The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
Suvankar Das - One of the best experts on this subject based on the ideXlab platform.
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l proline catalyzed multicomponent one pot synthesis of gem diheteroarylmethane derivatives using facile Grinding Operation under solvent free conditions at room temperature
ChemInform, 2014Co-Authors: Goutam Brahmachari, Suvankar DasAbstract:The advantages are mild reaction condition, high atom-economy, good yields, and eco-friendliness.
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l proline catalyzed multicomponent one pot synthesis of gem diheteroarylmethane derivatives using facile Grinding Operation under solvent free conditions at room temperature
RSC Advances, 2014Co-Authors: Goutam Brahmachari, Suvankar DasAbstract:An efficient and straightforward L-proline catalyzed one-pot synthesis of a series of biologically relevant gem-(β-dicarbonyl)arylmethanes has been developed via a three-component reaction between indoles, aldehydes and C–H activated acids by Grinding them together under solvent-free conditions at room temperature. Mild reaction conditions, high atom-economy, good yields, and eco-friendliness are some of the salient features of the present protocol.
Goutam Brahmachari - One of the best experts on this subject based on the ideXlab platform.
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l proline catalyzed multicomponent one pot synthesis of gem diheteroarylmethane derivatives using facile Grinding Operation under solvent free conditions at room temperature
ChemInform, 2014Co-Authors: Goutam Brahmachari, Suvankar DasAbstract:The advantages are mild reaction condition, high atom-economy, good yields, and eco-friendliness.
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l proline catalyzed multicomponent one pot synthesis of gem diheteroarylmethane derivatives using facile Grinding Operation under solvent free conditions at room temperature
RSC Advances, 2014Co-Authors: Goutam Brahmachari, Suvankar DasAbstract:An efficient and straightforward L-proline catalyzed one-pot synthesis of a series of biologically relevant gem-(β-dicarbonyl)arylmethanes has been developed via a three-component reaction between indoles, aldehydes and C–H activated acids by Grinding them together under solvent-free conditions at room temperature. Mild reaction conditions, high atom-economy, good yields, and eco-friendliness are some of the salient features of the present protocol.
Eduardo Carlos Bianchi - One of the best experts on this subject based on the ideXlab platform.
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low cost piezoelectric transducer for ceramic Grinding monitoring
IEEE Sensors Journal, 2019Co-Authors: Martin Antonio Aulestia Viera, Paulo Roberto De Aguiar, Felipe Aparecido Alexandre, Rosemar Batista Da Silva, Mark J. Jackson, Pedro Oliveira, Eduardo Carlos BianchiAbstract:This paper presents a performance assessment of the low-cost thin-disk piezoelectric diaphragm (PZT) for the surface integrity monitoring in the Grinding Operation. PZT diaphragms are flexible, low-cost, and smart devices, making them promising for applications in manufacturing fields, such as in the Grinding Operation. To accomplish this, experimental Grinding tests were carried out on workpieces of ceramic material covering from slight to severe machining conditions. A low-cost thin-disk PZT diaphragm was assessed, which was attached to the workpiece holder using a cyanoacrylate glue. In addition, a commercial and consolidated acoustic emission (AE) sensor was used as a baseline for comparative purpose. Likewise, the surface roughness measurements were performed after Grinding each workpiece to support the sensor monitoring system. Feature extraction using frequency spectrum and time domain analysis were applied to the sensor signals by means of fast Fourier transform (FFT), digital bandpass filters, root mean square (RMS) value, and power deviation (DPO) metric. The results indicate a clear relationship between the thin-disk PZT diaphragm and the conventional AE sensor for surface integrity monitoring in Grinding, which proves the effectiveness and reliability of an innovative sensing device for the present application.
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a contribution to the monitoring of ceramic surface quality using a low cost piezoelectric transducer in the Grinding Operation
2018Co-Authors: Martin Antonio Aulestia Viera, Felipe Aparecido Alexandre, Rosemar Batista Da Silva, Wenderson Nascimento Lopes, Paulo Aguiar, Doriana M Daddona, Andre Luiz Andreoli, Eduardo Carlos BianchiAbstract:The Grinding process is usually one of the last stages in the manufacturing process chain since it can provide superior surface finish and closer dimensional tolerances. However, due to peculiarities of the Grinding process, workpiece material is susceptible to many problems, what demands a reliable real-time monitoring system. Some Grinding monitoring systems have been proposed by means of sensors. However, literature is still scarce in terms of employing time-frequency analysis techniques during Grinding of ceramics. Thus, this paper proposes an application of the low-cost piezoelectric transducer (PZT - lead zirconate titanate) in the analysis of the surface quality of ceramic workpieces during Grinding process by means of frequency-time domain technique along with the ratio of power parameter (ROP). An integrated high-cost commonly used acoustic emission (AE) sensor was employed in order to compare the results with the low-cost PZT transducer. Tests were performed using a peripheral surface Grinding machine. Three values of depth of cut were selected in order to represent slight, moderate and severe Grinding conditions. Piezoelectric signals were collected at 2 MHz. The short-time Fourier transform (STFT) was studied in order to obtain the frequency variations over time. An analysis of the ratio of power (ROP) values was performed in order to establish a correlation with the surface roughness. The ROP values are highly desirable for setting a threshold to detect the workpiece surface quality and implementing into a monitoring system. The results of the PZT transducer had a great similarity to those of the AE sensor.
