Process optimization of SnCuNi soldering material using artificial parametric design

Huang, Chien‐Yi; Huang, Hui-Hua

doi:10.1007/s10845-012-0720-z

Cited by 15 publications

(5 citation statements)

References 25 publications

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“…Additionally, in (Jha et al 2014), the GA is embedded inside the ANN to optimize the synaptic weights between neurons. For the soldering operation, two approaches, one integrating the principal component analysis and grey relational analysis, and the other combining the ANN with GA, were investigated for their resultant quality characteristics (Huang and Huang 2014). Moreover, in (Huang and Huang 2014), the ANN is embedded inside the GA to produce the predicted process output.…”

Section: Related Workmentioning

confidence: 99%

Hybrid evolutionary optimization for nutraceutical manufacturing processes

2015

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In this paper, an intelligent approach, called HERON (hybrid evolutionary optimization for nutraceutical manufacturing), is proposed to optimize a variety of manufacturing processes in the nutraceutical field. The approach integrates the Taguchi method, an artificial neural network (ANN), and a genetic algorithm (GA). The Taguchi method is used to cost-effectively gather the data on the process parameters. Data obtained by the Taguchi method are divided into input and output data for an ANN's input and output parameters, respectively. The ANN trains itself to develop the relationship between its input and output parameters. The trained ANN is then integrated into a GA as the fitness function, such that the GA can evolutionarily obtain the optimal process parameters. The HERON is validated through a manufacturing process on soft-shell turtle soft-capsules. The objective is to minimize the soft-capsule defect rate. Compared to the defect rates obtained by the empirical and Taguchi methods, the HERON reduces the defect rate by 43.75 and 32.5 %, respectively. In addition, compared to the manufacturing costs obtained by the empirical and Taguchi methods, the HERON reduces the manufacturing cost by 11.81 and 25.29 %, respectively.

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Section: Related Workmentioning

confidence: 99%

Hybrid evolutionary optimization for nutraceutical manufacturing processes

2015

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“…The relationship between these factors on the soldering quality is also non-linear, so BPartificial neural network (BP-ANN) technology can be applied in lead-free soldering. Huang and Huang (2014) combined BP-ANN and genetic algorithms to determine reasonable process parameters for the SnCuNi soldering material. Péter and Oliver (2019) used an artificial neural network (ANN), an adaptive neuro-fuzzy inference system, to evaluate the hole filling aspects of solder paste technology, and the predictions obtained help to improve the reliability of "pluggable production."…”

Section: Introductionmentioning

confidence: 99%

Prediction of viscosity of ternary tin-based lead-free solder melt using BP neural network

2020

SSMT

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Purpose Viscosity is an important basic physical property of liquid solders. However, because of the very complex nonlinear relationship between the viscosity of the liquid ternary Sn-based lead-free solder and its determinants, a theoretical model for the viscosity of the liquid Sn-based solder alloy has not been proposed. This paper aims to address the viscosity issues that must be considered when developing new lead-free solders. Design/methodology/approach A BP neural network model was established to predict the viscosity of the liquid alloy and the predicted values were compared with the corresponding experimental data in the literature data. At the same time, the BP neural network model is compared with the existing theoretical model. In addition, a mathematical model for estimating the melt viscosity of ternary tin-based lead-free solders was constructed using a polynomial fitting method. Findings A reasonable BP neural network model was established to predict the melt viscosity of ternary tin-based lead-free solders. The viscosity prediction of the BP neural network agrees well with the experimental results. Compared to the Seetharaman and the Moelwyn–Hughes models, the BP neural network model can predict the viscosity of liquid alloys without the need to calculate the relevant thermodynamic parameters. In addition, a simple equation for estimating the melt viscosity of a ternary tin-based lead-free solder has been proposed. Originality/value The study identified nine factors that affect the melt viscosity of ternary tin-based lead-free solders and used these factors as input parameters for BP neural network models. The BP neural network model is more convenient because it does not require the calculation of relevant thermodynamic parameters. In addition, a mathematical model for estimating the viscosity of a ternary Sn-based lead-free solder alloy has been proposed. The overall research shows that the BP neural network model can be well applied to the theoretical study of the viscosity of liquid solder alloys. Using a constructed BP neural network to predict the viscosity of a lead-free solder melt helps to study the liquid physical properties of lead-free solders that are widely used in electronic information.

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“…Surface mount technology (SMT) has become the key process of printed circuit board assembly (Huang et al , 2011). Important steps in the manufacturing process include: stencil printing, coating the solder paste on the bonding pad on the PCB, and using automated equipment to precisely place the parts on the PCB (Lau et al , 2016; Borecki and Serzysko, 2016; Huang and Huang, 2014). Next, reflow soldering is carried out.…”

Section: Introductionmentioning

confidence: 99%

Applying data mining methodology to establish an intelligent decision system for PCBA process

Huang

Ruano

Chen

et al. 2019

SSMT

Self Cite

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Purpose This paper aims to consider the practical production environment of electronics manufacturing industry firms, and the large quantities of information collected on machine processes, testing data and production reports, while simultaneously taking into account the properties of the processing environment, in conducting analysis to obtain valuable information. Design/methodology/approach This research constructs a prediction model of the circuit board assembly process yield. A decision tree is used to extract the key attributes. The authors also integrate association rules to determine the relevance of key attributes of undesirable phenomena. Findings The results assure the successful application of the methodology by reconfirming the rules for solder skip and short circuit occurrence and their causes. Originality/value Measures for improvement are recommended, production parameters determined and debugging suggestions made to improve the process yield when the new process is implemented.

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Process optimization of SnCuNi soldering material using artificial parametric design

Cited by 15 publications

References 25 publications

Hybrid evolutionary optimization for nutraceutical manufacturing processes

Hybrid evolutionary optimization for nutraceutical manufacturing processes

Prediction of viscosity of ternary tin-based lead-free solder melt using BP neural network

Applying data mining methodology to establish an intelligent decision system for PCBA process

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