551 resultados para Grinding


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Marked ball grinding tests were carried out in the laboratory with a lead-zinc sulphide ore under different experimental conditions using high carbon low alloy steel (cast and forged) and high chrome cast iron balls. Relative ball wear as a function of grinding period and milling conditions was evaluated for the different types of ball materials. The role of corrosion and abrasion-erosion in the wear of grinding media is brought out. Methods to minimise ball wear through control of mill atmosphere and addition of reagents are discussed.

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Grinding media wear appears to be non-linear with the time of grinding in a laboratory-scale ball mill. The kinetics of wear can be expressed as a power law of the type w=atb, where the numerical constant a represents wear of a particular microstructure at time t = 1 min and b is the wear exponent which is independent of the particle size prevailing inside a ball mill at any instant of time of grinding. The wear exponent appears to be an indicator of the cutting wear mechanism in dry grinding: a plot of the inverse of the normalised wear exponent (Image ) versusHs (where Hs is the worn surface hardness of the media) yields a curve similar to that of a wear resistance plot obtained in the case of two-body sliding abrasive wear. This method of evaluating the cutting wear resistance of media is demonstrated by employing 15 different microstructures of AISI-SAE 52100 steel balls in dry grinding of quartz in a laboratory-scale ball mill.

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The type of abrasion that the grinding medium experiences inside a ball mill is classified as high stress or grinding abrasion, because the stress levels at the surface of the medium exceed the yield stress of the metal when hard abrasives are crushed. During dry grinding of ores the medium undergoes not only abrasion but also erosion and impact. As all three mechanisms of wear occur simultaneously, it is difficult to follow the individual components of wear. However, it is possible to show that the overall kinetics of wear follows a simple power law of the type w = at(b), where w is the weight loss of the grinding medium for a specified grinding time t and a and b are constants. Experimental data, obtained from dry grinding of quartz for a wide range of times using AISI 52100 steel balls having various microstructures in a laboratory scale batch mill, are fitted to the proposed equation and the wear rate w is calculated from the first derivative of the equation. The mean particle sizes of the quartz charge DBAR corresponding to 50 and 80% retained size are determined by mechanical sieving of the ground product after a grinding time t and thus the relationship between wear rate and particle size of the abrasive is established. It is found that w increases rapidly with DBAR up to some critical size and then increases at a much lower rate.

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Marked ball grinding tests were carried out in the laboratory using high carbon low alloy steel (cast and forged) and high chrome cast iron balls. Relative ball wear as a function of grinding period and milling conditions was evaluated for the different type of ball materials in the grinding of lead-zinc sulphide and phosphate ores. Results indicated that ball wear increased with time and showed a sharp increase for wet grinding over dry grinding. Ball wear under wet grinding conditions was also influenced by the gaseous atmosphere in the mill. The influence of oxygen on the corrosive wear of grinding balls was increasingly felt in case of sulphide ore grinding. The grinding ball materials could be arranged in the following order with respect to their overall wear resistance:

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Wear of high carbon low alloy (HCLA) cast steel balls during the grinding of a chalcopyrite ore was evaluated under different experimental conditions. The role of oxygen in enhancing ball wear during wet finding is brought out. The influence of pH on ball wear was also examined from the view point of acid production during grinding and reactivity of sulphides. Contributions from corrosion and abrasion towards ball wear are quantified in terms of ball wear rates as a function of time, particle size and gaseous atmosphere in the mill.

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When a metal is surface ground the roughness generated is the summation of a function of the wheel roughness and the roughness due to wheel attrition and damage to the workpiece. We identify this function here as a maximum em,elope profile, which is fractal within certain cut off wavelengths determined by the dressing conditions of the wheel. Estimating the global displacement of the binder-grit-workpiece system from the maximum envelope power spectra, we determine the plastic indentation of the workpiece at characteristic length scales using simple contact-mechanical calculation. The estimated roughness corresponds well with that recorded experimentally for hard steel, copper; titanium and aluminium.

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Marked ball grinding rests were carried out in the laboratory with a low grade phosphate ore under different experimental conditions. Two types of balls were used, namely high carbon low alloy (HCLA) cast steel and high chrome cast iron. Results of marked ball grinding tests indicated that ball wear increased with time and showed a sharp increase for wet grinding over dry grinding. Ball wear under wet grinding conditions was also influenced by the gaseous atmosphere in the mill. The grinding ball materials could be arranged in the following order with respect to their overall wear resistance: High chrome cast iron > HCLA cast steel balls Methods to minimize ball wear through control of mill atmosphere and addition of flotation reagents are discussed. Effect of grinding media and additions of flotation reagents during grinding on phosphate ore flotation are also discussed. (C) 1999 Elsevier Science Ltd. All rights reserved.