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Electromechanical impedance (EMI) technique as alternative to monitor workpiece surface damages after the Grinding Operation
The International Journal of Advanced Manufacturing Technology, 2018Co-Authors: Rosemar Batista Da Silva, Paulo Roberto De Aguiar, Rodrigo De Souza Ruzzi, Fabio Isaac Ferreira, Fabrício Guimares Baptista, Henrique Butzlaff Hübner, Maria Cindra Fonseca, Eduardo Carlos BianchiAbstract:Electromechanical impedance (EMI) technique has been employed in detection of structural failure in civil and mechanical structures because of its non-destructive property and easy implementation of small and inexpensive piezoelectric transducers that are attached to the structures, which lead to cost reduction as well as lesser dependence of manual inspection methods. In this technique, the capsule is excited by applying a sinusoidal voltage to generate waves to propagate throughout the structure. From the impedance signature of the structure without any damage, any structural change can be detected by measuring the electrical impedance of the piezoelectric (PZT) patch. Based on its real potentiality and because of its non-destructive characteristics, this work aimed to employ the EMI technique as the first alternative to monitor workpiece surface damages after Grinding Operation with a conventional abrasive Grinding wheel. EMI measurements were performed by using a low-cost PZT transducer and under controlled environmental conditions. Microhardness and surface roughness of the machined surfaces, as well as Grinding power, were also measured to detect any damage in the machined surface and to stablish relationship with the EMI technique. From the damage indices root mean square deviation (RMSD) and correlation coefficient deviation metric (CCDM), surface alterations on the ground surfaces were inferred by the EMI method. Also, it was observed a good correlation between the EMI technique and the other output parameters that were investigated in this work, such as surface roughness and power Grinding, thereby posing as a non-destructive, low-cost, and viable technique to monitor workpiece surface damages in the Grinding Operation.
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Prediction of Dressing in Grinding Operation via Neural Networks
Procedia CIRP, 2017Co-Authors: Doriana M. D’addona, Eduardo Carlos Bianchi, Paulo Roberto De Aguiar, D. Matarazzo, Roberto Teti, Arcangelo FornaroAbstract:Abstract In order to obtain a modelling and prediction of tool wear in Grinding Operations, a Cognitive System has been employed to observe the dressing need and its trend. This paper aims to find a methodology to characterize the condition of the wheel during Grinding Operations and, by the use of cognitive paradigms, to understand the need of dressing. The Acoustic Emission signal from the Grinding Operation has been employed to characterize the wheel condition and, by the feature extraction of such signal, a cognitive system, based on Artificial Neural Networks, has been implemented.
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Analysis of the influence of sparkout time on Grinding using several lubrication/cooling methods
Journal of The Brazilian Society of Mechanical Sciences and Engineering, 2009Co-Authors: Jose Alves, Anselmo Eduardo Diniz, Ulysses De Barros Fernandes, Eduardo Carlos Bianchi, Paulo Roberto De Aguiar, Rubens Chinali CanarimAbstract:The plunge cylindrical Grinding Operation has been widely employed in the manufacturing process of components which require excellent surface quality achieved within small ranges of dimensional tolerance. The sparkout time has proved to be an important parameter in this Operation, contributing to obtain surfaces with high geometric and dimensional precision. This parameter, which is defined as the period in which there is no wheel radial feed, allows the elimination of elastic deformations that build up as the Grinding wheel is fed. Experimentation with sparkout time was applied in the plunge cylindrical Grinding Operation and included the Minimum Quantity Lubrication (MQL)technique, which has proved to be an environmentally correct alternative, combining a small amount of lubricating oil with an intense flow rate of compressed air. The conventional lubrication and cooling method and the method involving the nozzle proposed by Webster [10] were also used. The results showed that longer sparkout times led to a decrease in tangential forces, wheel diametrical wear and surface roughness values for the MQL method.
