Research On The Intelligent Detection Method Of Metal Purity

With the development of technology,the demand for pure metals has increased exponentially and so has the production.However,in order to maximize profits,certain manufacturers have resorted to adulterating the manufacturing process of pure metals,thereby compromising their purity.Although these adulterated metals may be similar to their pure counterparts,they do not provide comparable benefits.At the same time,the booming economic boom has not only led to the expansion of the gold market,but has also triggered an unprecedented surge in the price of gold,making the market for gold-based trading more dynamic than ever before.As the gold market continues to develop,some traders have resorted to counterfeiting gold in pursuit of monetary gain,resulting in a plethora of fake gold.Currently,X-ray fluorescence techniques are commonly used for non-destructive and rapid metal purity testing.However,due to their high cost,they are not economically feasible for identifying the purity level of common metals.In addition,other spectral emission techniques may cause damage to the metal under test.Thus,this project investigates the intelligent metal purity testing method and proposes a metal purity testing system to achieve an intelligent and visualized metal purity testing process with high efficiency,real-time,and operability to better serve the field of intelligent metal purity testing.The advent of sensor network technology,big data analytics and artificial intelligence has opened up a whole new space of thought for metal purity testing.Integrating these cutting-edge technologies into existing purity testing processes has the potential to alleviate the long-standing challenge of accurately detecting metal purity,while improving testing efficiency and user experience.From the perspective of key technologies,BP-GA neural networks are used for nonlinear analog regression by optimizing the application of BP neural network models to improve the accuracy of sensors in achieving conversion between analog and digital quantities.The sensor adaptive frequency acquisition algorithm is investigated to change the sensing strategy of the sensor and adaptively adjust the next point of current acquisition to achieve dynamic update of the acquisition model and reduce the amount of transmitted data and redundancy of data.At the same time,the current stabilization is controlled by fuzzy PID to achieve a faster and stable current output.From a holistic perspective,an overall model of metal purity detection is constructed and studied from two aspects: hardware and software,respectively.The hardware part consists of sensors,communication nodes,and control circuit boards,which sense environmental information and collect data,and analyze and process the data;the software part uses a friendly human-computer interaction interface to complete the implementation of various functions.Half-duplex serial communication is used to communicate,and the serial communication protocol between the control board and the PC side is designed,and the communication instruction set is designed based on this,and a suitable serial communication method is adopted to complete the intelligent detection of metal purity.The research on the subject was completed by debugging the hardware and software and verifying the designed module.A method of intelligent metal purity detection is explored,which provides a practical solution for related research.