Stencil Printing Process Modeling and Control Using Statistical Neural Networks

Barajas, Leandro G.; Egerstedt, Magnus; Kamen, Edward W.; Goldstein, Alex

doi:10.1109/tepm.2007.914236

Cited by 30 publications

(15 citation statements)

References 21 publications

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“…The distribution of the selected articles in different years and different stages are illustrated in Figure 3. It can be seen that 17% (17 articles) were related to the stage of product and manufacturing process design [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42], and more than 75% (80 articles) applied DM and Big Data to production management and control in the stage of production , but less than 8% (8 articles) of applications focused on the stage of sale, service, and recycling [123][124][125][126][127][128][129][130]. The fluctuation in quantity of the selected articles in different years presents no obvious tendency, however, it indicates that the topic has attracted ongoing attention and research during the past decades, and the application areas have been extended and many new approaches have been developed.…”

Section: Article Selection and Distributionmentioning

confidence: 99%

“…(2) The optimization of the manufacturing process parameter is the main task of process planning, such as the parameter optimization of stencil printing process (SPP) [26,27,36], reflow soldering [29,31,32], fluid dispensing for microchip encapsulation [33], wave soldering [35,41], and hot solder dip [39] for component surface mounts on PCBs. These models always combined ANN, SVR, and regression for the quality prediction with GA for parameters optimization [26,[31][32][33].…”

Section: Application Of Dm and Big Data For Designmentioning

confidence: 99%

“…(3) KD-oriented ANN is the widely used DMT. The pervasive function is prediction and has been widely employed for parameter optimization and determination of its effect [26,27,[32][33][34][35][36] followed by clustering and association. Clustering was mainly employed to identify similarity products, process plans, and parameters and no supervision classification was conducted to support more efficient and reasonable manufacturing [39][40][41].…”

Section: Application Of Dm and Big Data For Designmentioning

confidence: 99%

See 2 more Smart Citations

A Review of Data Mining with Big Data towards Its Applications in the Electronics Industry

Kim

Zheng

et al. 2018

Applied Sciences

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Data mining (DM) with Big Data has been widely used in the lifecycle of electronic products that range from the design and production stages to the service stage. A comprehensive analysis of DM with Big Data and a review of its application in the stages of its lifecycle will not only benefit researchers to develop strong research themes and identify gaps in the field but also help practitioners for DM application system development. In this paper, a brief clarification of DM-related topics is presented first. A flowchart of DM and the main content of the flowchart steps are given in which commonly used data preparation and preprocessing approaches, DM functions and techniques, and performances indicators are summarized. Then, a comprehensive review covering 105 articles from 2007 to 2017 on DM or Big Data applications in the electronics industry is provided according to the flowchart from various points of view such as data handling, applications of DM, or Big Data at different lifecycle stages, and the software used in the applications. On this basis, a diagram of data content for different knowledge areas and a framework for DM and Big Data applications in the electronics industry are established. Finally, conclusions and future research directions are given.

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Section: Article Selection and Distributionmentioning

confidence: 99%

Section: Application Of Dm and Big Data For Designmentioning

confidence: 99%

Section: Application Of Dm and Big Data For Designmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Data Mining with Big Data towards Its Applications in the Electronics Industry

Kim

Zheng

et al. 2018

Applied Sciences

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show abstract

“…Since the reflow process has a certain randomness which is affected by various uncertainties and the environment, it is necessary for reflow profile setting and analysis from the perspective of reliability. Considering the complexity of reflow process [3,4], the paper conducts the study with the production examples from a company in Kunshan.…”

Section: Introductionmentioning

confidence: 99%

Reflow Process Parameters Analysis and Reliability Prediction Considering Multiple Characteristic Values

et al. 2016

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Abstract. As a major step surface mount technology, reflow process is the key factor affecting the quality of the final product. The setting parameters and characteristic value of temperature curve shows a nonlinear relationship. So parameter impacts on characteristic values are analyzed and the parameters adjustment process based on orthogonal experiment is proposed in the paper. First, setting parameters are determined and the orthogonal test is designed according to production conditions. Then each characteristic value for temperature profile is calculated. Further, multi-index orthogonal experiment is analyzed for acquiring the setting parameters which impacts the PCBA product quality greater. Finally, reliability prediction is carried out considering the main influencing parameters for providing a theoretical basis of parameters adjustment and product quality evaluation in engineering process.

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“…However, physical models usually consist of partial differential equations with respect to both process variables and process responses, which may not be developed easily due to the complex behaviour of certain manufacturing processes. Implicit process modelling approaches such as neural network modelling (Li et al 2006, Barajas et al 2008, Liukkonen et al 2009), fuzzy logic modelling (Kang 1993, Xie and Lee 1994, Babets et al 2000 and fuzzy neural network modelling (Giaquinto et al 2009) can be used for generating implicit process models based on experimental data. These implicit process models consist of all equations in process simulators with an inputoutput black-box structure.…”

Section: Introductionmentioning

confidence: 99%

Handling uncertainties in modelling manufacturing processes with hybrid swarm intelligence

Chan

Dillon

Kwong

2012

International Journal of Production Research

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Seldom has research regarding manufacturing process modelling considered the two common types of uncertainties which are caused by randomness as in material properties and by fuzziness as in the inexact knowledge in manufacturing processes. Accuracies of process models can be downgraded if these uncertainties are ignored in the development of process models. In this paper, a hybrid swarm intelligence algorithm for developing process models which intends to achieve significant accuracies for manufacturing process modelling by addressing these two uncertainties is proposed. The hybrid swarm intelligence algorithm first applies the mechanism of particle swarm optimisation to generate structures of process models in polynomial forms, and then it applies the mechanism of fuzzy least square regression algorithm to determine fuzzy coefficients on polynomials so as to address the two uncertainties, fuzziness and randomness. Apart from addressing the two uncertainties, the common feature in manufacturing processes, nonlinearities between process parameters, which are not inevitable in manufacturing processes, can also be addressed. The effectiveness of the hybrid swarm algorithm is demonstrated by modelling of the solder paste dispensing process.

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Stencil Printing Process Modeling and Control Using Statistical Neural Networks

Cited by 30 publications

References 21 publications

A Review of Data Mining with Big Data towards Its Applications in the Electronics Industry

A Review of Data Mining with Big Data towards Its Applications in the Electronics Industry

Reflow Process Parameters Analysis and Reliability Prediction Considering Multiple Characteristic Values

Handling uncertainties in modelling manufacturing processes with hybrid swarm intelligence

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