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Made available in DSpace on 2021-06-25T12:23:37Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-10-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Post Graduate Program of Mechanical Engineering of UFU Post Graduate Program of Mechanical Engineering of UNESP (Bauru campus) ITW Chemical Products Ltda Saint Gobain group One of the peculiarities associated with the grinding process is the large amount of heat generation. Therefore, the cooling lubrication technique plays an important role and must be conducted efficient. Even though the conventional cooling lubrication technique generally outperforms all other techniques, its use is harmful to both the environment and the health of the operator. Thus, techniques that minimize the use of cutting fluids are necessary. Among the various techniques available, minimum quantity of lubrication (MQL) has been widely employed in machining scenarios. However, its use in grinding is questioned because of the poor surface finish generated and thermal damage due to the clogging of the abrasive wheel pores during grinding. To overcome these problems, in this study, an auxiliary cleaning system (CS) of the wheel was used to remove the chips and oil from the clogged wheel surface during grinding, while assessing the CS performance with the aim of making the MQL a technically viable cooling lubrication alternative. Grinding trials were performed on hardened steel under three different cutting conditions using the conventional (flood) cooling lubricant technique and the MQL technique, by applying a biodegradable cutting fluid, with and without the CS. The CS performance was evaluated with regard to the roughness, roundness errors, wheel wear, grinding power, microhardness and residual stresses. Results of both surface roughness and grinding wheel wear were also used to determine empirical equations comparing the traditional MQL technique and the MQL technique with auxiliary cleaning system (MQL + CS). The results showed that MQL assisted with the CS can improve the machined surface integrity, reducing the surface roughness, roundness error and the variation in the microhardness. Furthermore, the MQL + CS condition also presented lower grinding wheel wear. Univ Fed Uberlandia, Sch Mech Engn, Uberlandia, MG, Brazil Inga Univ Ctr UNINGA, Dept Mech Engn, Rod PR-317,6114,Parque Ind 200, BR-87035510 Maringa, Parana, Brazil Sao Paulo State Univ Julio De Mesquita Filho, Dept Mech Engn, Bauru Campus, Bauru, SP, Brazil Univ Minas Gerais, Dept Mech Engn, Av Antonio Carlos 6627, BR-31270901 Belo Horizonte, MG, Brazil Sao Paulo State Univ Julio De Mesquita Filho, Dept Elect Engn Bauru, Bauru Campus, Bauru, SP, Brazil Sao Paulo State Univ Julio De Mesquita Filho, Dept Mech Engn, Bauru Campus, Bauru, SP, Brazil Sao Paulo State Univ Julio De Mesquita Filho, Dept Elect Engn Bauru, Bauru Campus, Bauru, SP, Brazil CAPES: 001

Made available in DSpace on 2019-10-06T17:10:53Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-10-01 The occurrence of chatter can generate parts outside the dimensional and geometric tolerances or even cause irreversible damage, such as changes to the hardness and surface roughness of the ground part. The objective of this study is to propose a new vibration signal processing technique based on the short-time Fourier transform (STFT) and the ratio of power (ROP) statistic for the detection of chatter during the tangential surface grinding of AISI 1045 steel with different grinding wheels. Vibration signals were recorded at 2 MHz in the grinding tests. The Vickers hardness, roughness and metallography of the ground workpiece surfaces were also evaluated. A digital processing technique based on the STFT and ROP was applied to the vibration signals to extract the characteristics of the chatter. The results show that this technique can be used to characterize over time the spectral patterns of a frequency band related to chatter. Electrical Engineering Department São Paulo State University (Universidade Estadual Paulista –UNESP), Av. Eng. Luiz E. C. Coube, 14-01 Mechanical Engineering College Federal University of Uberlândia (Universidade Federal de Uberlândia – UFU), Av. João Naves de Ávila, 2121 Mechanical Engineering Department UNESP, Av. Eng. Luiz E. C. Coube, 14-01 Electrical Engineering Department São Paulo State University (Universidade Estadual Paulista –UNESP), Av. Eng. Luiz E. C. Coube, 14-01 Mechanical Engineering Department UNESP, Av. Eng. Luiz E. C. Coube, 14-01

