Research On Quantum Neural Network Model With Application To The Identification Of Weld Defects

Weld surface defects is an important factor that affects the quality of the weldment,and weld defect detection is very significant to improve the weld quality.The recognition rate of weld defects is directly related to the quality of the welded parts and the safety of construction.Traditional X-ray weld quality detection mainly rely on manual,which is influenced by their professional quality and the experience of the film,it has some disadvantages such as low efficiency,complex operation process,and the result is not objective.This paper takes the Daqing oil field storage tank as the research object.Based on improved quantum inspired neural network model,the defect identification schemes are designed,which can effectively reduce the error of manual evaluation,and realize the standardization and intelligence of welding defects identification.The main research contents include the following aspects:First,based on the basic idea of the combination of quantum computation and neural network,and the operation relationship of single-bit quantum rotation gates and multi-bit controlled Hadamard gates,the quantum-inspired neuron model is constructed and its input-output operation relation is deduced.Secondly,some features which effect of weld defect types are investigated.By selecting the valuable features of the defect and describing them in the form of quantum state,the sample information described by the real vector can be converted into the formal description of the quantum state and finally used as the input of the quantum neural network model.Thirdly,the topological structure of quantum neural networks are designed,and the learning algorithms are derived from the basic principle of quantum computation.Aiming at the practical problem of weld defect recognition,a novel solution scheme based on quantum-inspired neural networks is proposed.By taking the characteristic parameters influencing the weld defect type as the input,and taking the typical weld defect as the output,the highly complex nonlinear mapping relationships between weld defect types and various influencing factors can be obtained with the help of the highly efficient quantum computing mechanism.On this basis,the weld defect type identification can be completed.Fourthly,based on the above research results,a welding seam defect identification system is designed and implemented.The practical application of the system shows that the method based on quantum neural network is easy to operate,and its efficiency is fast and the identification result is satisfactory.The experimental results show that the proposed model and algorithm can help to identify the type of weld defects,and its recognition accuracy can reach about 57%.