Eduardo Carlos Bianchi

Prof. Eduardo Carlos Bianchi


Research Focus & Metrics

Academic Career Length

25Years (2002-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 June 2005 in Journal of the Brazilian Society of Mechanical Sciences and Engineering
Verified
Journal Article
Open access
PDF file
LSL. R. Silva
L. R. Silva
EBEduardo Carlos Bianchi
Eduardo Carlos Bianchi
RCR. E. Catai
R. E. Catai
RFR. Y. Fusse
R. Y. Fusse
TFThiago França
Thiago França
PAPaulo Roberto Aguiar
Paulo Roberto Aguiar
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

28 April 2019 in The International Journal of Advanced Manufacturing Technology
Verified
Journal Article
Closed access
PDF file
RIRafael Liberatti Iavarone
Rafael Liberatti Iavarone
JLJ. C. Lopes
J. C. Lopes
BSB. K. Sato
B. K. Sato
LSL. E. A. Sanchez
L. E. A. Sanchez
HMH. J. Mello
H. J. Mello
EBEduardo Carlos Bianchi
Eduardo Carlos Bianchi

1 December 2017 in The International Journal of Advanced Manufacturing Technology
Verified
Journal Article
Closed access
PDF file
EBEduardo Carlos Bianchi
Eduardo Carlos Bianchi
RRRafael Lemes Rodriguez
Rafael Lemes Rodriguez
RHRodolfo Alexandre Hildebrandt
Rodolfo Alexandre Hildebrandt
JLJosé Claudio Lopes
José Claudio Lopes
HMHamilton José de Mello
Hamilton José de Mello
PAPaulo Roberto de Aguiar
Paulo Roberto de Aguiar

21 May 2014 in Journal of the Brazilian Society of Mechanical Sciences and Engineering
Verified
Journal Article
Open access
PDF file
DMD. F. G. Moia
D. F. G. Moia
ITI. H. Thomazella
I. H. Thomazella
PAPaulo Roberto Aguiar
Paulo Roberto Aguiar
EBEduardo Carlos Bianchi
Eduardo Carlos Bianchi
CMC. H. R. Martins
C. H. R. Martins
MMMarcelo Marchi
Marcelo Marchi
The grinding operation gives workpieces their final finish, minimizing surface roughness through the interaction between the abrasive grains of a tool (grinding wheel) and the workpiece. However, excessive grinding wheel wear due to friction renders the tool unsuitable for further use, thus requiring the dressing operation to remove and/or sharpen the cutting edges of the worn grains to render them reusable. The purpose of this study was to monitor the dressing operation using the acoustic emission (AE) signal and statistics derived from this signal, classifying the grinding wheel as sharp or dull by means of artificial neural networks. An aluminum oxide wheel installed on a surface grinding machine, a signal acquisition system, and a single-point dresser were used in the experiments. Tests were performed varying overlap ratios and dressing depths. The root mean square values and two additional statistics were calculated based on the raw AE data. A multilayer perceptron neural network was used with the Levenberg–Marquardt learning algorithm, whose inputs were the aforementioned statistics. The results indicate that this method was successful in classifying the conditions of the grinding wheel in the dressing process, identifying the tool as “sharp” (with cutting capacity) or “dull” (with loss of cutting capacity), thus reducing the time and cost of the operation and minimizing excessive removal of abrasive material from the grinding wheel.

30 November 2017 in The International Journal of Advanced Manufacturing Technology
Verified
Journal Article
Closed access
PDF file
EBEduardo Carlos Bianchi
Eduardo Carlos Bianchi
BSBruno Kenta Sato
Bruno Kenta Sato
ASAlan Rodrigo Sales
Alan Rodrigo Sales
JLJosé Claudio Lopes
José Claudio Lopes
HMHamilton José de Mello
Hamilton José de Mello
PAPaulo Roberto Aguiar
Paulo Roberto Aguiar

14 November 2019 in The International Journal of Advanced Manufacturing Technology
Verified
Journal Article
Closed access
PDF file
JLJosé Claudio Lopes
José Claudio Lopes
MGMateus Vinicius Garcia
Mateus Vinicius Garcia
MVMatheus Valentim
Matheus Valentim
RIRafael Liberatti Iavarone
Rafael Liberatti Iavarone
FRFernando Sabino Fonteque Ribeiro
Fernando Sabino Fonteque Ribeiro
EBEduardo Carlos Bianchi
Eduardo Carlos Bianchi