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.

Structural damage detection by fuzzy clustering

The development of strategies for structural health monitoring (SHM) has become increasingly important because of the necessity of preventing undesirable damage. This paper describes an approach to this problem using vibration data. It involves a three-stage process: reduction of the time-series data using principle component analysis (PCA), the development of a data-based model using an auto-regressive moving average (ARMA) model using data from an undamaged structure, and the classification of whether or not the structure is damaged using a fuzzy clustering approach. The approach is applied to data from a benchmark structure from Los Alamos National Laboratory, USA. Two fuzzy clustering algorithms are compared: fuzzy c-means (FCM) and Gustafson-Kessel (GK) algorithms. It is shown that while both fuzzy clustering algorithms are effective, the GK algorithm marginally outperforms the FCM algorithm. (C) 2008 Elsevier Ltd. All rights reserved.

Pyroelectric composite film for X-ray intensity detection

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

Neurochemistry of the afferents to the rat cochlear root nucleus: Possible synaptic modulation of the acoustic startle

Afferents to the primary startle circuit are essential for the elicitation and modulation of the acoustic startle reflex (ASR). In the rat, cochlear root neurons (CRNs) comprise the first component of the acoustic startle circuit and play a crucial role in mediating the ASR. Nevertheless, the neurochemical pattern of their afferents remains unclear. To determine the distribution of excitatory and inhibitory inputs, we used confocal microscopy to analyze the immunostaining for vesicular glutamate and GABA transporter proteins (VGLUT1 and VGAT) on retrogradely labeled CRNs. We also used reverse transcription-polymerase chain reaction (RT-PCR) and immunohistochemistry to detect and localize specific neurotransmitter receptor subunits in the cochlear root. Our results show differential distributions of VGLUT1- and VGAT-immunoreactive endings around cell bodies and dendrites. The RT-PCR data showed a positive band for several ionotropic glutamate receptor subunits, M1-M5 muscarinic receptor subtypes, the glycine receptor alpha 1 subunit (GlyR alpha 1), GABA(A), GABA(B), and subunits of alpha 2 and beta-noradrenergic receptors. By immunohistochemistry, we confirmed that CRN cell bodies exhibit positive immunoreaction for the glutamate receptor (GluR) 3 and NR1 GluR subunits. Cell bodies and dendrites were also positive for M2 and M4, and GlyR alpha 1. Other subunits, such as GluR1 and GluR4 of the AMPA GluRs, were observed in glial cells neighboring unlabeled CRN cell bodies. We further confirmed the existence of nor-adrenergic afferents onto CRNs from the locus coeruleus by combining tyrosine hydroxylase immunohistochemistry and tract-tracing experiments. Our results provide valuable information toward understanding how CRNs might integrate excitatory and inhibitory inputs, and hence how they could elicit and modulate the ASR. (C) 2008 IBRO. Published by Elsevier Ltd. All rights reserved.

Detection of phenolic compounds using impedance spectroscopy measurements

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

Spatial pattern detection modeling of thrips (Thrips tabaci) on onion fields

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Well-conditioned robust observer for detection of family systems components

A new concept of fault detection and isolation using robust observation for systems with random noises is presented. The method selects the parameters from components that may fault during the process and constructs well conditioned robust observers, considering sensors faults. To isolate component failures via robust observation, a bank of detection observers is constructed, where each observer is only sensitive to one specified component failure while robust to all other component failures.

DETECTION OF CELL-PROLIFERATION IN TISSUE-SECTIONS

1. Cell proliferation is of interest since abnormal cell proliferation appears to be a precursor of tumorigenesis and also because the quantitative description of cell proliferation in tumors can be used to predict the biological behavior of a particular neoplasia.2. Them am several reliable methods of studying cell proliferation in tissues. One of the most important is the detection of the Ki67 defined antigen in frozen sections. The number of cells expressing Ki67 correlates with histological grades of tumors and can also be predictive of clinical outcome. The Ki67 can be localized in tissue sections using monoclonal antibodies in association with the immunoperoxidase technique.3. Proliferating cell nuclear antigen (PCNA) is a component of DNA polymerase-delta and is another important cell proliferation marker manifesting a striking increase in concentration during the S phase of the cell cycle. 19A2 and PC10 are two different monoclonal antibodies which can be employed to detect PCNA in paraffin-embedded tissues.4. Molecular biology has also been making a great contribution to the study of cell proliferation. The most recent innovation in tissue identification of proliferating cells is the use of in situ hybridization for the localization of histone H3 and/or H4 mRNA. H3 mRNA-positive cells appear to be present in basal cells of the skin and in crypt cells of the intestine which are sites with high proliferation rate.

Well-conditioned robust observer for detection of faulty systems components

A new concept of fault detection and isolation using robust observation for systems with random noises is presented. The method selects the parameters from components that may fault during the process and constructs well conditioned robust observers, considering sensors faults. To isolate component failures via robust observation, a bank of detection observers is constructed, where each observer is only sensitive to one specified component failure while robust to all other component failures.

Lead zirconate titanate/polyurethane (PZT/PU) composite for acoustic emission sensors

Piezoelectric composite, made from ferroelectric ceramic lead zirconate titanate (PZT) and vegetable based polyurethane (PU) polymer, was doped with a semiconductor filler, graphite. The resulting composite (PZT/C/PU) with 49/1/50- vol. % composition could be poled at lower field and shorter time due to the increased conductivity of the polymer phase following the introduction of graphite. The PZT/C/PU composite showed higher pyroelectric coefficient in comparison with the undoped PZT/PU composite with 50/50-vol. % composition. Also, the PZT/C/PU composite has shown the ability to detect both extensional and flexural modes of simulated acoustic emission (AE) at a distance up to 8.0 m from the source, thus indicating that it may be used for detection of structural damages.

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.

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.

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.

Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition

Structural health monitoring (SHM) is related to the ability of monitoring the state and deciding the level of damage or deterioration within aerospace, civil and mechanical systems. In this sense, this paper deals with the application of a two-step auto-regressive and auto-regressive with exogenous inputs (AR-ARX) model for linear prediction of damage diagnosis in structural systems. This damage detection algorithm is based on the. monitoring of residual error as damage-sensitive indexes, obtained through vibration response measurements. In complex structures there are. many positions under observation and a large amount of data to be handed, making difficult the visualization of the signals. This paper also investigates data compression by using principal component analysis. In order to establish a threshold value, a fuzzy c-means clustering is taken to quantify the damage-sensitive index in an unsupervised learning mode. Tests are made in a benchmark problem, as proposed by IASC-ASCE with different damage patterns. The diagnosis that was obtained showed high correlation with the actual integrity state of the structure. Copyright © 2007 by ABCM.