Reflow Oven Zone Temperature Advisor using the AI-Driven Smart Recipe Generator

This study introduces a novel method for expeditiously configuring the heating zone temperatures and conveyor speed of a reflow oven, particularly for thick PCBs with bulky components. Historically, determining an appropriate reflow recipe for a new PCB assembly has relied heavily on expertise and experience. However, the author has developed an application utilizing AI algorithms that can efficiently identify suitable recipes based on the heat capacities of the PCB and the largest component. The proposed method utilizes regression models as the backbone of the APP. These models predict the temperature changes of the Printed Circuit Board Assemblies (PCBAs) in a step-by-step manner, resembling the transient simulation method. The output temperature from each step serves as the input for the next step in the process. With a step size of one second, the machine learning models act as heat transfer coefficients for forced convection and transient conduction, which are the dominant mechanisms in the reflow soldering process. This approach aims to reduce labor costs and time in seeking the proper reflow recipe for new PCB assemblies. The measurement using the profiling board is not meant to be replaced by using the Smart Recipe Generator, but it is recommended for every convection multi-zone oven to utilize the APP for its benefits when reflowing complex assemblies.