ReflowNet: ConvLSTM-based direct reflow oven recipe optimization framework

Jun Kataoka (), Abdelrahman Farrag (), Yangyang Lai (), Seungbae Park (), Yu Jin () and Daehan Won ()
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Jun Kataoka: State University of New York at Binghamton
Abdelrahman Farrag: State University of New York at Binghamton
Yangyang Lai: State University of New York at Binghamton
Seungbae Park: State University of New York at Binghamton
Yu Jin: State University at New York at Buffalo
Daehan Won: State University of New York at Binghamton

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 8, No 29, 5859-5873

Abstract: Abstract This paper presents ReflowNet, a domain-adaptive convolutional long short-term memory (ConvLSTM) neural network-based oven recipe optimization framework for reflow soldering, a critical step in printed circuit board (PCB) assembly using Surface Mount Technology (SMT). The proposed framework simultaneously utilizes synthetic datasets generated from physics-based computational fluid dynamics (CFD) simulations and real-world experimental trial data to predict the optimal oven recipe based on process-specific spatiotemporal information. ReflowNet addresses the limitations of previous methods by (I) considering deviations between simulation and experimental results, (II) directly predicting the oven recipe instead of the solder temperature profile, and (III) explicitly incorporating spatiotemporal information related to the reflow soldering process. Experimental results demonstrate that the proposed model accurately estimates the oven recipe and provides lower estimation variance across different recipe settings. By leveraging the power of domain adaptation (DA) and ConvLSTM network, ReflowNet offers a novel and effective solution for optimizing reflow soldering oven recipes in PCB assembly.

Keywords: Reflow soldering; Thermal profile; Computation fluid dynamics (CFD); Domain adaptation (DA); Deep learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-024-02505-0

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