Real-time monitoring method of state of cutting tool for numerical control machining of complicated structural component based on deep learning

A deep learning and complex structure technology, applied in neural learning methods, manufacturing tools, metal processing equipment, etc., can solve problems such as poor adaptability of parts, heavy monitoring and learning workload, poor model generalization ability, etc., to achieve a wide range of applications, The effect of shortening the production cycle and reducing the production cost

Active Publication Date: 2018-01-16
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

[0006] The purpose of the present invention is to solve the problems of heavy workload of monitoring and lea

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  • Real-time monitoring method of state of cutting tool for numerical control machining of complicated structural component based on deep learning
  • Real-time monitoring method of state of cutting tool for numerical control machining of complicated structural component based on deep learning
  • Real-time monitoring method of state of cutting tool for numerical control machining of complicated structural component based on deep learning

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Embodiment Construction

[0029] The following structural drawings and embodiments further illustrate the present invention.

[0030] A deep learning-based real-time monitoring method for the tool status of CNC machining of complex structural parts, such as figure 1As shown, it includes the following specific steps as follows:

[0031] Step 1: Establish a training sample database; select typical sensor signal data, discretely store each type of signal data, and affix corresponding tool status labels as training samples;

[0032] Step 2: Construction and training of deep learning model; construct deep confidence network, input original signal data, output signal feature extraction results, use unlabeled training set samples to train network; construct convolutional neural network, input monitoring information matrix, output tool State identification results, using labeled training set samples to train the network;

[0033] Step 3: NC machining tool status monitoring; during NC machining, monitor the s...

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Abstract

The invention discloses a real-time monitoring method of the state of cutting tools for numerical control machining of complicated structural components based on deep learning. The method is characterized by comprising the following steps: constructing a two-level deep learning model including deep belief network and convolutional neural network, training a deep learning network based on a large number of numerical control machining monitoring signals, realizing real-time monitoring of the state of the cutting tools; firstly, adopting a large number of monitoring signal data to train the deepbelief network so as to realize automatic extraction of characteristics of the monitoring signals, constructing a signal characteristic input matrix, then establishing a relationship between the monitoring signals and process information and geometric information so as to construct the convolutional neural network, training the convolution neural network by a large amount of sample data, establishing the mapping relationship between monitoring information and the state of the cutting tools, finally, according to the real-time monitoring information during numerical control machining, determining the state of the cutting tools through the trained deep learning model. The method is suitable for monitoring the state of cutting tools for numerical control machining of complicated structural components in mass production of parts as well as in small batches or even single-piece production.

Description

technical field [0001] The invention belongs to the field of state monitoring of numerically controlled machining tools, and relates to a real-time monitoring technology of the state of numerically controlled machining tools, in particular to a method for real-time monitoring of the state of numerically controlled machining tools of complex structural parts based on deep learning. Background technique [0002] The state of the tool in the CNC machining process includes normal state, abnormal state such as wear, breakage, accidental drop, etc. The abnormal state of the tool will cause the surface quality of the part to deteriorate, the size is out of tolerance, cause chatter, affect the processing accuracy, and increase the processing speed. cost. The cost and production cycle problems caused by parts scrapping in the single-piece and small-batch production mode are far more serious than those in mass production. According to research, the use of CNC machine tools equipped w...

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Application Information

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IPC IPC(8): B23Q17/09G06N3/04G06N3/08
Inventor 李迎光刘长青华家玘牟文平
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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