Kishalay Mitra - One of the best experts on this subject based on the ideXlab platform.
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MODELING OF WET Grinding Operation USING ARTIFICIAL INTELLIGENCE BASED TECHNIQUES
IFAC Proceedings Volumes, 2016Co-Authors: Kishalay Mitra, Mahesh GhivariAbstract:Abstract The Artificial Intelligence (AI) based modeling techniques applied to the industrial Grinding Operation of a lead-zinc ore-beneficiation plant to predict the key performance indicators (KPIs) for the circuit. As system identification of the non-linear process is a must in advanced control, AI based techniques are applied to predict the KPIs within some acceptable limits. The nonparametric model for these KPIs is constructed using Feed-Forward Neural Networks (FNN), and wavelet-frames. A well-validated hybrid-model, using physico-empirical methodologies, is used to approximate the actual behaviour of the plant. Merits and demerits of each of these techniques are presented.
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Multiobjective optimization of an industrial Grinding Operation under uncertainty
Chemical Engineering Science, 2009Co-Authors: Kishalay MitraAbstract:Multiobjective optimization of an industrial Grinding Operation under various parameter uncertainties is carried out in this work. Two sources of uncertainties considered here are related to the (i) parameters that are used inside a model representing the process under consideration and subjected to experimental and regression errors and (ii) parameters that express operators’ choice for assigning bounds in the constraints and operators prefer them to be expressed around some value rather than certain crisp value. Uncertainty propagation of these parameters through nonlinear model equations is reflected in terms of system constraints and objectives that are treated here using chance constrained fuzzy simulation based approach. Such problems are treated in literature using the standard two stage stochastic programming methodology that has a drawback of leading to combinatorial explosion with an increase in the number of uncertain parameters. This problem is overcome here using a combination of fuzzy and chance constrained programming approach that tackles the problem by representing and treating the uncertain parameters in a different manner. Simultaneous maximization of Grinding circuit throughput and percent passing mid size fraction are studied here with upper bound constraints for various performance metrics for the Grinding circuit, e.g. percent passing of fine and coarse size classes, percent solids in the Grinding circuit final outlet stream and circulation load of the Grinding circuit. Uncertain parameters considered are grindability indices of rod mill and ball mill, sharpness indices of primary and secondary cyclones and the respective upper bounds for the constraints mentioned above. The deterministic multiobjective Grinding optimization model of Mitra and Gopinath [2004. Multiobjective optimization of an industrial Grinding Operation using elitist nondominated sorting genetic algorithm. Chem. Eng. Sci. 59, 385–396.] forms the basis of this work on which various effects of uncertain parameters are shown and analyzed in a Pareto fashion. Nondominated sorting genetic algorithm, NSGA II, a popular elitist evolutionary multiobjective optimization approach, is used for this purpose.
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Multiobjective optimization of an industrial Grinding Operation using genetic algorithm
Computer-aided chemical engineering, 2007Co-Authors: Kishalay Mitra, Ravi GopinathAbstract:Abstract The elitist version of nondominated sorting genetic algorithm (NSGA II) has been adapted to optimize the industrial Grinding Operation of a lead-zinc ore beneficiation plant in mineral processing sector. Two objectives functions identified for this study are throughput of the Grinding circuit and percent passing of one of the most critical size fractions (both are maximized). Simultaneously, it is also ensured that the Grinding product meets all other quality requirements by keeping percent passing of two other size classes, percent solid and recirculation load of the Grinding circuit within the user specified bounds (constraints). Solid ore and water flowrates to the circuit are treated as manipulated (decision) variables. Pareto set, for the conflicting objectives, has been generated. NSGA II is found to generate Pareto front significantly dense in terms of spread of optimal points and better in comparison with the Pareto front generated by other weight and constraint based approaches.
