LSTM and CNN based tool wear state predicating method and device

A tool wear and prediction method technology, applied in manufacturing tools, measuring/indicating equipment, metal processing equipment, etc., can solve the problems of loss of original data sequence feature information, original data time series feature destruction, and difficulty in taking into account the extraction effect, etc., to achieve Improve prediction accuracy, improve learning speed and generalization ability, and improve prediction effect

Inactive Publication Date: 2020-09-22
CHONGQING UNIV
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Problems solved by technology

[0006] However, in the actual prediction process, the existing tool wear state prediction method still has the following problems: it first extracts the multi-dimensional features of the original data through the CNN network, and then extracts the time series features of the original data through the LSTM network, so that when extracting multiple When dimen

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  • LSTM and CNN based tool wear state predicating method and device
  • LSTM and CNN based tool wear state predicating method and device
  • LSTM and CNN based tool wear state predicating method and device

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Embodiment 1

[0067] This embodiment discloses a tool wear state prediction method based on LSTM and CNN.

[0068] Such as figure 1 Shown: A tool wear state prediction method based on LSTM and CNN, including the following steps:

[0069] Step A: collecting the original data matrix during the machining process of the machine tool, the original data matrix includes machine tool vibration data, tool cutting force data and high-frequency stress wave data;

[0070] Step B: Input the original data matrix into the LSTM network, extract the time series feature matrix of the original data matrix; then input the extracted time series feature matrix into the CNN network, and extract the time series feature corresponding to the original data matrix The multi-dimensional feature matrix of ;

[0071] Step C: Based on the multi-dimensional feature matrix containing time series features corresponding to the original data matrix, and the set mapping relationship, the predicted value of tool wear is calcul...

Embodiment 2

[0111] On the basis of the first embodiment, this embodiment further discloses a tool wear state prediction device based on LSTM and CNN.

[0112] Such as Figure 4 Shown: a tool 102 wear state prediction device based on LSTM and CNN, including a detection unit and a processing calculation unit; the detection unit is installed on the machine tool workbench 103, and is used to collect vibration data of the machine tool and cutting force data of the tool 102 respectively And collect the vibration sensor 4 of high-frequency stress wave data, force sensor 3, and acoustic emission sensor 5; Described vibration sensor 4, force sensor 3 and acoustic emission sensor 5 are all connected with processing computing unit signal, make corresponding data Send to the processing calculation unit; the processing calculation unit is used to receive the vibration data of the machine tool, the cutting force data of the tool 102 and the high-frequency stress wave data, and calculate the wear of the...

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Abstract

The invention relates to an LSTM and CNN based tool wear state predicating method and device. The method comprises the following steps: acquiring original data in a machine tool processing process, wherein the original data comprise machine tool vibration data, tool cutting force data and high-frequency stress wave data; inputting the original data into an LSTM network, and extracting time sequence characteristics of the original data; inputting the original data with time sequence characteristics extracted into the CNN network, and extracting multi-dimensional characteristics comprising the time sequence characteristics from the original data; and calculating to obtain a tool wear predicated value based on the multi-dimensional characteristics comprising the time sequence characteristicsand a set mapping relationship. The invention further discloses an LSTM and CNN based tool wear state predicating device. According to the LSTM and CNN based tool wear state predicating method and device disclosed by the invention, the extraction effect on the data multi-dimensional characteristics and the time sequence characteristics can be integrated, so that the tool wear state predicating effect can be improved.

Description

technical field [0001] The invention relates to the technical field of CNC machine tool tool identification, in particular to a tool wear state prediction method and device based on LSTM and CNN. Background technique [0002] CNC machine tool is the abbreviation of Computer numerical control machine tools, which is an automatic machine tool equipped with a program control system. CNC machine tools generally include the machine tool body, machine table, machine tool spindle and tools; and the prediction of tool wear status is very important, because excessive tool wear will increase the consumption of production resources and affect the quality of the workpiece. [0003] The existing tool wear state prediction method is to collect data reflecting tool wear through a variety of sensors, and analyze the collected data in time domain, frequency domain and time-frequency domain to extract features related to tool wear, such as mean Square root error value, variance, skewness, sp...

Claims

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Application Information

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IPC IPC(8): B23Q17/09
CPCB23Q17/0957
Inventor 何彦李育锋凌俊杰刘雪晖鄢萍吴鹏程王兴全
Owner CHONGQING UNIV
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