Design Based on Industrial Material Inspection and Defect Analysis System

The industrial material inspection and defect analysis system is designed for material quality control and fault detection in industrial production. Physical properties of the system through the acquisition of steel as the experimental data set, in combination with particle swarm algorithm to optimize ParticleSwarmOptimization, (PSO) algorithm of BP neural network (BackpropagationNeuralNetwork) artificial intelligence, the analysis of testing data and forecast, Ability to quickly identify potential defects or failure hazards in materials. At the same time, this paper also studies the state transition of steel in the process of industrial production, and proposes the state monitoring based on FiniteStateMachine (FSM) to provide timely warning and solutions for the fault types predicted by the algorithm. This research is mainly to solve the problem of automatic defect monitoring and intelligent early warning of key manufacturing links in the industry, and proposes the combination of PSO-BP neural network algorithm and finite state machine, so as to improve the detection accuracy and problem-solving efficiency, reduce the maintenance cost and production interruption risk, and ensure the safety and reliability of industrial materials.