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Liukkonen M.,University of Eastern Finland | Havia E.,3K Factory of Electronics | Leinonen H.,3K Factory of Electronics | Hiltunen Y.,University of Eastern Finland
Expert Systems with Applications | Year: 2011

Quality issues have become increasingly important in the production of electronics, especially when dealing with electronic products not assimilated to the mainstream of consumer electronics, but rather to the group of industrial electronic devices and machinery designed to last for years or even decades. In this paper, an intelligent optimization and modeling system for electronics production is demonstrated. The system exploits real production data and can be used to diagnose and optimize the manufacturing processes. It contains three modules consisting of appropriate mathematical tools specifically tailored to each task: (1) preprocessing, (2) variable selection, and (3) optimization modules. Moreover, concrete examples are presented from the latter two modules, by using a wave soldering process as a case study. Currently, the system works on the Matlab platform, but can be programmed into standalone software and automated in the future. The results illustrate that the system can offer an efficient tool for diagnostics and process optimization in the electronics industry. © 2011 Elsevier Ltd. All rights reserved.


Liukkonen M.,University of Eastern Finland | Havia E.,3K Factory of Electronics | Leinonen H.,3K Factory of Electronics | Hiltunen Y.,University of Eastern Finland
Applied Soft Computing Journal | Year: 2011

In this paper, the optimal process parameters of a wave soldering process were defined. The optimization was performed in respect to soldering quality by minimizing a cost function describing the total repairing cost of a wave-soldered printed circuit board (PCB). The data analysis stages were as follows. First, the process data were coded into inputs for a self-organizing map (SOM). Next, a function for the repairing cost was constructed and used to find the optimal map neurons. At the last phase, the optimal parameters were approximated on the basis of the reference vectors of the optimal neurons. The results showed clearly potential in the optimization of the wave soldering process, especially in the visualization of the optimal process conditions. Therefore, it would be useful to exploit the method more widely in the electronics industry. © 2010 Elsevier B.V. All rights reserved.


Liukkonen M.,University of Eastern Finland | Havia E.,3K Factory of Electronics | Hiltunen Y.,University of Eastern Finland
Expert Systems with Applications | Year: 2012

Mass soldering of electronic components is one of the key processes in electronics production, because it affects directly the functionality of the final product. Mass soldering, like other processes of manufacturing electronics, is constantly facing new challenges arising from the evolving production environment, increasing product variety and complexity, miniaturization of components and products, new environmental regulations, and increasing time-based competition. In the last two decades, advancements in information technology and data acquisition systems have promoted the use of manufacturing-related data in process improvement, which has also promoted the use of new computational methods and made them more applicable to industrial problems. Especially computational intelligence and its applications have expanded among the different fields of industry. The benefits of these so called intelligent methods include an ability to learn from experience, to self-organize and to adapt in response to dynamically changing conditions, and a considerable potential in solving real world problems. This survey provides an insight to the application of computational intelligence to mass soldering of electronics. The survey includes a summary on the main application fields of these methods in the past, what methods have been typically used, and what the most probable application fields will be in the future. © 2012 Elsevier Ltd. All rights reserved.

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