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

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review


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.

Original languageEnglish
Title of host publicationSoft Computing : Theories and Applications - Proceedings of SoCTA 2021
EditorsRajesh Kumar, Chang Wook Ahn, Tarun K. Sharma, Om Prakash Verma, Anand Agarwal
Number of pages11
Place of PublicationCham
PublisherSpringer Science and Business Media Deutschland GmbH
Publication date2022
ISBN (Print)978-981-19-0706-7
ISBN (Electronic)978-981-19-0707-4
Publication statusPublished - 2022
Event6th International Conference on Soft Computing: Theories and Applications, SoCTA 2021 - Kota, India
Duration: 17.12.202119.12.2021
Conference number: 6

    Research areas

  • Manufacturing applications, Nonparametric identification, Particle swarm optimization
  • Engineering