Friction stir welding diagnosis method based on YOLO, terminal and storage medium
A technology of friction stir welding and diagnostic methods, applied in neural learning methods, image data processing, instruments, etc., can solve the problem of high cost of money, time, and labor, increased production and management costs of enterprises, and the inability to monitor the macroscopic quality of friction stir welding, etc. problems, to achieve the effects of improving production efficiency, real-time monitoring of appearance quality, and fast computing speed
Pending Publication Date: 2022-04-29
广州瑞松威尔斯通智能装备有限公司
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Problems solved by technology
On the one hand, engineers who lack knowledge are often unable to monitor the macroscopic quality of friction stir welding in time; The labor cost is very high, which adds a certain amount of production and management costs to the enterprise
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[0023] Embodiments of the present application are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific implementation modes, and various modifications or changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the various embodiments of the present disclosure generally described and shown in the drawings herein can be combined with each other under the premise of no conflict, and the structural components or functional modules can be arranged and arranged in various configurations. design. Accordingly, the following detailed description of the embodiments of the present disclosure provided in the accompanying d...
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The invention provides a friction stir welding diagnosis method based on YOLO, a terminal and a storage medium, and the diagnosis method comprises the following steps: S101, obtaining a weld joint picture of friction stir welding, and preprocessing the weld joint picture to generate a data set; s102, importing the data set into a YOLO target detection network for model training, and adjusting training parameters of the YOLO target detection network; and S103, according to a defect identification result of the target detection model formed by training, adjusting operation parameters of the model so as to obtain an appearance defect detection model, and identifying the appearance defect of the welding seam through the appearance defect detection model. The characteristics that the YOLO target detection network is high in operation speed, high in robustness and good at detecting small targets are utilized, the trained model can accurately recognize the appearance defects of friction stir welding, the appearance quality of welding seams can be monitored in real time on an industrial site in the mode of being combined with the machine vision technology, the production efficiency is improved, and the production cost is reduced.
Description
technical field [0001] The invention relates to the field of weld appearance defect detection, in particular to a YOLO-based friction stir welding diagnosis method, a terminal and a storage medium. Background technique [0002] As an important connection technology, welding technology has played an irreplaceable role in the development of the manufacturing industry. It is an important guarantee to ensure the safety, quality and reliability of the final products of the manufacturing industry. In recent years, with the rapid rise of new energy and other industries, friction stir welding, as an environmentally friendly and reliable solid-phase joining technology for light alloys, has become an eye-catching new welding technology. [0003] With the further popularization of friction stir welding, the lack of professionals with professional knowledge related to friction stir welding has become a shackle for the further promotion of this technology. On the one hand, engineers who...
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Patent Timeline
Login to View More IPC IPC(8): G06T7/00G06V10/762G06V10/774G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06T7/0004G06N3/04G06N3/08G06T2207/20081G06T2207/20084G06T2207/20104G06T2207/30152G06F18/23G06F18/214
Inventor 黎子浩何艳兵辛志
Owner 广州瑞松威尔斯通智能装备有限公司