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Modeling of an industrial wet Grinding Operation using data-driven techniques
Computers & Chemical Engineering, 2006Co-Authors: Kishalay Mitra, Mahesh GhivariAbstract:Abstract The data-driven modeling techniques have been applied to the industrial Grinding Operation of a lead–zinc ore beneficiation plant to predict the output variables, the key performance indicators (KPIs) for the circuit. Many Grinding plants are not adequately equipped with measuring instruments that are used only to measure some of the output parameters leading to a major hindrance towards modeling the Operation through the route of first principles. To add to this, for controlling the Grinding Operation, system identification of the process is a must and poses a critical problem in advanced control as the Grinding process behavior is highly non-linear. This necessitates applying some advanced data-driven techniques that are capable of predicting the KPIs within some acceptable limits. A total of six important KPIs considered here are throughput, three size fractions (+150 μm, +63 μm, −38 μm), percentage solids and recirculation load. These KPIs are predicted using three manipulated variables, namely, solid ore feed rate, two water feed rates to two sumps. To capture the nonparametric model for these KPIs, the data-driven techniques used here are several versions of neural networks and wavelets. While using neural network topologies, feed-forward neural networks (FNN) and recurrent neural networks (RNN) are utilized whereas for wavelet-based networks, wavelet frames are used. A well-validated hybrid model, a combination of physical and empirical methodologies, is used to approximate the actual behavior of the plant. A set of data, generated from this hybrid model is used for training the above-mentioned networks whereas another exclusive set of data is used to validate the evolved data-driven models. Merits and demerits of each of these techniques are also presented. Implementation of these techniques based on analysis to the actual plant may influence implementation of control and optimization technologies and thereby enhancing the plant performance tremendously where lack of hardware sensors does not allow them to be a right candidate to take part in systematic exercises for plant performance improvement.
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multiobjective optimization of an industrial Grinding Operation using elitist nondominated sorting genetic algorithm
Chemical Engineering Science, 2004Co-Authors: Kishalay Mitra, Ravi GopinathAbstract:The elitist version of nondominated sorting genetic algorithm (NSGA II) has been adapted to optimize the industrial Grinding Operation of a lead-zinc ore beneficiation plant. Two objective functions have been identified in this study: (i) throughput of the Grinding Operation is maximized to maximize productivity and (ii) percent passing of one of the most important size fractions is maximized to ensure smooth flotation Operation following the Grinding circuit. Simultaneously, it is also ensured that the Grinding product meets all other quality requirements, to ensure least possible disturbance in the following flotation circuit, by keeping two other size classes and percent solid of the Grinding product and recirculation load of the Grinding circuit within the user specified bounds (constraints). Three decision variables used in this study are the solid ore flowrate and two water flowrates at two sumps, primary and secondary, each of them present in each of the two stage classification units. Nondominating (equally competitive) optimal solutions (Pareto sets) have been found out due to conflicting requirements between the two objectives without violating any of the constraints considered for this problem. Constraints are handled using a technique based on tournament selection operator of genetic algorithm which makes the process get rid of arbitrary tuning requirement of penalty parameters appearing in the popular penalty function based approaches for handling constraints. One of the Pareto points, along with some more higher level information, can be used as set points for the previously mentioned two objectives for optimal control of the Grinding circuit. Implementation of the proposed technology shows huge industrial benefits.
Sudarsan Ghosh - One of the best experts on this subject based on the ideXlab platform.
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Development of an ultrasonic vibration assisted minimum quantity lubrication system for Ti-6Al-4V Grinding
International Journal of Precision Technology, 2019Co-Authors: Rajeshkumar Madarkar, Sahaj Agarwal, Sudarsan GhoshAbstract:Minimum quantity lubrication (MQL) is widely used in machining/Grinding as a competent cooling-lubrication technique owing to its advantages in terms of better cooling, lubrication, and lower coolant consumption. Ultrasonic vibration can be used to enhance the efficiency of MQL system by atomising the cutting fluid into fine and uniform droplets. In this study, an ultrasonic vibration assisted MQL (UAV-MQL) system is indigenously developed to effectively atomise the cutting fluid using the ultrasonic vibration of a suitably designed horn. To check the effectiveness of the developed UAV-MQL system, a set of experiments have been conducted on Ti-6Al-4V alloy during surface Grinding Operation, and the results have been compared with dry, flood and air-assisted conventional MQL Grinding process using soluble oil as a cutting fluid.
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an investigation into the application of al2o3nanofluid based minimum quantity lubrication technique for Grinding of ti 6al 4v
International Journal of Precision Technology, 2014Co-Authors: Dinesh Setti, Manoj Kumar Sinha, Sudarsan GhoshAbstract:Nanofluids, suspensions of nanoparticles in base fluid, has shown attractive cooling and lubricating properties. The nano–coolants and nano–lubricants find applications in a wide variety of materials processing technologies. It is anticipated that, if properly employed, nanofluids usage could surpass the conventional cutting fluids in the future. Minimum quantity lubrication (MQL) technique also, has achieved a significant consideration in manufacturing processes to minimise the environmental loads caused by the usage of traditional cutting fluids. The aim of this work is to examine the potential of Al2O3 nanofluid under MQL mode to improve the Grinding characteristics of Ti–6Al–4V alloy. 1% Volume concentration of water–based Al2O3 nanofluid was applied during the surface Grinding Operation using an indigenously developed MQL setup and the results have been compared with those of conventional coolant under both flood cooling and MQL mode.
