Determination of Smarter Reflow Profile to Achieve a Uniform Temperature Throughout a Board

Lai, Yangyang; Pan, Ke; Xu, Jiefeng; Yang, Junbo; Park, Seungbae

doi:10.1109/itherm51669.2021.9503258

Cited by 15 publications

(2 citation statements)

References 17 publications

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“…Abdul Aziz presents an effective numerical method to study pin shape during wave soldering (Abdul Aziz et al, 2014). Lai studied the influence of uneven thermal mass distribution during reflow (Lai et al, 2021(Lai et al, , 2022a. Whalley presented a very simple approach to model the reflow process and got an extremely accurate prediction (Whalley, 2004).…”

Section: Introductionmentioning

confidence: 99%

Reflow profiling with the aid of machine learning models

Lai

Park

2023

SSMT

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Purpose This paper aims to propose a method to quickly set the heating zone temperatures and conveyor speed of the reflow oven. This novel approach intensely eases the trial and error in reflow profiling and is especially helpful when reflowing thick printed circuit boards (PCBs) with bulky components. Machine learning (ML) models can reduce the time required for profiling from at least half a day of trial and error to just 1 h. Design/methodology/approach A highly compact computational fluid dynamics (CFD) model was used to simulate the reflow process, exhibiting an error rate of less than 1.5%. Validated models were used to generate data for training regression models. By leveraging a set of experiment results, the unknown input factors (i.e. the heat capacities of the bulkiest component and PCB) can be determined inversely. The trained Gaussian process regression models are then used to perform virtual reflow optimization while allowing a 4°C tolerance for peak temperatures. Upon ensuring that the profiles are inside the safe zone, the corresponding reflow recipes can be implemented to set up the reflow oven. Findings ML algorithms can be used to interpolate sparse data and provide speedy responses to simulate the reflow profile. This proposed approach can effectively address optimization problems involving multiple factors. Practical implications The methodology used in this study can considerably reduce labor costs and time consumption associated with reflow profiling, which presently relies heavily on individual experience and skill. With the user interface and regression models used in this approach, reflow profiles can be swiftly simulated, facilitating iterative experiments and numerical modeling with great effectiveness. Smart reflow profiling has the potential to enhance quality control and increase throughput. Originality/value In this study, the employment of the ultimate compact CFD model eliminates the constraint of components’ configuration, as effective heat capacities are able to determine the temperature profiles of the component and PCB. The temperature profiles generated by the regression models are time-sequenced and in the same format as the CFD results. This approach considerably reduces the cost associated with training data, which is often a major challenge in the development of ML models.

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Section: Introductionmentioning

confidence: 99%

Reflow profiling with the aid of machine learning models

Lai

Park

2023

SSMT

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“…Shen conducted a finite element method simulation to investigate the solder temperatures of a BGA package (Shen et al, 2005). Lai used the computational fluid dynamics (CFD) model to analyze the temperatures of solder balls of a BGA assembly during the reflow soldering process (Lai et al, 2021). Shao used The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/0954-0911.htm the CFD model to obtain the working status temperature distribution of a 2.5 D package and mapped it to the finite element analysis model to observe component warpage (Shao et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

An intelligent system for reflow oven temperature settings based on hybrid physics-machine learning model

Lai

Pan

Cen

et al. 2022

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Purpose This paper aims to provide the proper preset temperatures of the convection reflow oven when reflowing a printed circuit board (PCB) assembly with varied sizes of components simultaneously. Design/methodology/approach In this study, computational fluid dynamics modeling is used to simulate the reflow soldering process. The training data provided to the machine learning (ML) model is generated from a programmed system based on the physics model. Support vector regression and an artificial neural network are used to validate the accuracy of ML models. Findings Integrated physical and ML models synergistically can accurately predict reflow profiles of solder joints and alleviate the expense of repeated trials. Using this system, the reflow oven temperature settings to achieve the desired reflow profile can be obtained at substantially reduced computation cost. Practical implications The prediction of the reflow profile subjected to varied temperature settings of the reflow oven is beneficial to process engineers when reflowing bulky components. The study of reflowing a new PCB assembly can be started at the early stage of board design with no need for a physical profiling board prototype. Originality/value This study provides a smart solution to determine the optimal preset temperatures of the reflow oven, which is usually relied on experience. The hybrid physics–ML model providing accurate prediction with the significantly reduced expense is used in this application for the first time.

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