Soft Optimal Computing Methods to Identify Surface Roughness in Manufacturing Using a Monotonic Regressor

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Authors

This contribution deals with the identification of gloss as a function of roughness using particle swarm optimization (PSO) methods. The proposed PSO methods use a least squares method (LSM) as a cost function to be optimized. The nonparametric identification structure uses a Gaussian regressor characterized by three parameters to be estimated. Three different algorithms are proposed: a global classical PSO, an intertwined PSO structure and a PSO structure combined with a linear regression method obtained using a logarithmical transformation. Results using measured data are shown at the end of this analysis to compare the three different techniques.

OriginalspracheEnglisch
TitelSoft Computing : Theories and Applications - Proceedings of SoCTA 2021
HerausgeberRajesh Kumar, Chang Wook Ahn, Tarun K. Sharma, Om Prakash Verma, Anand Agarwal
Anzahl der Seiten11
ErscheinungsortCham
VerlagSpringer Science and Business Media Deutschland GmbH
Erscheinungsdatum2022
Seiten527-537
ISBN (Print)978-981-19-0706-7
ISBN (elektronisch)978-981-19-0707-4
DOIs
PublikationsstatusErschienen - 2022
Veranstaltung6th International Conference on Soft Computing: Theories and Applications, SoCTA 2021 - Kota, Indien
Dauer: 17.12.202119.12.2021
Konferenznummer: 6

DOI