Design of Experiment Optimization Capabilities – AxPLAN

Introduction

While traditional optimization methods need a computation time that exponentially varies with the number of parameters involved there exists multiple design of experiment (DoE) approaches which allow significantly cutting this computation time. The best news about it is that the time saved using DoE grows with the number of variables selected (about 20 different parameters can be selected at once for optimization tasks).

Scope

A very extensive number of parameters can be chosen for their optimization based on any combination of criteria using individual, customized weights to define specific optimization tasks. This can all be done within the AxSTREAM® platform while using the meanline and streamline solvers for direct task calculations.

A response surface is created based on a mathematical model that defines the characteristics of the given machine for the provided range of values and parameters. These allow performing very fast optimization tasks and provide the best 5 combinations of values for the selected components.

Results can be reviewed in tabular and graphical formats and files can be saved individually to reload an existing calculation or use it as a template for an upcoming task.