Drilling torque control using spindle motor current and its effect on tool wear

Oh, Young-Taig; Kwon, Won‐Tae; Chu, Chong Nam

doi:10.1007/s00170-002-1490-0

Cited by 33 publications

(18 citation statements)

References 7 publications

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“…This is an important gain in productivity. Furthermore, an additional technological advantage that follows from keeping an almost-constant cutting force during the cycle time is an extension of tool life as reported by Oh et al [10].…”

Section: Article In Pressmentioning

confidence: 87%

“…Typically, overshoots provoke instability when drilling depth exceeds 3-4 tool diameters. A similar paper by the same authors verifies the merits of drilling-process control in terms of tool wear [10]. A trial-and-error procedure is used for controller tuning in this second version.…”

Section: Introductionmentioning

confidence: 91%

“…Using this new proportional gain, simulation tests corroborate the elimination of overshoot; however, the transient response becomes very sluggish. Again, similar to the procedure of Oh et al [10], a trial-and-error tuning approach is chosen in order to minimize the overshoot and to provide faster response. The resulting simulation responses for various PID gain settings are shown in Fig.…”

Section: Controller Designmentioning

confidence: 99%

See 2 more Smart Citations

A classic solution for the control of a high-performance drilling process

Haber-Haber

Haber

Schmittdiel

et al. 2007

International Journal of Machine Tools and Manufacture

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Section: Article In Pressmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 91%

Section: Controller Designmentioning

confidence: 99%

See 1 more Smart Citation

A classic solution for the control of a high-performance drilling process

Haber-Haber

Haber

Schmittdiel

et al. 2007

International Journal of Machine Tools and Manufacture

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“…Jeong and Cho [9] used cutting torque from the rotating and stationary feed motor current during a milling process to estimate the tool breakage. Oh et al [10] estimated drilling torque using the RMS current of spindle motor and controlled the drilling torque. Wang et al [11, 12, and 13] have developed a self organizing map (neural network) and trained in a batch mode after each cutting passes against the flank wear.…”

Section: Introductionmentioning

confidence: 99%

Online Tool Wear Prediction in Milling Operation Using Feed Motor Current Signal

2016

Arimpie - 2016

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Abstract-In the present work, artificial neural network (ANN) and regression analysis are used to predict the real time flank wear of a cutting tool (KC710) in milling operation. The various parameters such as root mean square (RMS) value of the spindle motor current, spindle speed, depth of cut and feed-rate are the inputs to the network, and flank wear is the output. Online data of milling operation is taken for training and testing of the neural networks. Effect of various cutting conditions (speed, depth of cut, feed-rate) on the flank wear has been investigated. The flank wear prediction using the trained network is found to be in better agreement in comparison with the experimentally measured flank wear of the milling tool. The value of co-efficient of determination (R 2 ), between the model predictions and the experimentally obtained value are compared. Accuracy of the predicted flank wear using neural network is found to be better than that using regression model.

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“…Therefore, drill wear is a very important issue in manufacturing industries. Previous researchers have considered various kinds of signals as indicators of tool wear, such as cutting torque and thrust force [3] , acoustic emission [4] , and motor current [5] .…”

Section: Introductionmentioning

confidence: 99%

Wear state recognition of drills based on K-means cluster and radial basis function neural network

Yang

2010

Int. J. Autom. Comput.

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Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps: first, decomposing cutting torque components from the original signals by wavelet packet decomposition (WPD); second, extracting wavelet coefficients of different wear states (i.e., slight, normal, or severe wear) with signal features adapting to Welch spectrum. Finally, monitoring and recognition of the feature vectors of cutting torque signal are performed by using the K-means cluster and radial basis function neural network (RBFNN). The experiments on different tool wears of the multivariable features reveal that the results of monitoring and recognition are significant and effective.

show abstract

Drilling torque control using spindle motor current and its effect on tool wear

Cited by 33 publications

References 7 publications

A classic solution for the control of a high-performance drilling process

A classic solution for the control of a high-performance drilling process

Online Tool Wear Prediction in Milling Operation Using Feed Motor Current Signal

Wear state recognition of drills based on K-means cluster and radial basis function neural network

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