Control Of Grinding Process Research Articles

High-Speed CNC Grinding Process Optimization of Non-Circular Rotary Parts Based on Particle Swarm Optimization

As for the high-speed CNC grinding process for non-circular rotary parts with a large rate of lift change (such as camshaft of automotive engine), the acceleration of grinding wheel often has a direct impact on the contour grinding precision of the workpiece due to the large acceleration of movement between of the workpiece and the grinding wheel produced from CNC grinding interpolation. Researches show that solving this problem is an important way to improve the precision of high-speed CNC grinding for non-circular rotary parts. In this paper, a high-speed CNC grinding process control and optimization method is presented for non-circular rotary parts based on acceleration interpolation movement. The negative influence of the problem about high acceleration can be reduced effectively and the precision of high-speed CNC grinding for non-circular rotary parts can be improved greatly by the proposed method. Additionally, the simulation shows that the optimization of grinding parameters forgrinding process has a good performance.

Design of Cemented Carbide Face Automation Grinder Based on Constant Force Grinding and Online Measurement Technology

A set of face automation grinder is designed aimed at the cemented carbide characteristics of high hardness, high wear resistance, and good dimensional stability,and automatic grinding process is divided into three stages of coarse grinding,accurate grinding and light grinding; Constant force feed system is designed using servo drive mechanism and pneumatic mechanism; Using on-line measurement and grinding support institutions, the grinding process real-time monitoring and real-time processing size measurement are realized; With PLC as the central control unit automatic control system is designed for the grinding force control, measurement data processing and feedback show and grinding process control; The machine design idea is provided the references for other grinding machine automation design.

Application of neural network and fuzzy model to grinding process control

Grinding is a machining process for removing material from a workpiece by using an expendable grinding wheel. The paper presents, a control system for grinding process using neural network and fuzzy technique. It was discovered that the maximum grinding temperature was very important since too high temperature would lead to surface burns and thermal damage to the grinding wheel as well as the workpiece material. A neuro-fuzzy model was used to analyze the grinding wheel performance index as it affects the general grinding operations of the grinding process. The research work can be applied to any other grinding process, whether wet or dry grinding process.

Predictive modeling of force and power based on a new analytical undeformed chip thickness model in ceramic grinding

The grinding force and power play an important role in ceramic grinding process as they not only have the direct influence on the wheel wear, grinding accuracy, grinding temperature and surface integrity but also have strong influence on local contact deflection and the nature of the contact deflection that has an important effect on the mechanism of material removal. In addition, they are also important to many aspects of ceramic grinding process optimization, monitoring, and control. So the prediction of grinding force and power in ceramic grinding is essential. But, the force and power is governed by many factors and its experimental determination is laborious and time consuming. So the establishment of a model for the reliable prediction of grinding force and power is still a key issue for ceramic grinding. In this study, a new grinding force and power model is developed, for the reliable prediction of grinding force and power in ceramic grinding, based on a new analytical undeformed chip thickness model. This new analytical undeformed chip thickness model is developed on the basis of stochastic nature of the grinding process, governed mainly by the random geometry and the random distribution of cutting edges. The model includes the real contact length that results from combined contact length, due to wheel-workpiece contact zone deflection and the local deflection due to the microscopic contact at the grain level and contact length due to geometry of depth of cut. The proposed model is used to predict the total grinding forces and power in surface grinding. The new model has been validated by conducting experiments on a horizontal surface grinding machine by grinding silicon carbide with diamond grinding wheel. Results indicate that the proposed model shows a good agreement with the experimental data obtained from different kinematic conditions. It also results in a significant reduction in the grinding forces, as compared with that obtained by the force model developed based on the existing undeformed chip thickness model, under the same operating conditions, in silicon carbide grinding.

