Online monitoring method for abrasion forms and abrasion state of drill bit in high-temperature alloy drilling process

A technology of superalloy and wear state, applied in metal processing equipment, measuring/indicating equipment, metal processing machinery parts, etc., can solve problems such as poor applicability, and achieve the effect of good applicability and expanded applicability

Active Publication Date: 2019-02-15
NORTHWESTERN POLYTECHNICAL UNIV
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AI Technical Summary

Problems solved by technology

[0003] In order to overcome the shortcomings of poor applicability of the existing monitoring method for the wear state of the drill bit in the drilling

Method used

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  • Online monitoring method for abrasion forms and abrasion state of drill bit in high-temperature alloy drilling process
  • Online monitoring method for abrasion forms and abrasion state of drill bit in high-temperature alloy drilling process

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Embodiment Construction

[0018] refer to Figure 1-2 . The specific steps of the on-line monitoring method of the drill bit wear form and the wear state in the superalloy drilling process of the present invention are as follows:

[0019] Step 1. Data acquisition test of superalloy drilling wear.

[0020] A standard hard alloy twist drill with a diameter of φ10mm is used, and the drilling material is a GH4169 superalloy sheet with a thickness of 16mm. Set the speed range of the spindle to 500-800rpm, the feed range per revolution to 0.1-0.16mm / r, and drill through holes. Two-factor four-level orthogonal test design is adopted, in which the levels of spindle speed are 500rpm, 600rpm, 700rpm and 800rpm; the levels of feed per revolution are 0.1mm / r, 0.12mm / r, 0.14mm / r and 0.16mm / r. Under each set of drilling parameters, unworn drill bits were used to carry out continuous drilling tests, and the axial force and torque data were collected by a dynamometer during the drilling process. In the numerical ...

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Abstract

The invention discloses an online monitoring method for the abrasion forms and the abrasion state of a drill bit in the high-temperature alloy drilling process. The online monitoring method is used for solving the technical problem that an existing drilling process drill bit abrasion state monitoring method is poor in applicability. According to the technical scheme, signal features are extractedfrom the influence rule of drilling force signals based on the different drill bit abrasion forms of the high-temperature alloy drilling process, and a Bayesian network model of the drill bit abrasionforms and the signal features is set up through the drilling force and drill bit abrasion data based on the Bayesian theory on this basis; and then the drill bit abrasion forms are judged through Bayesian diagnosis and inference according to monitoring signals, and the signal features influencing the abrasion forms are acquired through Bayesian causal reasoning. Meanwhile, according to the tool abrasion curve rule, the signal features are monitored through an accumulation and control chart method, the target of monitoring the drill bit abrasion states in real time is achieved, and high practicality is achieved.

Description

technical field [0001] The invention relates to a method for monitoring the wear state of a drill bit in the drilling process, in particular to an on-line monitoring method for the wear form and wear state of the drill bit in the high temperature alloy drilling process. Background technique [0002] The document "Drill wear monitoring using back propagation neural network, Journal of Materials Processing Technology, 2006, Vol172(2), p283-290" discloses a method for monitoring the wear state of a drill bit during drilling based on a BP neural network. This method uses a standard three-layer BP neural network, takes spindle speed, feed rate, drill diameter, drilling axial force and torque, and chip thickness as the input nodes of the neural network, and takes the main cutting edge flank wear zone of the drill bit as the input node. Width is used as the output node, and the neural network model is trained using the drilling test data to accurately predict the wear state of the ...

Claims

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Application Information

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IPC IPC(8): B23Q17/09
CPCB23Q17/0957
Inventor 张定华韩策罗明吴宝海
Owner NORTHWESTERN POLYTECHNICAL UNIV
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