Soft Optimal Computing Methods to Identify Surface Roughness in Manufacturing Using a Monotonic Regressor
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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Soft Optimal Computing Methods to Identify Surface Roughness in Manufacturing Using a Monotonic Regressor. / Haus, Benedikt; Yap, Jin Siang; Schaefer, Lennart et al.
Soft Computing: Theories and Applications - Proceedings of SoCTA 2021. Hrsg. / Rajesh Kumar; Chang Wook Ahn; Tarun K. Sharma; Om Prakash Verma; Anand Agarwal. Cham : Springer Science and Business Media Deutschland GmbH, 2022. S. 527-537 (Lecture Notes in Networks and Systems; Band 425).Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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TY - CHAP
T1 - Soft Optimal Computing Methods to Identify Surface Roughness in Manufacturing Using a Monotonic Regressor
AU - Haus, Benedikt
AU - Yap, Jin Siang
AU - Schaefer, Lennart
AU - Mercorelli, Paolo
N1 - Conference code: 6
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Manufacturing applications
KW - Nonparametric identification
KW - Particle swarm optimization
KW - Engineering
UR - http://www.scopus.com/inward/record.url?scp=85132044524&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-0707-4_48
DO - 10.1007/978-981-19-0707-4_48
M3 - Article in conference proceedings
AN - SCOPUS:85132044524
SN - 978-981-19-0706-7
T3 - Lecture Notes in Networks and Systems
SP - 527
EP - 537
BT - Soft Computing
A2 - Kumar, Rajesh
A2 - Ahn, Chang Wook
A2 - Sharma, Tarun K.
A2 - Verma, Om Prakash
A2 - Agarwal, Anand
PB - Springer Science and Business Media Deutschland GmbH
CY - Cham
T2 - 6th International Conference on Soft Computing: Theories and Applications, SoCTA 2021
Y2 - 17 December 2021 through 19 December 2021
ER -