In-process drill wear and breakage monitoring for a machining centre based on cutting force parameters

Li, G.S.; Lau, W.S.; Zhang, Y.Z.

doi:10.1016/0890-6955(92)90035-f

Cited by 21 publications

(4 citation statements)

References 4 publications

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“…Parameters for monitoring drill conditions, thrust force and cutting torque have been used because they are the most sensitive to the changes of drill conditions (G.S. Li, W.S. Lau, Y.Z.…”

Section: Introductionmentioning

confidence: 99%

Back Propagation Wavelet Neural Network Based Prediction of Drill Wear from Thrust Force

Yang¹,

Kumehara²,

Zhang³

2009

CIS

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The fast monitoring of tool wears by using various Cutting signals and the prediction models developed rapidly in recent years. Comparatively, various wear forecast models based on artificial neural networks (ANN) perform much better in accuracy and speediness than the conventional prediction models. Combining the prominent dynamic properties of back propagation neural network (BPNN) with the enhanced ability of a wavelet neural network (WNN) in mapping nonlinear functions, a Back propagation wavelet neural network (BPWNN) is newly established to perform prominent prediction of drill wear. In this work, a multilayer BPWNN with wavelet algorithm has been applied to predict the average wear of a K10 carbide drill bit for drilling on a high silicon aluminum work piece. Mean value of the thrust force, cutting torque, and drilling depth, spindle speed and feed-rate are inputs to the network, and drill wear is the output. Drilling experiments have been carried out over a wide range of cutting conditions and the effects of drill wear, cutting conditions (spindle speed, drilling depth and feed-rate) on the thrust force and cutting torque have been investigated. Performance of BPWNN has proved to be satisfactory by experimental result. The accuracy of the prediction of drill wear using BPWNN is found to be better than using BPNN, and that BPWNN can learn the pattern faster compared to BPNN and could be used advantageously in online drill wear monitoring and prediction.

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“…Parameters for monitoring drill conditions, thrust force and cutting torque have been used because they are the most sensitive to the changes of drill conditions (G.S. Li, W.S. Lau, Y.Z.…”

Section: Introductionmentioning

confidence: 99%

Back Propagation Wavelet Neural Network Based Prediction of Drill Wear from Thrust Force

Yang¹,

Kumehara²,

Zhang³

2009

CIS

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show abstract

“…The low-pass filter method with a cutoff frequency of 10 Hz was used to pro original cutting force data in the X and Y directions [33,34], and the curve of the cutting force of the tool with the cutting distance was extracted, as shown in Figu It could be clearly seen that the mean cutting force in both the X-direction and Y-d increased sharply with the cutting length increasing and the cutting force in the Y tion was always larger than that in the X-direction. To be more specific, the cuttin in the Y direction increased from 11.21 N to 20.12 N with an increase of 79.5%, cutting force in the X direction increased from 17.29 N to 26.38 N with an increase o The growth rate of the cutting force in X and Y directions was different because th ured force in the X-direction included not only the X-direction component cuttin but also the friction force in the opposite direction of the feed rate caused by th force.…”

Section: Cutting Forcementioning

confidence: 99%

“…The low-pass filter method with a cutoff frequency of 10 Hz was used to process the original cutting force data in the X and Y directions [33,34], and the curve of the average cutting force of the tool with the cutting distance was extracted, as shown in Figure 2a,b. It could be clearly seen that the mean cutting force in both the X-direction and Y-direction increased sharply with the cutting length increasing and the cutting force in the Y-direction was always larger than that in the X-direction.…”

Section: Cutting Forcementioning

confidence: 99%

Research on Tool Wear and Surface Integrity of CFRPs with Mild Milling Parameters

Qiu

Zhang

et al. 2023

Coatings

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Carbon fiber-reinforced polymer composites are widely used in aviation and aerospace applications due to their excellent mechanical properties. Numerous studies have focused on tool wear at extreme machining parameters (f ≥ 540 mm/min) to accelerate tool wear experiments. However, using mild machining parameters is also instructive for current process production. To investigate the effect of wear on the surface integrity of CFRPs under mild parameters, a tool wear experiment on CFRPs was carried out at a conventional feed rate (f = 60 mm/min). The results show a cutting temperature increase of 47%, a cutting force increase of 53% and a flank wear width increase of 282% for PCD tools (with mild milling parameters) from 0 to 100 m of cutting length. As the cutting temperature reaches the glass transition temperature, Tg, during the stable wear stage, severe burrs and a large number of cavities appeared on the groove, and the subsurface with granular wear on flank wear land due to the transform of the matrix. Furthermore, Ra and Rz exhibit good indicators of cutting length and subsurface damage.

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“…Torque transducers are widely used in several mechanical processes, in order to control power transmission systems, improve machine performances and allow precise machinery assembly. Among others, torque measurements are used to investigate the cutting processes, in order to predict tool wear and breakage and correlate cutting parameters with cutting forces [1][2][3]. The measurement of the true torque at the tool's shaft, rather than its estimate from measurements at the electrical spindle, is useful for the accurate evaluation of the cutting process performance.…”

Section: Introductionmentioning

confidence: 99%

An optical torque transducer for high-speed cutting

Pantaleo

Pellerano

2006

Meas. Sci. Technol.

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In this paper, the design and manufacture of an angular twist-type optical torque transducer is described. It is based on the measurement of the angular twist of a stainless steel wire gauge proportional to the torque transmitted, through two optical reflective sensors. The main objective of this research is to provide a cost-effective, reliable and accurate torque transducer for high-speed wood cutting process analysis. After a description of the measurement methodology, the manufacturing of the transducer is discussed. The relative uncertainty in the measurement of the torque is 1% full scale, while the transducer bandwidth is 50 Hz.

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In-process drill wear and breakage monitoring for a machining centre based on cutting force parameters

Cited by 21 publications

References 4 publications

Back Propagation Wavelet Neural Network Based Prediction of Drill Wear from Thrust Force

Back Propagation Wavelet Neural Network Based Prediction of Drill Wear from Thrust Force

Research on Tool Wear and Surface Integrity of CFRPs with Mild Milling Parameters

An optical torque transducer for high-speed cutting

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