Design Optimization of a Wafer Dough Blade Using Artificial Neural Network and Monte-Carlo Simulation

Abstract

In this work, a systematic method to conduct the surrogate-based design optimization is proposed by utilizing Artificial Neural Network and Monte Carlo Simulation. To show its applicability, the design optimization of a wafer dough blade that is an important component in the food industry is carried out. In the optimization problem, design variables or inputs are totally six variables including distances, diameter and thickness, and design responses or outputs are the blade mass, the maximum stress occurred on it, and its surface area. When the results of the initial and optimum designs are compared, there is a significant decrease in the maximum stress (nearly 66%) whereas there was a reasonable low difference in both the mass and surface area. Thanks to the proposed method, it can be possible to take into account the experimental data instead of analytical data in a design problem. Moreover, the followed method provides engineers with a practical and systematic way to find the optimum solution for even nonlinear problems needs to be solved during engineering design process.