On-line monitoring of boring tools for control of boring operations

Liu, T. I.; Kumagai, Akihiko; Wang, Y. C.; Song, Shin-Da; Fu, Zhenwen; Lee, Jay

doi:10.1016/j.rcim.2009.11.002

Cited by 25 publications

(9 citation statements)

References 16 publications

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“…The raw material for the boring process had an inside diameter of 38.1 mm (1.5 in), an outside diameter of 116.84 mm (4.6 in), and a length of 57.15 mm (2.25 in). 10 The work material was Titanium 6Al4V with a hardness of Rc 28. The material was bored from an inner diameter of 38.1 mm (1.5 in) to 88.9 mm (3.5 in).…”

Section: Experimental Setup and Daqmentioning

confidence: 99%

“…Feature extraction is the process of obtaining important parameters from the measured force data. 10,24,25 Since the tool wear is measured at the end of every boring process, the force data measured on-line/in-process represents the respective tool wear measurement.…”

Section: Feature Extraction and Feature Selectionmentioning

confidence: 99%

“…Feature selection of this research was based on previous research for on-line detection and measurements of tool wear and the observation of the experimental data of this work. 10,11 The values of the selected features were calculated from cutting force data using the equations described in section “Feature extraction and feature selection” and then used as inputs of ANFIS. There were 48 training sets for on-line detection of boring tool conditions and 39 training sets for on-line measurements.…”

Section: On-line Detection and Measurements Of Tool Wearmentioning

confidence: 99%

“…4 So far, TCM research has used different indirect indexes including cutting forces, vibration, accelerations, acoustic emissions, and temperature measurements for various machining operations. 5–22…”

Section: Introductionmentioning

confidence: 99%

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On-line detection and measurements of tool wear for precision boring of titanium components

Liu

Jolley²,

Yang

2015

Proceedings of the Institution of Mechanical Engineers, Part B:

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On-line detection and measurements of tool wear is important to assure manufacturing accuracy, enhance manufacturing efficiency, and reduce manufacturing costs. In this research, adaptive neuro-fuzzy inference systems are utilized in conjunction with features extracted from three-axis cutting force data for the on-line detection and measurements of tool wear for precision boring of titanium components. Cutting force data were measured for carbide tools during the boring of titanium parts. At the end of every boring process, the average flank wear width was measured to determine the cutting tool conditions. Measurements were accomplished with the aid of a toolmaker’s microscope. In total, 14 features were obtained from the cutting force data. Euclidean distance measure was utilized to determine which features showed the best indication of cutting tool conditions. This approach can reduce the number of features for on-line detection and measurements of tool wear for precision boring of titanium parts. The selected two most prominent features were kurtosis of longitudinal force and average of the ratio between tangential force and radial force. On-line detection of boring tool wear obtained excellent results, using a 2×2 adaptive neuro-fuzzy inference systems, of being able to predict tool conditions on-line with 100% reliability. On-line measurements of boring tool wear also produced exceedingly successful results with a minimum error for a 1×10 adaptive neuro-fuzzy inference systems of 0.87%.

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Section: Experimental Setup and Daqmentioning

confidence: 99%

Section: Feature Extraction and Feature Selectionmentioning

confidence: 99%

Section: On-line Detection and Measurements Of Tool Wearmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On-line detection and measurements of tool wear for precision boring of titanium components

Liu

Jolley²,

Yang

2015

Proceedings of the Institution of Mechanical Engineers, Part B:

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show abstract

“…The objective of this research is to develop an efficient mixing process for the glass production furnace by the use of artificial neural networks [16][17][18][19][20][21]. Compressible gas flow is nonlinear in nature and involves several different parameters.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Intelligent measurements for monitoring and control of glass production furnace for green and efficient manufacturing

Liu

Lyons

Sukanya

et al. 2014

Int J Adv Manuf Technol

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Liquefied petroleum (LP) gas is used as one of the fuel systems for glass production furnaces. This research was conducted to develop an intelligent online measurement system for monitoring and control of LP gas so as to achieve green and efficient manufacturing. LP gas is mixed with air at a desired ratio in order to get a proper specific gravity for glass production. Counterpropagation neural networks (CPNs), which are based on competitive learning, were used in this work. Three inputs, air inlet pressure, air/mixed gas differential pressure, and propane/mixed gas differential pressure, were selected for online measurements of specific gravity for monitoring and control of a glass production furnace. Using a 3×12×1 CPN yields exceedingly successful results for online measurements of specific gravity. An average error of 1.68 %, a minimum error of 0.08 %, and a maximum error of 4.43 % were achieved for online measurements. Control actions can then be taken to achieve much higher energy efficiency which is very important for glass production for green and efficient manufacturing.

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Monitoring and diagnosis of the tapping process for product quality and automated manufacturing

Liu

Lee

Liu³

et al. 2012

Int J Adv Manuf Technol

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On-line monitoring of boring tools for control of boring operations

Cited by 25 publications

References 16 publications

On-line detection and measurements of tool wear for precision boring of titanium components

On-line detection and measurements of tool wear for precision boring of titanium components

Intelligent measurements for monitoring and control of glass production furnace for green and efficient manufacturing

Monitoring and diagnosis of the tapping process for product quality and automated manufacturing

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