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ReflowNet: ConvLSTM-based direct reflow oven recipe optimization framework.

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Title: ReflowNet: ConvLSTM-based direct reflow oven recipe optimization framework.
Authors: Kataoka, Jun1 (AUTHOR) jkataok1@binghamton.edu, Farrag, Abdelrahman1 (AUTHOR) afarrag1@binghamton.edu, Lai, Yangyang2 (AUTHOR) ylai9@binghamton.edu, Park, Seungbae2 (AUTHOR) sbpark@binghamton.edu, Jin, Yu3 (AUTHOR) yujin@buffalo.edu, Won, Daehan1 (AUTHOR) dhwon@binghamton.edu
Source: Journal of Intelligent Manufacturing. Dec2025, Vol. 36 Issue 8, p5859-5873. 15p.
Subjects: Surface mount technology, Solder & soldering, Machine learning, Spatiotemporal processes, Long short-term memory, Printed circuit manufacturing
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. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Intelligent Manufacturing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Intelligent+Manufacturing%22">Journal of Intelligent Manufacturing</searchLink>. Dec2025, Vol. 36 Issue 8, p5859-5873. 15p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Surface+mount+technology%22">Surface mount technology</searchLink><br /><searchLink fieldCode="DE" term="%22Solder+%26+soldering%22">Solder & soldering</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Spatiotemporal+processes%22">Spatiotemporal processes</searchLink><br /><searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Printed+circuit+manufacturing%22">Printed circuit manufacturing</searchLink>
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  Label: Abstract
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Intelligent Manufacturing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10845-024-02505-0
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      – Code: eng
        Text: English
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        PageCount: 15
        StartPage: 5859
    Subjects:
      – SubjectFull: Surface mount technology
        Type: general
      – SubjectFull: Solder & soldering
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Spatiotemporal processes
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      – SubjectFull: Long short-term memory
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      – SubjectFull: Printed circuit manufacturing
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      – TitleFull: ReflowNet: ConvLSTM-based direct reflow oven recipe optimization framework.
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            NameFull: Kataoka, Jun
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              M: 12
              Text: Dec2025
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              Y: 2025
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