Grinding is a finish process of parts that require high precision and tight dimensional tolerance, which owe high value-added. As the grinding process takes place, the cutting surface of the grinding wheel undergoes wear and then its cutting capacity is reduced. On the other hand, the dressing operation is responsible for restoring the cutting surface of the grinding wheel and, therefore, plays a key role in the grinding process. This work aims at obtaining acoustic images of the grinding wheel surface to identify its conditions during the dressing operation. Experimental tests were conducted with a single-point diamond dresser in a surface grinding machine, which was equipped with an oxide aluminum grinding wheel in which specific marks were intentionally made on its surface to simulate damages for identification. An acoustic emission sensor was fixed to the dresser holder and the signal were acquired at 5 MHz. The signal spectrum was investigated and a frequency band was carefully selected, which represented the conditions of grinding wheel surface. The root mean square values were then computed from the raw signal with and without filtering for several integration periods, and the acoustic images obtained. The results show that the proposed technique is efficient to identify the damage on the wheel surface during the dressing operation as well as its location.

Made available in DSpace on 2022-05-01T11:07:18Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-01-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Minimal Quantity of Lubrication was developed to achieve green manufacturing in machining process. In this study, parameters of cutting fluid flow, grinding wheel type and wheel cleaning system were evaluated to achieve the maximum potential of this technique in grinding process. The comparison of super abrasive (CBN) and conventional (Al2O3) grinding wheel, the cutting fluid flow (30 ml/h, 60 ml/h and 120 ml/h), and the wheel cleaning system were analyzed based on: surface roughness, roundness errors, grinding wheel wear, confocal of grounded surface, grinding power, acoustic emission and workpiece microscopy. In a novel way, this study compared the costs per piece and the carbon footprint in terms of CO2 emission for both abrasive tool application. For all the parameters analyzed, the CBN grinding with MQL outperformed the Al2O3 grinding, showing a superior performance not only in terms of surface finishing, on average, 29% lower surface roughness and dimensional deviation 9% lower but also regarding tool efficiencies, such as 127% lower diametrical wear, 42% lower acoustic emission, and 13% lower grinding power. Despite that, the cost per piece was also evaluated for the different lubri-cooling conditions and tools, and the cost per piece recorded for Al2O3 grinding was substantially lower than CBN grinding, irrespective of the lubri-cooling method employed. The cost of grinding for all MQL techniques analyzed with aluminum oxide wheel was, on average, 72% less than grinding with CBN wheel. Besides, the conventional method with the CBN wheel was 7% more expensive than the aluminum oxide wheel. Sao Paulo State University – Unesp Department of Mechanical Engineering São Paulo State University “Júlio de Mesquita Filho” Department of Electrical Engineering Imperial College London Department of Mechanical Engineering, Exhibition Rd. Sao Paulo State University – Unesp Department of Mechanical Engineering São Paulo State University “Júlio de Mesquita Filho” Department of Electrical Engineering

This work aims at determining the right moment to stop single-point dressing the grinding wheel in order to optimize the grinding process as a whole. Acoustic emission signals and signal processing tools are used as primary approach. An acoustic emission (AE) sensor was connected to a signal processing module. The AE sensor was attached to the dresser holder, which was specifically built to perform dressing tests. In this work there were three types of test where the edit parameters of each dressing test are: the passes number, the dressing speed, the width of action of the dresser, the dressing time and the sharpness. Artificial Neural Networks (ANNs) technique is employed to classify and predict the best moment for stopping the dressing operation. During the ANNs use, the results from Supervised Neural Networks and Unsupervised Neural Networks are compared.

