality and increase productivity. Grinding wheel wear, which is a very complex phenomenon, causes changes in most of the shapes and properties of the tool during machining, reducing the efficiency of the grinding operation and impairing workpiece quality. Therefore, monitoring the condition of the tool during the grinding process plays a key role in the quality of workpieces being manufactured. In this study, diamond tool wear was estimated during the grinding of advanced ceramics using intelligent systems composed of four types of neural networks. Experimental tests were performed on a surface grinding machine and tool wear was measured by the imprint method throughout the tests. Acoustic emission and cutting power signals were acquired during the tests and statistics were obtained from these signals. Training and validating algorithms were developed for the intelligent systems in order to automatically obtain the best estimation models. The combination of signals and statistics along with the intelligent systems brings an innovative aspect to the grinding process. The results indicate that the models are highly successful in estimating tool wear.

69 citations


Cited by
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TL;DR: In this article, a hybrid nanofluid consisting of MoS2 nanoparticles with good lubrication effect and CNTs with high heat conductivity coefficient is investigated for Ni-based alloy grinding.
Abstract: A nanofluid minimum quantity lubrication with addition of one kind of nanoparticle has several limitations, such as grinding of difficult-to-cutting materials Hybrid nanoparticles integrate the properties of two or more kinds of nanoparticles, thus having better lubrication and heat transfer performances than single nanoparticle additives However, the use of hybrid nanoparticles in nanofluid minimum quantity lubrication grinding has not been reported This study aims to determine whether hybrid nanoparticles have better lubrication performance than pure nanoparticle A hybrid nanofluid consisting of MoS2 nanoparticles with good lubrication effect and CNTs with high heat conductivity coefficient is investigated The effects of the hybrid nanofluid on grinding force, coefficient of friction, and workpiece surface quality for Ni-based alloy grinding are analyzed Results show that the MoS2/CNT hybrid nanoparticles achieve better lubrication effect than single nanoparticles The optimal MoS2/CNT mixing ratio and nanofluid concentration are 2:1 and 6 wt%, respectively

365 citations

Journal ArticleDOI
TL;DR: In this article, the dispersing mechanism of different surfactants and evaluated the dispersion stability and tribological performances of PPO-based CNT nanofluids were analyzed. And different experimental evaluations confirm that APE-10 is the optimal dispersant of CNT nanoparticles.

353 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present the current state of the art regarding the assumed working mechanisms of MWFs including the effects of desired and undesired changes of the MWF properties.

292 citations

Journal ArticleDOI
TL;DR: In this paper, the authors compared the performances of MQL grinding by using castor oil, soybean oil, rapeseed oil, corn oil, sunflower oil, peanut oil, and palm oil as base oils.

287 citations