Aero-engine blade defect quick and accurate recognition method based on artificial intelligence

An aero-engine and recognition method technology, applied in character and pattern recognition, computer parts, image data processing, etc., can solve the problems of low detection accuracy, overcome experience differences, improve training speed and accuracy, improve detection efficiency and The effect of accuracy

Active Publication Date: 2020-11-20
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0004] In order to overcome the missed detection and false detection caused by human factors such as human factors such as human factors such as eye fatigue and standard understanding caused by manual evaluation of blade CT images, and to avoid the problem of low detection accuracy in the past, the present invention proposes a The rapid and accurate identification method of aero-engine blade defe

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  • Aero-engine blade defect quick and accurate recognition method based on artificial intelligence
  • Aero-engine blade defect quick and accurate recognition method based on artificial intelligence
  • Aero-engine blade defect quick and accurate recognition method based on artificial intelligence

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[0043]In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, not to limit the present application.

[0044] In one embodiment, such as figure 1 As shown, a fast and accurate identification method for aero-engine blade defects based on artificial intelligence is proposed, including:

[0045] Step 1: Establish the turbine blade radiographic inspection image database, including the intact blade image database and the defective blade image database; the defect type and position in the digital image of each defective blade are calibrated in the defective blade image database.

[0046] In step 1, the digital images in the turbine blade radiography image database include digital images obtained ...

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Abstract

According to the aero-engine blade defect quick and accurate recognition method based on artificial intelligence, the turbine blade detection image database is constructed by adopting the manual calibration and data enhancement technology, the problem of small sample size is effectively solved, and the generalization ability of the model is improved. Based on an artificial intelligence deep learning method, a turbine blade defect detection and recognition network is constructed, the true and false defect recognition problem is solved by adopting technologies such as a maximum entropy principleacceleration strategy and the like, and the training speed and precision of the model are greatly improved. According to the detection method, the influence of human factors such as experience difference, artificial evaluation eye fatigue and standard understanding is effectively overcome, the standardized and intelligent targets of the ray detection work of the blade are achieved, the detectionefficiency and accuracy are greatly improved, and the production reliability of the turbine blade is guaranteed.

Description

technical field [0001] The invention belongs to the field of processing, manufacturing and quality inspection of aero-engine blades, and in particular relates to an artificial intelligence-based method for quickly and accurately identifying defects of aero-engine blades. Background technique [0002] Turbine blades are one of the core components of aero-engines. With the continuous improvement of aircraft performance, the requirements for reliability testing are becoming more and more stringent. Turbine blade defect detection has gradually developed from traditional film detection to current computerized ray tomography detection. At the same time, the requirements for objectivity, accuracy and reliability of ray detection are getting higher and higher. [0003] Due to the complex internal structure, the existing engine blades are generally formed by precision casting without margin. During the molding process, defects such as cracks, cold shuts, pores, slag inclusions, and ...

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Application Information

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IPC IPC(8): G06T7/00G06K9/62
CPCG06T7/0006G06T2207/10081G06F18/214G06F18/241
Inventor 肖洪王栋欢
Owner NORTHWESTERN POLYTECHNICAL UNIV
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