An adaptive random experiment design method for engineering experiment

TL;DR

This paper introduces an adaptive random experiment design (ARED) algorithm that optimizes multi-factor experiments by using real-time model error, resulting in improved precision and applicability over traditional methods.

Contribution

The paper presents a novel ARED algorithm that dynamically adjusts experimental design based on real-time error, enhancing efficiency and accuracy in multi-factor experiments.

Findings

ARED requires fewer test cases than traditional methods.

ARED achieves higher precision in model prediction.

ARED demonstrates better applicability across different functions.

Abstract

This paper proposes an adaptive random experiment design (ARED) algorithm that can be applied to optimize the multiple factors and levels experiments. The algorithm takes real-time model error as the adaptive condition, and outputs a model that conforms to the error quantization standard based on the automatic process. According to the actual experimental scenario, the similar number of test cases were selected between the ARED method and the comparative experimental design method under the bimodal Gaussian function, the bimodal surface function and the peaks function, respectively. simultaneously, the support vector machine (SVM) algorithm is used to construct the model for the selected test cases, and the verification surface (or curve) is predicted. The qualitative and quantitative analysis is carried out at two-slice of applicability and precision. The results show that the ARED method can be applied to the experiment of multi-factor, and has better precision and applicability than the comparative experimental methods.