Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Industrial application of fuzzy systems: adaptive fuzzy control of solder paste stencil printing
Information Sciences: an International Journal
An Introduction to Genetic Algorithms for Scientists and Engineers
An Introduction to Genetic Algorithms for Scientists and Engineers
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Fuzzy and Neural Approaches in Engineering
Fuzzy and Neural Approaches in Engineering
Object-oriented software fault prediction using neural networks
Information and Software Technology
Using recurrent neural networks to detect changes in autocorrelated processes for quality monitoring
Computers and Industrial Engineering
Development of a soldering quality classifier system using a hybrid data mining approach
Expert Systems with Applications: An International Journal
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This paper presents a comparison study for the optimization of stencil printing operations using hybrid intelligence technique and response surface methodology (RSM). An average 60% of soldering defects are attributed to solder paste stencil printing process in surface mount assembly (SMA). The manufacturing costs decrease with increasing first-pass yield in the stencil printing process. This study compares two hybrid intelligence approaches with RSM as methods of solving the stencil printing optimization problem that involves multiple performance characteristics. The optimization process is threefold. A data set obtained from an experimental design following data preprocessing process provides an accurate data source for RSM study and training neural networks to formulate the nonlinear model of the stencil printing process with/without combining multiple performance characteristics into a single desirability value, followed by a genetic algorithm searching the trained neural networks for obtaining the optimal parameter sets. The empirical defect-per-million-opportunities (DPMO) measurements demonstrate that the two hybrid intelligence methods can provide satisfactory performance for stencil printing optimization problem.