Intelligent Monitor and Control System for Grinding Process

Liu, Gui Jie; Gong, Ya Dong; Wang, Wan Shan

doi:10.4028/0-87849-986-5.535

Cited by 3 publications

(3 citation statements)

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“…In the grinding process with grinding wheel radial cut-in, the real feed of grinding wheel is equal to that subtract the elasticity deflection of the system and the wear of the grinding wheel from the nominal feed [3]. In order to control grinding wheel feed accurately, the new program of cut-in grinding process (shown in Figure 1) is used to control grinding wheel feed.…”

Section: Mathematic Model Buildingmentioning

confidence: 99%

See 1 more Smart Citation

Study on The Self-Adaptive Forecast and Optimal Control Method for Grinding Wheel Infeed

Liu

Mei

Gong

et al. 2007

KEM

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A self-adaptive forecast & optimal control method for grinding wheel in-feed is presented, it can control grinding wheel plunge by using the new program of grinding process, and can compensate availably the size errors produced by the elasticity deflection of the grinding system and the deference of work-pieces rough and the wear of the grinding wheel, et al. The result of computer simulation and real testing indicate that this method can improve grinding quality.

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Section: Mathematic Model Buildingmentioning

confidence: 99%

“…The k step forecasting method is used to identify the model coefficients of grinding wheel feed on-line, so Eq.8 must be turned into the discrete model on sample period , as flowing [3].…”

Section: Self-adaptive Forecast and Optimized Controlling Strategy Gr...mentioning

confidence: 99%

Study on The Self-Adaptive Forecast and Optimal Control Method for Grinding Wheel Infeed

Liu

Mei

Gong

et al. 2007

KEM

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“…They found that the grinding temperature was relatively lower due to a less heat generation and more heat dissipation. Liu G et al [12] set up an intelligent monitoring system for grinding temperature by using nonlinear theory models and neural network learning. Chen T et al [13] utilized the ultrasonic vibration-assisted grinding method to grind CFRP and studied the relationship between grinding temperature and grinding process parameters: they observed that the grinding temperature is far below the conventional grinding temperature, which is remarkably influenced by the grinding depth.…”

Section: Introductionmentioning

confidence: 99%

Grinding temperature prediction model of high-volume fraction SiCp/Al composite

Zhu

Ping

et al. 2020

Int J Adv Manuf Technol

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Grinding is the main processing technique for particle-reinforced composites, in which grinding force and temperature are significant for investigating the removal mechanism and surface properties. In this study, a grinding force prediction model is established based on the response surface methodology. The energy density of surface heat source in the grinding area is calculated by combining the contact area between the grinding wheel and workpiece. Based on the integration algorithm in time and space, a theoretical ideal grinding temperature prediction model is deduced. The grinding heat energy transfer coefficient is defined as the percentage of heat energy, which is transferred from the grinding heat energy to the workpiece. According to the experimental and ideal theoretical highest grinding temperatures, a prediction model of the heat energy transfer coefficient is established, and the modified theoretical highest grinding temperature prediction model is obtained. Furthermore, a finite element model of SiCp/Al composite in grinding is established. The highest grinding temperature in the finite element model is consistent with the modified theoretical highest grinding temperature, and the grinding temperature field is analysed. The highest grinding temperature increases as the wheel speed and grinding depth increase, while it decreases as table speed increases, which differs from that of some common materials. Thus, the grinding temperature prediction model provides an important reference for the study of removal mechanism and surface properties of SiCp/Al composite.

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GICS for grinding process optimization

Holešovský¹,

Náprstková²,

Novák³

2012

Manufacturing Technology

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The article deals with the automation of the grinding machine duty cycle. There are both described, the problems of the automation of the duty cycle and the present state summarised in this paper. The control systems of grinding cycle run from the experiences and process knowledge. Real GICS (Grinding Intelligent Control System) must be founded on the process monitoring and to create own control system with principal parameters of grinding. Cutting speed, workpiece motion speed, depth of cut, volume of coolant, course of cutting forces and other process parameters can form single control components at the adequate process knowledge. At some control systems description the authors assume the future possible direction of development, considering possibilities in the use of the expert and intelligent systems.

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Intelligent Monitor and Control System for Grinding Process

Cited by 3 publications

References 0 publications

Study on The Self-Adaptive Forecast and Optimal Control Method for Grinding Wheel Infeed

Study on The Self-Adaptive Forecast and Optimal Control Method for Grinding Wheel Infeed

Grinding temperature prediction model of high-volume fraction SiCp/Al composite

GICS for grinding process optimization

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