Application Research Of Solder Joint Quality Detection Algorithm Based On Deep Learning

With the advent of the information age,electronic devices have become an indispensable part of people's lives.As an independent industrial category,the production of electronic equipment has become the engine that drives the country's economic development.There is a rapidly rising demand for the quantity and quality of electronic devices.A large number of components are required for welding in the production of electronic equipment.The quality of the welding directly affects the quality,performance and cost of the product.Therefore,the quality inspection of solder joints directly affects the quality and production efficiency of the products.However,the solder joint quality inspection methods that have appeared so far rely on a large amount of manpower,which not only has low detection efficiency,but also requires enterprises to afford huge labor expenses.In recent years,the rapid development of Internet companies has made the traditional physical industry face a more severe living environment.As a typical example of labor-intensive enterprises,the competition among electronic manufacturers is even more intense.The factory has to reduce production costs,improve market competitiveness,and continue to develop toward automation and intelligence.Therefore,the enterprise is eager for a new cheap and efficient solderjoint quality inspection method.The development of artificial intelligence technology has provided a new solution to this problem.Especially in recent years.with the exponential growth of computing power of computer,artificial intelligence has surpassed humans in many fields,especially in terms of a lot of simple repetitive work.It is undoubtedly an efficient and feasible method to use artificial intelligence target detection algorithm instead of manpower to complete the quality detection of solder joints.Therefore,this paper is devoted to the study of a solder joint quality inspection method based on the deep learning target algorithm,which should have the advantages of high efficiency,high accuracy,low cost,easy to use and no special training for workers.To this end,this paper contacted the electronics manufacturer to obtain the solder joint data source,built the solder joint quality inspection dataset,and then improved the Faster R-CNN training process using our own dataset to achieve the solderjoint quality inspection task requirements and indicators,and then analyzes the problems that arise during the field deployment of the algorithm,and studies with the manufacturer to finally design an algorithm deployment solution that perfectly fits the existing production line.In order to optimize the algorithm with the infinite data in the industrial production process,this paper proposes an incremental learning method for the algorithm,which uses continuous new data to continuously expansion our dataset to make it more consistent with real data distribution,thus improving the accuracy of the detection algorithm rate continuously.