The Simulation and Design Applications of Grinding and Classification Process Control System

Based on the grinding and classification process dynamic model, the distributed simulation platform for semi-physical grinding process was analyzed. Based on the feedback correction and dynamic optimal control and optimization model calculated the optimal control law, the quality indicators to feedback regulation mechanism was introduced to eliminate the impact of process disturbances and other uncertainties. Intelligent control unit according to the deviation between the artificial test and expectations of quality indicators can feedback correction of the optimal control law. The field experiment results show that the program to stabilize the process of product quality, to achieve the process of saving energy. The grinding process of the optimal control of distributed simulation platform for the optimal control method and system design provide effective, convenient, reliable and intuitive engineering lab environment. Also it has important reference value to other metallurgical optimization of industrial process control engineering verification and simulation.

Intelligent Multivariable Decision-Making System for Complicated Industrial Process Control

Complicated industrial processes are generally characterized by being multivariable, non-linearity, severe coupling, large-time delay and time-variance. As such, it is difficult to achieve a satisfactory performance using a conventional control method. In this paper, an intelligent multivariable decision-making system for mineral grinding process control is proposed with the case-based reasoning (CBR). This intelligent decision system can auto-adjust the set-points of the process controllers according to the operational conditions. As long as the process control system tracks their adjusted set-points, the closed-loop control of grinding particle size is achieved. The industrial application in a mineral processing plant shows the effectiveness of the proposed approach.

A Mathematical Model for Predicting the Internal Parameters of Ball Mill

The internal parameters of ball mill are very important in the grinding process and have significant impact on the grinding results. Accurate measurement of the internal parameters of ball mill is crucial and indispensable for optimization and control of grinding process. In this paper, a novel tri-sensor measurement method was used to measure the external signals (i.e., bearing pressure, sound intensity and power consumption) of ball mill. Mathematical models, which link the external signals of ball mill with its internal parameters, were established to predict the internal parameters of ball mill. Experimental results showed that the mathematical models could directly predict the internal parameters of ball mill once the external signals were obtained. The tri-sensor measurement method and the mathematical models proposed in this paper provided a new way and solid basis for optimization and control of ball milling.

Comparing the performance of neural networks developed by using Levenberg–Marquardt and Quasi-Newton with the gradient descent algorithm for modelling a multiple response grinding process

Monitoring and control of multiple process quality characteristics (responses) in grinding plays a critical role in precision parts manufacturing industries. Precise and accurate mathematical modelling of multiple response process behaviour holds the key for a better quality product with minimum variability in the process. Artificial neural network (ANN)-based nonlinear grinding process model using backpropagation weight adjustment algorithm (BPNN) is used extensively by researchers and practitioners. However, suitability and systematic approach to implement Levenberg–Marquardt (L–M) and Boyden, Fletcher, Goldfarb and Shanno (BFGS) update Quasi-Newton (Q-N) algorithm for modelling and control of grinding process is seldom explored. This paper provides L–M and BFGS algorithm-based BPNN models for grinding process, and verified their effectiveness by using a real life industrial situation. Based on the real life data, the performance of L–M and BFGS update Q-N are compared with an adaptive learning (A-L) and gradient descent algorithm-based BPNN model. The results clearly indicate that L–M and BFGS-based networks converge faster and can predict the nonlinear behaviour of multiple response grinding process with same level of accuracy as A-L based network.

Framework of grinding process modeling and simulation based on microscopic interaction analysis

This paper describes a framework of grinding process modeling to understand the grinding fundamentals and design grinding processes with predictive performance. The model regards grinding process as a time dependent process and an integration of microscopic interactions in the wheel-workpiece contact zone, including cutting, plowing, and sliding as well as other frictional interactions. The grinding process control and design are in fact to manage and balance all these interactions. The principles of microscopic interactions are analyzed and used to correlate the grinding process input parameters and performance output.

Modeling for Robot High Precision Grinding Based on SVM

To improve the removal control for robot grinding process, we propose a modeling method based on SVM (support vector machine) regression. By analyzing a group of measurable variables relevant to grinding removal, such as robot’s speed, contact force and curvature of the workpiece’s surface, a regression model is built using machine learning method to predict the grinding removal. In this way, the analysis on a series of complicated dynamic variables could be avoided. The experimental results show that this method could achieve good performance. The prediction accuracy of the model reaches higher than 90%, which basically meets the demand of practical grinding.

