Multi-parameter optimization and control of the cylindrical grinding process

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.

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Monitoring and Model Generation for Intelligent Optimization and Control of Surface Grinding Processes

ASME 2007 International Manufacturing Science and Engineering Conference ◽

10.1115/msec2007-31092 ◽

2007 ◽

Author(s):

Radu Pavel ◽

Anil Srivastava

Keyword(s):

Process Condition ◽

Surface Grinding ◽

Grinding Wheel ◽

Model Generation ◽

Grinding Process ◽

Exact Science ◽

Machining Processes ◽

Workpiece Material ◽

Optimization And Control ◽

And Control

The grinding process involves more variables than most of the other machining processes. In the past, grinding process has been viewed as an art more than an exact science. This paper presents a monitoring and model generation strategy developed to allow science-based optimization and control of the grinding process. The monitoring solution involves simultaneous acquisition of power, forces, acoustic emission and vibration data generated during surface grinding. A custom build data acquisition program helps capture the information and visualize the process condition. Dressing consistency and spindle condition are monitored through the same system. Part of the data is processed off-line to determine coefficient values for generalized equations that model main monitored parameters. An optimization relative to cycle time or cost can be conducted based on the results gathered for each combination of grinding wheel, workpiece material and metalworking fluid. The procedure requires a minimal number of experimental runs to determine the model coefficients. The solution opens the path towards the development of a model-based condition monitoring system with adaptive control.

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The Grinding Fluid Influences on Surface Roughness Using Acoustic Emission

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.686.186 ◽

2016 ◽

Vol 686 ◽

pp. 186-193

Author(s):

Hao Liu ◽

Long Zhao ◽

Omar Bafakeeh ◽

Ioan D. Marinescu

Keyword(s):

Surface Roughness ◽

Acoustic Emission ◽

Grinding Process ◽

Response Factors ◽

Key Factor ◽

Optimization And Control ◽

Ae Signal ◽

And Control ◽

Ae Signals ◽

Acoustic Emission Technology

For optimization and control of the grinding process, it is necessary to monitor the process state. Fluid selection for grinding process is also considered as key factor for surface quality. This study focuses on the effects of different fluids in grinding process using Acoustic Emission technology. The analysis is carried out grouping the tests according to the main measured: Acoustic Emission (AE) signals, Normal and Tangential Forces on the workpiece surface, Grinding Temperature and Surface Roughness. The potential of real-time monitoring grinding process using Acoustic Emission technology is also tested. The results of this research show that selections of grinding fluids do have a significant influence on response factors such as surface roughness and AE signals. Further, prediction of surface roughness during the grinding process using AE signal monitoring is demonstrated in this work.

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