Turning chatter detection method

A detection method and flutter technology, which are applied in measurement/indication equipment, metal processing mechanical parts, metal processing equipment, etc., can solve the problems of the impact of flutter recognition accuracy algorithm execution speed and high computational complexity.

Active Publication Date: 2015-12-02
SHANGHAI JIAO TONG UNIV
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Problems solved by technology

The second category is statistical methods, such as permutation entropy, approximate entropy, etc. The calculation of entropy in this type of method has high computational complexit

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Embodiment Construction

[0070] figure 1 For the overall implementation flow chart of the present invention, the fast and effective flutter identification method of the present invention comprises the following steps:

[0071] Step 1: First, obtain the cutting force signal in the stable turning state through the turning experiment, and then use larger cutting parameters in the experiment to stimulate the chatter state to obtain the corresponding force signal, which is used as offline data.

[0072] Step 2: Perform windowing on the original signal, and take 1024 points without overlap as a data processing unit. For a data unit signal f 0 1 (t) Carry out wavelet packet decomposition according to the following formula:

[0073]

[0074] where h(k) and g(k) are low-pass and high-pass filter coefficients respectively, f j i is the wavelet coefficient of the i-th node in the j-th layer. Repeat the above decomposition steps until the sixth layer is decomposed, and the wavelet coefficient f of each n...

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Abstract

The invention discloses a turning chatter detection method, and relates to the technical field of detection. In the turning process, the state of a machine tool can be reflected in dynamic cutting force. The turning chatter detection method includes the steps that firstly, an off-line data training model is used, force signals are decomposed to a sixth layer through wavelet packet transformation, energy of each node is worked out, and a 64-dimension feature vector is obtained; dimensionality reduction is conducted on the feature vector through least squares support vector machine-regression feature elimination (LSSVM-RFE), redundancy features are eliminated continuously, optimal features are selected out, and a least squares support vector machine classifier is trained according to the optimal features; and each selected feature corresponds to one wavelet packet node, in the on-line detection process, only a small wavelet packet matrix is needed to decompose force signals to the small wavelet packet nodes selected in the off-line training process, the feature vector is built and input into the classifier, and a detection result is obtained. By the adoption of the dimensionality reduction method, the turning chatter detection method has the beneficial effects of being high in speed and high in identifying accuracy and effectively guaranteeing the machining safety and the product quality.

Description

technical field [0001] The invention relates to the technical field of fault detection, in particular to the detection technology of turning vibration of numerical control machine tools. Background technique [0002] Cutting chatter is a dynamic instability phenomenon in the closed-loop cutting system of machine tools. It is a severe vibration that occurs between the cutting tool and the workpiece. The occurrence of chatter will affect production efficiency and processing quality, and it can also cause excessive noise, tool damage, etc. The harm to product quality, tool and machine tool equipment is beyond doubt. The turning state of the CNC machine tool can be reflected in the vibration signal of the machine tool. By detecting the state of the machine tool and implementing the corresponding control strategy, the quality of the processed product can be effectively guaranteed, the production efficiency can be improved, and the tool wear can be reduced at the same time. With ...

Claims

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Application Information

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IPC IPC(8): B23Q17/12
CPCB23Q17/12
Inventor 钱士才熊振华孙宇昕朱向阳
Owner SHANGHAI JIAO TONG UNIV
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