Open AccessMulti-response optimization design based on Non- parametric error-corrected method
Author(s) -
Tingyu Gao
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1043/4/042025
Subject(s) - parametric statistics , computer science , nonparametric statistics , mathematical optimization , parametric model , function (biology) , design of experiments , parametric design , genetic algorithm , machining , algorithm , mathematics , engineering , machine learning , statistics , mechanical engineering , evolutionary biology , biology
The construction of a response surface model is critical to the experimental results. Traditional model construction method is parametric method. The parametric estimates may be highly biased, and the optimal control factor settings can be miscalculated if the models are not correctly specified. To solve these problems, this paper proposes a new multi-response optimization design method, Non-parametric error-corrected method. The nonparametric method provides a very useful alternative when researchers don’t have any information about the form of underlying functions. Finally, the hybrid genetic algorithm is used to achieve global optimization aiming at the expected quality loss function the validity of the method was verified by the experimental data of the femtosecond laser micro/nano-machining.