title: Searching for optimal setting conditions in technological processes using parametric estimation models and neural network mapping approach: a tutorial.
abstract: Engineering optimization is an actual goal in manufacturing and service industries. In the tutorial we represented the concept of traditional parametric estimation models (Factorial Design (FD) and Central Composite Design (CCD)) for searching optimal setting parameters of technological processes. Then the 2D mapping method based on Auto Associative Neural Networks (ANN) (particularly, the Feed Forward Bottle Neck Neural Network (FFBN NN)) was described in comparison with traditional methods. The FFBN NN mapping technique enables visualization of all optimal solutions in considered processes due to the projection of input as well as output parameters in the same coordinates of 2D map. This phenomenon supports the more efficient way of improving the performance of existing systems. Comparison of two methods was performed on the bases of optimization of solder paste printing processes as well as optimization of properties of cheese. Application of both methods enables the double check. This increases the reliability of selected optima or specification limits.
authors.affiliation: National Institute of Chemistry, Hajdrihova 19, SI, 1000 Ljubljana, Slovenia. Electronic address: natalja.fjodorova@ki.si.
authors.forename: Natalja
authors.initials: N
authors.lastname: Fjodorova
authors.affiliation: National Institute of Chemistry, Hajdrihova 19, SI, 1000 Ljubljana, Slovenia.
authors.forename: Marjana
authors.initials: M
authors.lastname: Novič
mesh_terms: D000465:Algorithms; D002611:Cheese / Q000032:analysis; D004581:Electronics / Q000379:methods; D005524:Food Technology / Q000379:methods; D016571:Neural Networks, Computer; D012984:Software
keywords: Design of experiment; Factorial design; Feed-forward bottleneck neural network; Optimization methods; Response surface design