An artificial-neural-networks-based in-process tool wear prediction system in milling operations

Chen, Jacob C.; Chen, Joseph C.

doi:10.1007/s00170-003-1848-y

Cited by 64 publications

(14 citation statements)

References 5 publications

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“…Persistent all-encompassing analysis has been carried out to advance the adequacy of cutting tools. Jacob and Joseph (2005) pointed out that the product quality and ability of machining action depend on cutting tool condition. The tool wear is provoked by adhesion, abrasion, diffusion and/or oxidation (Lorentzon and Jarvstrat, 2009).…”

Section: Introductionmentioning

confidence: 99%

Optimization of end milling parameters under minimum quantity lubrication using principal component analysis and grey relational analysis

Murthy¹,

Rajendran²

2012

J. Braz. Soc. Mech. Sci. & Eng.

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Section: Introductionmentioning

confidence: 99%

Optimization of end milling parameters under minimum quantity lubrication using principal component analysis and grey relational analysis

Murthy¹,

Rajendran²

2012

J. Braz. Soc. Mech. Sci. & Eng.

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“…They found that the predictive neural network model offers better tool flank wear predictions within the trained range. An artificial-neural-networks-based in-process tool wear prediction (ANN-ITWP) system was proposed and evaluated by Jacob Chen and Joseph Chen [5]. Results showed that the system could predict the tool wear online with an average error of 0.037 mm.…”

Section: Introductionmentioning

confidence: 98%

Tool flank wear prediction in CNC turning of 7075 AL alloy SiC composite

Chavoshi

2010

Prod. Eng. Res. Devel.

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Flank wear occurs on the relief face of the tool and the life of a tool used in a machining process depends upon the amount of flank wear; so predicting of flank wear is an important requirement for higher productivity and product quality. In the present work, the effects of feed, depth of cut and cutting speed on flank wear of tungsten carbide and polycrystalline diamond (PCD) inserts in CNC turning of 7075 AL alloy with 10 wt% SiC composite are studied; also artificial neural network (ANN) and co-active neuro fuzzy inference system (CANFIS) are used to predict the flank wear of tungsten carbide and PCD inserts. The feed, depth of cut and cutting speed are selected as the input variables and artificial neural network and co-active neuro fuzzy inference system model are designed with two output variables. The comparison between the results of the presented models shows that the artificial neural network with the average relative prediction error of 1.03% for flank wear values of tungsten carbide inserts and 1.7% for flank wear values of PCD inserts is more accurate and can be utilized effectively for the prediction of flank wear in CNC turning of 7075 AL alloy SiC composite. It is also found that the tungsten carbide insert flank wear can be predicted with less error than PCD flank wear insert using ANN. With Regard to the effect of the cutting parameters on the flank wear, it is found that the increase of the feed, depth of cut and cutting speed increases the flank wear. Also the feed and depth of cut are the most effective parameters on the flank wear and the cutting speed has lesser effect.

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“…In a study by Chen and Chen (2005) used an artificial neural network to evaluate data obtained from a force dynamometer measuring cutting forces during linear milling cuts. Another similar study was completed by Lin and Lin (1996) to monitor face mill inserts.…”

Section: Introductionmentioning

confidence: 99%

Condition Monitoring for Indexable Carbide End Mill Using Acceleration Data

Milner

Roth

2010

Machining Science and Technology

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In order to automate machining operations, it is necessary to develop robust tool condition monitoring techniques. In this paper, a tool monitoring strategy for indexable tungsten carbide end milling tools is presented based on the Fourier transform and statistical analysis of the vibrations of the tool during the machining operations. Using a low-cost, tri-axial piezoelectric accelerometer, the presented algorithm demonstrates the ability to accurately monitor the condition of the tools as the wear increases during linear milling operations. One benefit of using accelerometer signals to monitor the cutting process is that the sensor does not limit the machine's capabilities, as a workpiece mounted dynamometer does. To demonstrate capabilities of the technique, four tool wear life tests were conducted under various conditions. The indirect method discussed herein successfully tracks the tool's wear and is shown to be sensitive enough to provide sufficient time to replace the insert prior to damage of the machine tool, cutter, and/or workpiece.

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An artificial-neural-networks-based in-process tool wear prediction system in milling operations

Cited by 64 publications

References 5 publications

Optimization of end milling parameters under minimum quantity lubrication using principal component analysis and grey relational analysis

Optimization of end milling parameters under minimum quantity lubrication using principal component analysis and grey relational analysis

Tool flank wear prediction in CNC turning of 7075 AL alloy SiC composite

Condition Monitoring for Indexable Carbide End Mill Using Acceleration Data

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