The emergence of the Internet as a commercialization method has offered organizations many opportunities. However, when buyers want to execute a transaction online, a certain distrust typically accompanies the need to provide personal data. To address this problem, assurance seals have been designed for use on websites. Different guarantee logos certify compliance with buyer protection laws and offer transparency in the commercial process. Thus, assurance seals displayed on the websites of sellers that adhere to a specific trust system allow consumers to choose among entities that have publicly committed to best business practices. We researched 130 sellers in Europe and Latin America that adhere to assurance seals in order to determine the influence of such seals. Our research shows that the use of trust seals increases Internet sales for more than 66% of these companies, improves their corporate image, enhances the number of potential buyers who visit their websites, and generally captures consumers’ attention.

Cognitive modeling of tool wear progress is employed to obtain a dependable trend of tool wear curves for optimal utilization of tool life and productivity improvement, while preserving the surface integrity of the ground parts. This paper describes a method to characterize the dresser wear condition utilizing vibration signals by applying a cognitive paradigm, such as Artificial Neural Networks (ANNs). Dressing tests with a single-point dresser were performed in a surface grinding machine and tool wear measurements taken along the experiments. The results show that ANN processing offers an effective method for the monitoring of grinding wheel wear based on vibration signal analysis.

Made available in DSpace on 2020-12-12T02:35:51Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-07-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Minimum Quantity of Lubricant (MQL) has been used in the grinding process to make the process eco-friendlier. On the other hand, it brings some technical problems to this process: the clogging phenomenon, i.e., chips adherence on the wheel cutting surface. Although this phenomenon yields widely known negative effects on productivity and workpiece quality, few studies exist about clogging in MQL grinding. Thus, this paper aims to explain the clogging formation and evaluate its effects on workpiece finishing and geometrical tolerances. External cylindrical plunge grinding tests in quenched and tempered AISI 4340 steel with CBN wheel, varying lubri-cooling conditions were carried out. Clogging affects the cutting ability of the grinding wheel and impacts on the chip formation. The addition of water in the MQL flow reduced the clogging occurrence in 40%, which corroborated to a reduction of 51% the surface roughness and 58% the roundness error. Sao Paulo State University – Unesp Department of Mechanical Engineering University of Campinas – UNICAMP School of Mechanical Engineering University of Sao Paulo – USP Department of Mechanical Engineering Sao Paulo State University – Unesp Department of Mechanical Engineering FAPESP: 2015-09868-9

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.

Grinding causes progressive dulling and glazing of the grinding wheel grains and clogging of the voids on the wheel's surface with ground metal dust particles, which gradually increases the grinding forces. The condition of the grains at the periphery of a grinding wheel strongly influences the damage induced in a ground workpiece. Therefore, truing and dressing must be carried out frequently. Dressing is the process of conditioning the grinding wheel surface to reshape the wheel when it has lost its original shape through wear, giving the tool its original condition of efficiency. Despite the very broad range of dressing tools available today, the single-point diamond dresser is still the most widely used dressing tool due to its great versatility. The aim of this work is to predict the wear level of the single-point dresser based on acoustic emission and vibration signals used as input variables for fuzzy models. Experimental tests were performed with synthetic diamond dressers on a surface-grinding machine equipped with an aluminum oxide grinding wheel. Acoustic emission and vibration sensors were attached to the tool holder and the signals were captured at 2 MHz. During the tests, the wear of the diamond tip was measured every 20 passes using a microscope with 10 to 100 X magnification. A study was conducted of the frequency content of the signals, choosing the frequency bands that best correlate with the diamond's wear. Digital band-pass filters were applied to the raw signals, after which two statistics were calculated to serve as the inputs for the fuzzy models. The results indicate that the fuzzy models using the aforementioned signal statistics are highly effective for predicting the wear level of the dresser.

Grinding is a workpiece finishing process for advanced products and surfaces. However, the constant friction between workpiece and grinding wheel causes the latter to lose its sharpness, thereby impairing the result of the grinding process. When this occurs, the dressing process is essential to sharpen the worn grains of the grinding wheel. The dressing conditions strongly influence the performance of the grinding operation; hence, monitoring them throughout the process can increase its efficiency. The purpose of this study was to classify the wear condition of a single-point dresser using intelligent systems whose inputs were obtained by digitally processing acoustic emission signals. Two multilayer perceptron (MLP) neural networks were compared for their classification ability, one using the root mean square (RMS) statistics and another the ratio of power (ROP) statistics as input. In this study, it was found that the harmonic content of the acoustic emission signal is influenced by the condition of the dresser, and that the condition of the tool under study can be classified by using the aforementioned statistics to feed a neural network.