Development of CBN Internal Grinding Machine for Precision Grinding of the Air-Conditioner Compressor Piston Hole

According to the demand for internal grinding machine in precise machining of air-conditioner compressor piston hole, a kind of high precise CNC internal grinding machine with CBN wheel is developed by adopting self-adaptive control for grinding process. Meanwhile, grinding precision and stability are achieved by overcoming many problems emerging from high-speed grinding process with CBN wheel. More than 100000 times’ grinding experimental results show that the whole performance of grinding machine developed is the same as that of the same kind of international machine. In order to study grinding mechanism with CBN wheels and then improve machine’s capability, grinding data in several machining cycles is analyzed.

Characterisation of case-hardened gear steel by multiparameter Barkhausen noise measurements

Renewable energy sources have become nowadays very popular. Wind energy is one of the most promising renewable energy sources of the future. Due to the booming energy markets and increasing production of wind turbines, the requirement for long-lasting and reliably working windmills has emerged. The Barkhausen noise (BN) method is a world-leading technique in the quality control of industrial grinding processes, for example in windmill gear production. The main goal for this study is to create a profound approach to the multiparameter Barkhausen noise measured and to compare the measured values to the absolute values obtained with other research methods such as residual stress measurements by X-ray diffraction, hardness measurements and microstructural studies. A test bar series was made from a case-hardened steel used in wind turbine gears. Different tempering parameters were used to vary the surface hardness of the test bars. A stepwise loading was done simultaneously with the BN measurements to obtain relevant multiparameter BN data and to investigate the effect of applied stress on the BN signal. The results indicate clearly that the response of the studied material is mainly consistent with the known BN relations, but there exist some unequal BN stress correlations in the tempered test bars.

Constrained model predictive control in ball mill grinding process

Stable control of grinding process is of great importance for improvements of operation efficiency, the recovery of the valuable minerals, and significant reductions of production costs in concentration plants. Decoupled multi-loop PID controllers are usually carried out to manage to eliminate the effects of interactions among the control loops, but they generally become sluggish due to imperfect process models and a close control of the process is usually impossible in real practice. Based on its inherent decoupling scheme, model predictive control (MPC) is employed to handle such highly interacting system. For high quality requirements, a three-input three-output model of the grinding process is constructed. Constrained dynamic matrix control (DMC) is applied in an iron ore concentration plant, and operation of the process close to their optimum operating conditions is achieved. Some practical problems about the application of MPC in grinding process are presented and discussed in detail.

Fuzzy Control in Profile Envelope Process of Form Cutter for Involute Gear and its Movement Control

In profile envelope process of form cutter for involute gear, the grinding depth is variable which causes grinding force to change on a large scale and the profile error of envelope to increase. To improve form cutter accuracy, a closed-loop control system was designed to accomplish control of envelope process. For complicated dynamic process, fuzzy controller is used to adjust parameters online. By measuring grinding force, characteristic information of grinding process is acquired. Regulation factor of feed rate is determined by grinding force ratio, force deviation and its change rate used as evaluation indexes. Thus, fuzzy control of constant force grinding process is accomplished. Simulation and cutter grinding test indicates that the system has high precision and stability, and reduces cutter error effectively.

Modeling of Micro-Positioning System Using Direct Mapping and Polynomial Fit for Precision Grinding Process Control

For micro-positioning systems using piezoelectric actuators for precision grinding process control, sinusoidal command signals will be used and will give additional problems in comparison with the commonly used step signals due to the hysteresis effects, which require a good modeling approach. In order to avoid the discontinuity problem in obtaining the values of the piezoelectric constant, a new approach of direct mapping with polynomial fit is proposed. Theoretical and experimental studies are conducted and comparative studies are made. Compared with the single polynomial approach, the proposed method of direct mapping with polynomial fit is able to reduce the modeling error to 12.5%, which is 6.5% lower, and the problems in obtaining the values of the piezoelectric constant are avoided. The proposed approach is shown advantageous. Further studies are necessary to significantly reduce the modeling error.

