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Optimization of Temperature Curve of Reflow Welding Furnace Based on Machine Learning

The surface assembly of welding furnace is an important part in the production of electronic products such as integrated circuit board. The accurate control of the process temperature of each part of the welding furnace is directly related to the quality of the finished product. Minimal error can lead to virtual welding, plate burning and other problems. At present, the research on this problem focuses on the influence of process parameters on the temperature curve, and the direct control of welding furnace temperature is insufficient. Based on the artificial intelligence algorithm, the temperature determination of welding furnace is divided into four angles, from the establishment of mathematical model of temperature change in welding area under given temperature zone to the optimization of furnace temperature curve under given limit conditions. The temperature control problems in welding furnace production are solved by model step by step.