Product designers need to consider a variety of consumers' tastes and preferences to design
a product that appeals to consumers. However, it is often difficult for designers to find optimal …

This study presents a neural–genetic algorithm to solve the selection problem of
manufacturing process parameters. The proposed algorithm is a combination of artificial …

A genetic algorithm (GA) based optimization of the composite desirability of the tensile
properties of thermomechanically processed high strength low alloy (HSLA) steel plates is …

A proper selection of a work-in-process (WIP) inventory level has great impact onto the
productivity of wafer fabrication processes, which can be properly used to trigger the …

This study analyzes the contour distortions of polypropylene (PP) composite components
applied to the interior of automobiles. Combining a trained radial basis network (RBN)[1] …

The machining factors affecting the tool wear and the surface roughness produced in the
end-milling process are generally the cutting speed, the feed rate, the depth of cut, etc. This …

Resolving a multi-response parameter design problem using the traditional Taguchi method
is difficult. This study presents an integrated optimization approach based on neural …

This study analyzed contour distortions, wear and tensile properties of polypropylene (PP)
components applied in the interior coffer of automobiles. A hybrid method integrating a …

When developing new products many different customer needs must be fulfilled. Often a few
of the needs are prioritized (as emphasized in Quality Function Deployment) and most effort …

An integrated approach using neural networks, exponential desirability functions and
genetic algorithms to optimize parameter design problems with multiple responses is …