Research And Development Of Large Steel Components Quality Traceability System Based On The Iot

Compared with the traditional architecture, steel structure has many advantages, suchas high strength, light weight, good rigidity and the high speed of construction. So it hasbeen widely used in recent years. At the same time, its quality problems also continuouslyemerging. In addition to the installation quality, the quality problem of the steel members inprocessing directly influences the whole quality of the steel structure engineering. Becauseof the complex steel structure processing technology, the quality problem in the process ismore. And the reason for this is difficult to determine. With the progress of science and thedevelopment of information industry, quality tracking has been widely applied to define thequality responsibility, improve the quality of enterprise management level, improve thequality of the products. Therefore, how to build an efficient platform for the steel membersquality traceability is an urgent problem in the steel structure enterprise.With a large steel structure company as the research background, according to thecharacteristics of the steel structure production, combined with the advantage of the Internetof things application, steel members quality tracking system is designed. First of all, on thebasis of site investigation, analysis of steel structure construction enterprise businessprocess and production process, the common quality problems in the production of steelmembers are summarized. A quality information tracing system is designed. And the systemcan trace the information from every process of the steel members. According to thecharacteristics of the complex steel structure quality reasons, a steel structure qualityproblem tracing system is designed Based on the expert system. According to the qualityproblems, it can trace to the cause of the common quality problems. Welding quality is thekey to the quality of steel structure. The causes of the welding quality problems are verycomplex. So a RBF neural network is adopted to this system to trace the quality problem ofthe welding process. Basis function center, breadth, and weight is the key of RBF neuralnetwork. And it is difficult to determine the parameters. The three parameters are optimizedby genetic algorithm. And it is proved to be effective by simulation.Finally, the function modules, database, and the front desk interface of the steelmembers quality are designed. And the system has been put into use.