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Paulo Roberto de Aguiar
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2020 – today
- 2024
- [j12]Gabriel Augusto David
, Paulo Monteiro De Carvalho Monson
IoT-Driven Deep Learning for Enhanced Industrial Production Forecasting. IEEE Internet Things J. 11(23): 38486-38495 (2024) - [j11]Thiago Glissoi Lopes
fff_segmenter: A signal segmentation script for acoustic FFF fabrication data in MATLAB. J. Open Source Softw. 9(99): 6620 (2024) - [j10]Cristiano Soares Junior
A Proposed Non-Destructive Method Based on Sphere Launching and Piezoelectric Diaphragm. Sensors 24(18): 5874 (2024) - [c8]Thiago Glissoi Lopes
Detection and Classification of Defects in 3D Printing using a Novel Skewness and Kurtosis-based Parameter of Sound Signals and Machine Learning. ICCAD 2024: 1-5 - 2022
- [c7]Cristiano Soares Junior
A Study on a Novel Impact Test for SHM of CFRP Based on Screwdriver Bit Launch and Electret Microphone. SYSINT 2022: 379-386 - 2020
- [j9]Martin Antonio Aulestia Viera
Erratum: Aulestia Viera, M., et al. A Time-Frequency Acoustic Emission-Based Technique to Assess Workpiece Surface Quality in Ceramic Grinding with PZT Transducer. Sensors 2019, 19, 3913. Sensors 20(8): 2387 (2020) - [j8]Fábio Romano Lofrano Dotto
In-Dressing Acoustic Map by Low-Cost Piezoelectric Transducer. IEEE Trans. Ind. Electron. 67(8): 6927-6936 (2020)
2010 – 2019
- 2019
- [j7]Martin Antonio Aulestia Viera
A Time-Frequency Acoustic Emission-Based Technique to Assess Workpiece Surface Quality in Ceramic Grinding with PZT Transducer. Sensors 19(18): 3913 (2019) - [j6]Felipe A. Alexandre
A Novel Ultrasound Technique Based on Piezoelectric Diaphragms Applied to Material Removal Monitoring in the Grinding Process. Sensors 19(18): 3932 (2019) - 2018
- [j5]Pedro Oliveira Junior
Dressing Tool Condition Monitoring through Impedance-Based Sensors: Part 2 - Neural Networks and K-Nearest Neighbor Classifier Approach. Sensors 18(12): 4453 (2018) - [j4]Pedro Oliveira Junior
Dressing Tool Condition Monitoring through Impedance-Based Sensors: Part 1 - PZT Diaphragm Transducer Response and EMI Sensing Technique. Sensors 18(12): 4455 (2018) - 2017
- [j3]Danilo M. S. Ribeiro, Paulo Roberto de Aguiar
Spectra Measurements Using Piezoelectric Diaphragms to Detect Burn in Grinding Process. IEEE Trans. Instrum. Meas. 66(11): 3052-3063 (2017) - 2015
- [j2]Mauricio Eiji Nakai, Paulo Roberto de Aguiar, Hildo Guillardi Jr.
Evaluation of neural models applied to the estimation of tool wear in the grinding of advanced ceramics. Expert Syst. Appl. 42(20): 7026-7035 (2015) - 2014
- [j1]Cesar H. R. Martins, Paulo Roberto de Aguiar, Arminio Frech, Eduardo Carlos Bianchi
Tool Condition Monitoring of Single-Point Dresser Using Acoustic Emission and Neural Networks Models. IEEE Trans. Instrum. Meas. 63(3): 667-679 (2014) - [c6]Rodolpho F. Godoy Neto, Marcelo Marchi, Cesar H. R. Martins, Paulo Roberto de Aguiar, Eduardo Carlos Bianchi
Monitoring of Grinding Burn by AE and Vibration Signals. ICAART (1) 2014: 272-279 - 2013
- [c5]Cesar H. R. Martins, Paulo Roberto de Aguiar, Arminio Frech, Eduardo Carlos Bianchi
Neural Networks Models for Wear Patterns Recognition of Single-Point Dresser. MIM 2013: 1524-1529
2000 – 2009
- 2007
- [c4]Paulo Roberto de Aguiar, C. E. D. Cruz, W. C. F. Paula, Eduardo C. Bianchi, Rogério Thomazella, Fábio Romano Lofrano Dotto:
Neural network approach for surface roughness prediction in surface grinding. Artificial Intelligence and Applications 2007: 111-117 - 2005
- [c3]Paulo Roberto de Aguiar, Eduardo C. Bianchi, Fábio Romano Lofrano Dotto, Rogério Andrade Flauzino, Danilo Hernane Spatti:
Neural Network Applied to Detect Burn in Grinding. Artificial Intelligence and Applications 2005: 831-836 - 2004
- [c2]Paulo Roberto de Aguiar, Paulo José Amaral Serni, Eduardo C. Bianchi, Fábio Romano Lofrano Dotto:
In-process grinding monitoring by acoustic emission. ICASSP (5) 2004: 405-408 - 2002
- [c1]Fábio Romano Lofrano Dotto
In-process thermal damage detection in grinding with monitoring via Internet. ICARCV 2002: 270-275
Coauthor Index
aka: Eduardo C. Bianchi
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