Application of neural network and fuzzy model to grinding process control

Odior, AO

doi:10.1007/s12530-013-9073-x

Cited by 13 publications

(2 citation statements)

References 13 publications

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“…Theoretically, FNN is a neural network with fuzzy signals and/or fuzzy weight, or sigmoidal transfer function, all of which are defined by Zadeh’s extension principle. In virtual of monotone mapping and multiple inputs, back propagation (BP) network combining with fuzzy inference system have already been used for wheel abrasion rectification, eyestrain correction, and other compensation issues (Odior, 2013). Thus, the fuzzy ruler updated by the neural network is adopted for data processing of AFS, i.e., the inputs of neural network are discrete values; while the outputs of neural network are smooth and robust.…”

Section: Hybrid Data Fusion Frameworkmentioning

confidence: 99%

A novel multi-sensors fusion framework based on Kalman Filter and neural network for AFS application

Liu

Cheng

Zeng

2014

Transactions of the Institute of Measurement and Control

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An adaptive front light system (AFS) is put forward by the Society of Automotive Engineers and Economic Commission for Europe as a means of enhancing vehicular lighting. Traditionally, AFS can be divided into three parts: (1) a leveling subsystem to make lighting parallel to the road surface; (2) a swiveling subsystem to change light distribution along with the angle of the steering wheel; (3) a dimming subsystem to reduce or intensify the lighting. In this paper, a new hybrid multi-sensor fusion framework combining Kalman Filter with neural network is proposed to adjust two stepper motors controlling the vehicles headlights pitch and yaw. Kalman Filter as the frontend is used to deal with redundant sensor signals that are collected from sensors in the different places. Fuzzy Neutral Network as the backend is used to generate adjustment of leveling and swiveling angle through the integration of different type signals. An adaptive parameter adjustment is accomplished by the proposed fusion framework with the varying filter coefficients. The simulation and experiment of leveling angle are conducted using the predefined experimental data. The evaluation results of leveling angle prove that the proposed algorithm can effectively filter out high-frequency perturbations and provide reliable outputs for stepper motor. The same results can be obtained for a swiveling subsystem. Consequently, the hybrid fusion framework is a feasible approach for AFS design to accomplish data processing and nonlinear mapping.

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Section: Hybrid Data Fusion Frameworkmentioning

confidence: 99%

A novel multi-sensors fusion framework based on Kalman Filter and neural network for AFS application

Liu

Cheng

Zeng

2014

Transactions of the Institute of Measurement and Control

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“…Taking into account the complexity of the interactions of the influencing factors in the grinding process, intelligent technologies increasingly are being used in data processing and parameter optimization, such as neural networks, 19 genetic algorithms, 20–22 fuzzy theory, 23 and the application of particle swarm optimization. 24 By combining algorithms, genetic neural network algorithm, 25 neural network model based on improved particle swarm optimization, 26 and fuzzy neural network model, 27 these methods are used to optimize the grinding parameters or to predict grinding quality. These methods have some problems, however, such as the need for large amount of experimental data, large amount of calculation, and regular data or the mathematical models are too complicated.…”

Section: Introductionmentioning

confidence: 99%

A new method for optimizing process parameters of active measurement grinding based on grey target decision making

Zheng

Liu

Guo

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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Active measurement technology is used widely in the modern precision-grinding process; currently, however, it is tedious and time consuming to adjust the process parameters of grinding process. This process usually depends on the experience of operators, which directly affects the processing efficiency and intelligent control level. Aiming to optimize these grinding-process parameters, we propose a grey target decision-making method based on the uniform effect measure to realize the intelligent optimization of process parameters. First, we obtain the experimental data of process parameters and evaluation parameters using the orthogonal experimental method, and then we calculate and analyze the degree of relation between process parameters and evaluation parameters based on grey system theory. Then we can obtain the process parameters that have the greatest influence on the workpiece surface roughness, workpiece roundness, and grinding process time. Second, through the proposed grey target decision-making method based on the uniform effect measure, and combined with grey relation analysis, we assign the weights of different evaluation parameters. By optimizing the calculation of multigroup evaluation parameters, we obtain the group of evaluation parameters with the best synthetic effect and identify the optimal combination of parameters to guide production. Through a large number of experimental verifications and analyses, we find that the proposed grinding process parameters optimization method can obtain the best combination of process parameters and can effectively improve processing quality and processing efficiency. The research results provide a theoretical basis for the optimization of grinding process parameters and enhance the intelligent degree of precision grinding.

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Thermal behaviors in the metal high-efficiency grinding process under simulated vacuum conditions: state of the art and future perspectives

Zhang,

Wang

et al. 2024

Int J Adv Manuf Technol

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Application of neural network and fuzzy model to grinding process control

Cited by 13 publications

References 13 publications

A novel multi-sensors fusion framework based on Kalman Filter and neural network for AFS application

A novel multi-sensors fusion framework based on Kalman Filter and neural network for AFS application

A new method for optimizing process parameters of active measurement grinding based on grey target decision making

Thermal behaviors in the metal high-efficiency grinding process under simulated vacuum conditions: state of the art and future perspectives

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