Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Electronics Process Technology: Production Modelling, Simulation and Optimisation
Electronics Process Technology: Production Modelling, Simulation and Optimisation
SOM-Based method for process state monitoring and optimization in fluidized bed energy plant
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
A modelling and optimization system for fluidized bed power plants
Expert Systems with Applications: An International Journal
Emission analysis of a fluidized bed boiler by using self-organizing maps
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Quality-oriented optimization of wave soldering process by using self-organizing maps
Applied Soft Computing
Analysis of flue gas emission data from fluidized bed combustion using self-organizing maps
Applied Computational Intelligence and Soft Computing
Expert system for analysis of quality in production of electronics
Expert Systems with Applications: An International Journal
Review: Computational intelligence in mass soldering of electronics - A survey
Expert Systems with Applications: An International Journal
Monitoring of caliper sensor fouling in a board machine using self-organising maps
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
This paper presents an overview of a data analysis method based on self-organizing maps (SOM), a well-known unsupervised neural network learning algorithm, which was applied to a lead-free wave soldering process. The aim of the study was to determine whether the neural network modeling method could be a useful and time-saving way to analyze data from a discrete manufacturing process, such as wave soldering, which is a widely used technique in the electronics industry to solder components on printed circuit boards. The data variables were mostly various process parameters, but also some solder defect numbers were present in the data as a measure of the product quality. The data analysis procedure went as follows. At first, the process data were modeled using the SOM-algorithm. Next, the neuron reference vectors of the formed map were clustered to reveal the desired dominating elements of each territory of the map. At the final stage, the clusters were utilized as sub-models to indicate variable dependencies in these sub-models. The results show that the method presented here can be a good way to analyze this type of process data because interesting interactions between certain process parameters and solder defects were found by means of this data-driven modeling method.