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The recently developed reference-command tracking version of model predictive static programming (MPSP) is successfully applied to a single-stage closed grinding mill circuit. MPSP is an innovative optimal control technique that combines the philosophies of model predictive control (MPC) and approximate dynamic programming. The performance of the proposed MPSP control technique, which can be viewed as a `new paradigm' under the nonlinear MPC philosophy, is compared to the performance of a standard nonlinear MPC technique applied to the same plant for the same conditions. Results show that the MPSP control technique is more than capable of tracking the desired set-point in the presence of model-plant mismatch, disturbances and measurement noise. The performance of MPSP and nonlinear MPC compare very well, with definite advantages offered by MPSP. The computational speed of MPSP is increased through a sequence of innovations such as the conversion of the dynamic optimization problem to a low-dimensional static optimization problem, the recursive computation of sensitivity matrices and using a closed form expression to update the control. To alleviate the burden on the optimization procedure in standard MPC, the control horizon is normally restricted. However, in the MPSP technique the control horizon is extended to the prediction horizon with a minor increase in the computational time. Furthermore, the MPSP technique generally takes only a couple of iterations to converge, even when input constraints are applied. Therefore, MPSP can be regarded as a potential candidate for online applications of the nonlinear MPC philosophy to real-world industrial process plants. (C) 2014 Elsevier Ltd. All rights reserved.

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Grinding is an advanced machining process for the manufacturing of valuable complex and accurate parts for high added value sectors such as aerospace, wind generation, etc. Due to the extremely severe conditions inside grinding machines, critical process variables such as part surface finish or grinding wheel wear cannot be easily and cheaply measured on-line. In this paper a virtual sensor for on-line monitoring of those variables is presented. The sensor is based on the modelling ability of Artificial Neural Networks (ANNs) for stochastic and non-linear processes such as grinding; the selected architecture is the Layer-Recurrent neural network. The sensor makes use of the relation between the variables to be measured and power consumption in the wheel spindle, which can be easily measured. A sensor calibration methodology is presented, and the levels of error that can be expected are discussed. Validation of the new sensor is carried out by comparing the sensor's results with actual measurements carried out in an industrial grinding machine. Results show excellent estimation performance for both wheel wear and surface roughness. In the case of wheel wear, the absolute error is within the range of microns (average value 32 mu m). In the case of surface finish, the absolute error is well below R-a 1 mu m (average value 0.32 mu m). The present approach can be easily generalized to other grinding operations.

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The application of precision grinding for the formation of a silicon diaphragm is investigated. The test structures involved 2-6 mm diam diaphragms with thicknesses in the range of 25-150 //m. When grinding is performed without supporting the diaphragm, bending occurs due to nonuniform removal of the silicon material over the diaphragm region. The magnitude of bending depends on the µNal thickness of the diaphragm. The results demonstrate that the use of a porous silicon support can significantly reduce the amount of bending, by a factor of up to 300 in the case of 50 m thick diaphragms. The use of silicon on insulator (SOI) technology can also suppress or eliminate bending although this may be a less economical process. Stress measurements in the diaphragms were performed using x-ray and Raman spectroscopies. The results show stress of the order of 1 X107-! X108 Pa in unsupported and supported by porous silicon diaphragms while SOI technology provides stress-free diaphragms. Results obtained from finite element method analysis to determine deterioration in the performance of a 6 mm diaphragm due to bending are presented. These results show a 10% reduction in performance for a 75 µm thick diaphragm with bending amplitude of 30 fim, but negligible reduction if the bending is reduced to

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This paper describes the development of neural model-based control strategies for the optimisation of an industrial aluminium substrate disk grinding process. The grindstone removal rate varies considerably over a stone life and is a highly nonlinear function of process variables. Using historical grindstone performance data, a NARX-based neural network model is developed. This model is then used to implement a direct inverse controller and an internal model controller based on the process settings and previous removal rates. Preliminary plant investigations show that thickness defects can be reduced by 50% or more, compared to other schemes employed. (c) 2004 Elsevier Ltd. All rights reserved.