Metamodeling approach in solving the machine parameters optimization problem using neural network and genetic algorithms: A case study
The use of multilayer ceramic capacitors (MLCCs) is increasing because they are surface-mountable and are used primarily in the expanding communication and computing market. In the MLCC manufacturing process, some 80% of the loss in yield is attributable to paste-printing quality problems. Improvement in the quality of MLCC screen-printing is therefore tactically and strategically important. This research extends existing MLCC screen-printing robust design results to search for a universal optimum solution. A metamodeling approach has been applied to solving a variety of optimization problems. This is an abstraction model form from a model. The abstracted model aims to reduce model complexity, and yet maintain validity. This work involved building a screen-printing quality metamodel, based upon fractional factorial experimental design data using a neural network approachthat were then solved by genetic algorithms. The empirical results are promising. The paper concludes with practical constraints and insights for management.
neural network genetic algorithms multilayer ceramic capacitor robust design taguchi method