The control of industrial manufacturing processes is of great economic importance due to the ongoing search to reduce raw materials and labor wastage. Indirect manufacturing operations such as dimensional quality control generate indirect costs that can be avoided or reduced through the use of control systems. The use of intelligent manufacturing systems, which is the next step in the monitoring of manufacturing processes, has been researched through the application of artificial neural networks in the last two decades. In this work, artificial intelligence systems were trained to estimate the diameter of holes in precision drilling processes. The methodology involved the use of an acoustic emission sensor, a three-dimensional dynamometer, an accelerometer, and a Hall effect sensor to monitor the drilling process. The method was applied to test specimens composed of packages of Ti6Al4 V titanium alloy and 2024-T3 aluminum alloy sheets, which are widely employed in the aerospace industry. The collected signals were processed and the data were organized and fed into artificial intelligence systems, which consisted of an artificial multilayer perceptron (MLP) neural network and the adaptive neuro-fuzzy inference system (ANFIS). The results indicated that the MLP network was the most efficient of the two artificial intelligence techniques. The results also demonstrated a strong potential for the industrial application of the models.

A two-stage study compared the BAX system PCR method with the reference culture method used by the Brazilian Ministry of Agriculture and Food Supply for the detection of Salmonella in food, water, and environmental samples. In stage 1, fish matrix samples (n = 258) were spiked at several levels with Salmonella and a combination of Salmonella and non-Salmonella competitive organisms. Replicates were analyzed by the BAX system PCR method and the reference method with comparable results (sensitivityor = 97.5%, specificityor = 83.3%) from both methods at the limit of detection. In stage 2, a total of 1,988 samples with 70 product types were analyzed with both methods. Five laboratories were involved in this study, and the samples used were from routine analyses. The BAX system PCR method was shown to be comparable to the reference method, with a limit of detection of 1.0 to 2.0 CFU/25 g of sample. Analysis of the results obtained in stage 2 and in the combination of stages 1 and 2 for the BAX system showed the following performance: sensitivityor = 99.0%, specificityor = 97.2%, false-negative rateor = 1.1%, and false-positive rateor = 2.8%. Therefore, the BAX system appears to be equivalent to the reference method, withor = 97.3% agreement.

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.

Resumen Objetivos Revision de la presencia de microdeleciones del cromosoma Y y su relacion clinica e histopatologica. Metodos Se estudio un total de 237 hombres infertiles con azoospermia (117) y oligoastenozoospermia grave (120). Se realizo una completa historia clinica y examen fisico, estudio endocrinologico y genetico y se comprobo la existencia de microdeleciones del cromosoma Y. A los pacientes azoospermicos se les realizo una biopsia testicular. Resultados Solo se consiguio demostrar diferencias significativas en la relacion entre azoospermia y delecion en la zona de factor azoospermico (AZF, por sus siglas en ingles), pero no en la relacion entre delecion AZF y tipo histologico que presenta.

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 mm 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.

This article discusses the development of a test bench and a methodology for the study of composite resin abrasive wear. To evaluate the operation of the test bench and to compare the proposed methodology with other existing ones, a study was made of the five composites most commonly used by dentists. The one-way ANOVA method and the Tukey test were used to statistically analyze the results by multiple comparisons of the groups of resins tested. Using the proposed methodology, these resins were classified in an increasing order of abrasive wear strength, as follows: Charisma (the lowest abrasive wear strength), Tetric, TPH, Herculite and Z-100 (the highest abrasive wear strength) ( P< 0.05). In comparison to other methodologies, the results of the proposed methodology presented the lowest coefficient of variation. © 2002 Elsevier Science B.V. All rights reserved.