Experimental measurements of the shear force on surface mount components simulating the wave soldering process

Carvalho, Violeta; Arcipreste, Bruno; Soares, Delfim; Ribas, Luís; Rodrigues, Nélson; Teixeira, Senhorinha; Teixeira, José Carlos

doi:10.1108/ssmt-12-2020-0057

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…Cen et al (2022) presented P&P defect patterns for different root causes based on automated optical inspection data and developed a root cause identification model using a machine learning. Carvalho et al (2022) presented a study to determine the minimum force required to pull out a surface mount component from a PCB during the wave soldering process. Table 1 shows a comparative analysis of the research work done on self-alignment prediction for SMT.…”

Section: Related Workmentioning

confidence: 99%

Parameter optimization for surface mounter using a self-alignment prediction model

Mistry

Gupta

Jain

et al. 2022

SSMT

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Purpose The purpose of this paper is to develop a machine learning model that predicts the component self-alignment offsets along the length and width of the component and in the angular direction. To find the best performing model, various algorithms like random forest regressor (RFR), support vector regressor (SVR), neural networks (NN), gradient boost (GB) and K-nearest neighbors (KNN) were performed and analyzed. The models were implemented using input features, which can be categorized as solder paste volume, paste-pad offset, component-pad offset, angular offset and orientation. Design/methodology/approach Surface-mount technology (SMT) is the technology behind the production of printed circuit boards, which is used in several types of commercial equipment such as communication devices, home appliances, medical imaging systems and sensors. In SMT, components undergo movement known as self-alignment during the reflow process. Although self-alignment is used to decrease the misalignment, it may not work for smaller size chipsets. If the solder paste depositions are not well-aligned, the self-alignment might deteriorate the final alignment of the component. Findings It were trained on their targets. Results obtained by each method for each target variable were compared to find the algorithm that gives the best performance. It was found that RFR gives the best performance in case of predicting offsets along the length and width of the component, whereas SVR does so in case of predicting offsets in the angular direction. The scope of this study can be extended to developing this model further to predict defects that can occur during the reflow process. It could also be developed to be used for optimizing the placement process in SMT. Originality/value This paper proposes a predictive model that predicts the component self-alignment offsets along the length and width of component and in the angular direction. To find the best performing model, various algorithms like RFR, SVR, NN, GB and KNN were performed and analyzed for predicting the component self-alignment offsets. This helps to achieve the following research objectives: best machine learning model for prediction of component self-alignment offsets. This model can be used to optimize the mounting process in SMT, which reduces occurrences of defects and making the process more efficient.

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Section: Related Workmentioning

confidence: 99%

Parameter optimization for surface mounter using a self-alignment prediction model

Mistry

Gupta

Jain

et al. 2022

SSMT

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“…Traditional electronic component soldering mainly includes soldering iron, wave soldering, reflow soldering and other methods [ 1 ]. However, these electronic assembly methods can interfere with the surrounding components, resulting in a significantly insufficient soldering yield, especially for microelectronic components.…”

Section: Introductionmentioning

confidence: 99%

Path Planning of Laser Soldering System Based on Intelligent Algorithm

Zhang

Fan

Dai

et al. 2022

Sensors

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Laser soldering has been gradually applied to the soldering of electronic components due to the rapid development of microelectronics. However, it is inefficient to use a mechanical shaft to move a laser beam. Here, a laser soldering system is constructed using galvanometer scanning, and an intelligent algorithm is also introduced to optimize the soldering path. Firstly, a laser soldering system for scanning of galvanometers is established, and the functions of visual monitoring, motion planning and parameter integration are presented. Secondly, the position of the laser beam and the corresponding soldering spot are determined, and the coordinate information is provided to plan a route by camera calibration and coordinate system transformation. Finally, the problem of path planning in this system is decomposed into the generation of the soldering point full coverage processing frame, and the route optimization of processing platform and laser beam motion. Furthermore, an improved clustering algorithm, based on the characteristics of system structure, and a hybrid optimization algorithm are designed to deal with the generation of the soldering point full coverage processing frame, the route optimization of processing platform and laser beam motion. In addition, the simulations and experiments are verified by test board. These findings shown that the established system and designed optimization algorithm can promote the efficiency of laser soldering.

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Experimental measurements of the shear force on surface mount components simulating the wave soldering process

Cited by 2 publications

References 17 publications

Parameter optimization for surface mounter using a self-alignment prediction model

Parameter optimization for surface mounter using a self-alignment prediction model

Path Planning of Laser Soldering System Based on Intelligent Algorithm

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