Plasma arc welding perforation state prediction method and system based on molten pool image
A plasma arc and prediction method technology, applied in image enhancement, image analysis, image data processing and other directions, can solve the problems of low accuracy, prone to over-fitting, lack of practical value, etc., to achieve improved performance and improved robustness. Effect
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Embodiment 1
[0032] This embodiment provides a plasma arc welding perforation state prediction method based on molten pool images;
[0033] A method for predicting the state of perforation in plasma arc welding based on molten pool images, including:
[0034] S101: Obtain an image of a plasma arc welding molten pool with a known perforation state; preprocess the acquired image, and divide it into training data, verification data, and test data;
[0035] S102: Define M improved backbone networks, and each improved backbone network removes the last fully connected layer and output layer of the original backbone network; each improved backbone network is connected with a Dense-GAP-Dense structure to form a prediction Model; define N optimizers; combine M prediction models and N optimizers to obtain M*N groups of prediction models; where M and N are both positive integers;
[0036] S103: After training, verification and testing, select the prediction model with the highest prediction accuracy...
Embodiment 2
[0094] This embodiment provides a plasma arc welding perforation state prediction system based on molten pool images;
[0095] Plasma arc welding perforation state prediction system based on molten pool image, including:
[0096] An image preprocessing module, which is configured to: acquire a plasma arc welding molten pool image of a known perforation state; preprocess the acquired image, and divide it into training data, verification data, and test data;
[0097] A network building block configured to: define M improved backbone networks, each of which removes the last fully connected layer and output layer of the original backbone network; each improved backbone network is combined with Dense-GAP- The Dense structure is connected to form a forecasting model; define N optimizers; combine M forecasting models and N optimizers to obtain M*N sets of forecasting models; where M and N are both positive integers;
[0098] The model screening module is configured to: select the pr...
Embodiment 3
[0104] This embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, the processor is connected to the memory, and the one or more computer programs are programmed Stored in the memory, when the electronic device is running, the processor executes one or more computer programs stored in the memory, so that the electronic device executes the method described in Embodiment 1 above.
[0105] It should be understood that in this embodiment, the processor can be a central processing unit CPU, and the processor can also be other general-purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic devices , discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, o...
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