With the expansion and deepening of the field of ocean engineering,ship lifting robotic arms have become a key technical support for ocean operations.In order to ensure the welding quality of the robotic arm's welding joints and re-duce the risk of marine operations,a defect intelligent recognition model for spot welding joint ultrasonic non-destructive testing was designed using neural networks as technical support.The experimental results show that the optimization al-gorithm designed for the ultrasonic non-destructive intelligent testing model has a minimum optimization value of 4.804E-11 on the unimodal testing function.The maximum optimized hyper volume is 0.954,and the minimum distance from the true frontier solution is 0.203.The improved detection model has a maximum F1 value of 0.946 and a minimum loss value of 0.07,indicating strong classification detection ability.This method can well fit the characteristics of ultrasonic signals,ef-fectively distinguish different defects in welded joints,and distinguish between qualified welding and defective welding.The research and design of an ultrasonic non-destructive defect intelligent detection and recognition model effectively ensures the welding quality of ship lifting robotic arms,meeting the high requirements of marine engineering for the welding of ship lift-ing robotic arms.
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