Modelling of piezoelectric micropositioning system using direct mapping and numerical analysis for precision grinding process control

For micro/nano positioning systems using piezoelectric actuators for precision grinding process control, sinusoidal command signals will be used and will give additional problems in comparison with the commonly used step signals due to the hysteresis effect, which require good modelling of the effect. To avoid discontinuity in obtaining the values of the piezoelectric constant, a method of direct mapping with polynomial fit has been proposed. Compared with the single polynomial approach, the model obtained was able to reduce the modelling error by 6.5%. To further reduce the modelling error, a new method using direct mapping and numerical analysis is proposed while discontinuity is avoided. Theoretical and experimental studies are conducted and comparative studies are made. Compared with the single polynomial approach, it is able to reduce the modelling error to 7.3%, which is 45.5% lower. Compared with the direct mapping with polynomial approach, the proposed method is able to reduce the modelling error by 41.7%. For a typically fast piezoelectric micro/nano positioning system, hysteresis distortion is more significant towards modelling error than dynamic distortion. The proposed new approach should be useful in precision grinding process control.

FEM Analysis of Grinding Damage Mechanisms for Ceramics Materials

The utilization of ceramics materials for structural components has been widespread because of their excellent properties. But the ceramic components require high precision and integrity of surface. The grinding is the main productive technique for finishing the ceramic components in the manufacturing industry, but it is prone to surface and sub-surface damage. Consequently the induced damage will reduce the life of the component, even leads to failure. So, to improve grinding quality and reduce grinding damage, it is important to understand the damage mechanisms and process for ceramic materials. In this paper, attempts are made to build a damage model and to compute damage variable D. The damage variable will be used in the further computation. The research effort made may be the foundation of the ceramic components design and the optimization and control of ceramic grinding process.

Grinding force and power modeling based on chip thickness analysis

The ability to predict grinding force and power is important to many aspects of grinding process optimization, monitoring, and control. This paper presents the predictive modeling of grinding force and power based on the probabilistic distribution of undeformed chip thickness as a function of the kinematic conditions, material properties, wheel microstructure, and dynamic effects. The chip thickness is the main random variable and it is expected to assume a Rayleigh probability density function. The model takes into account the microstructure of the grinding wheel given by the grain geometry and the static grain density in terms of the radial depth into the wheel. The dynamic cutting edge density was calculated incorporating the effects of kinematic and dynamic phenomena such as the kinematic hidden grains and the local grain deflection. The elastic deformation of the grinding contact length was also considered. The model was used to predict the total tangential and normal forces in surface grinding and the total grinding power in cylindrical grinding. In both cases experimental measurement data in the context of chip thickness probability density, tangential force, normal force, and power have been presented and compared to model calculations.

Intelligent Monitor and Control System for Grinding Process

The intelligent monitor and control system for grinding process is proposed in this paper, it utilizes the nonlinear modeling theory and self-learning capability of neural network to setup non-liner mapping relations between grinding conditions and AE signals, and to realize grinding process intelligent monitor and control by picking-up grinding AE signals on line. Grinding quality can be monitored on-line, starting parameters for grinding process can be selected, on-line intelligent adjustment of grinding conditions also can bee realized by using this system.

INTEGRATED APPROACH TO MONITORING AND CONTROL OF MINERAL GRINDING PROCESSES

To enhance the operation of mineral grinding processes, a greater number of monitoring services and control schemes are nowadays being offered by the equipment manufacturers. In this paper an integrated approach to grinding process monitoring and control is formulated and the components of the integrated automation for typical grinding processes are proposed. Furthermore, the benefits of the process monitoring services are studied on the basis of a specific case study - the Outokumpu Chrome Kemi concentrator. Finally, the results are discussed and a new control scheme is outlined.