Soft Optimal Computing to Identify Surface Roughness in Manufacturing Using a Gaussian and a Trigonometric Regressor
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Soft Optimal Computing to Identify Surface Roughness in Manufacturing Using a Gaussian and a Trigonometric Regressor. / Haus, Benedikt; Mercorelli, Paolo; Yap, Jin Siang et al.
Soft Computing and its Engineering Applications: Third International Conference, icSoftComp 2021, Changa, Anand, India, December 10–11, 2021, revised selected papers. ed. / Kanubhai K. Patel; Gayatri Doctor; Atul Patel; Pawan Lingras. Cham : Springer Nature Switzerland AG, 2022. p. 41-50 (Communications in Computer and Information Science; Vol. 1572 CCIS).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - Soft Optimal Computing to Identify Surface Roughness in Manufacturing Using a Gaussian and a Trigonometric Regressor
AU - Haus, Benedikt
AU - Mercorelli, Paolo
AU - Yap, Jin Siang
AU - Schäfer, Lennart
PY - 2022
Y1 - 2022
N2 - This contribution deals with the identification of roughness as a function of gloss in manufacturing using Particle Swarm Optimization (PSO) methods. The proposed PSO method uses a Least Squares Method as a cost function to be optimized. The identification structure uses a Gaussian and a Trigonometric Regressor characterized by seven parameters to be estimated. In PSO algorithms, there is a delicate balance to maintain between exploration (global search) and exploitation (local search) and this is one of the most important issues of this optimization method. An analysis of an increment of the dimension of the search space of the PSO is proposed. This is realized through an increment of its exploitation dimension to improve the precision of the search phase of the PSO, at the cost of more computations in each iteration. Nevertheless, convergence time results to be shorter in the presented case. Thus, an optimal increment of the dimension exists which states a compromise between velocity of the convergence and precision. Measured results from a manufacturing system with and without enlargement of the search space are shown together with results obtained using a Genetic Algorithm (GA) for comparison. Advantages and drawbacks are pointed out.
AB - This contribution deals with the identification of roughness as a function of gloss in manufacturing using Particle Swarm Optimization (PSO) methods. The proposed PSO method uses a Least Squares Method as a cost function to be optimized. The identification structure uses a Gaussian and a Trigonometric Regressor characterized by seven parameters to be estimated. In PSO algorithms, there is a delicate balance to maintain between exploration (global search) and exploitation (local search) and this is one of the most important issues of this optimization method. An analysis of an increment of the dimension of the search space of the PSO is proposed. This is realized through an increment of its exploitation dimension to improve the precision of the search phase of the PSO, at the cost of more computations in each iteration. Nevertheless, convergence time results to be shorter in the presented case. Thus, an optimal increment of the dimension exists which states a compromise between velocity of the convergence and precision. Measured results from a manufacturing system with and without enlargement of the search space are shown together with results obtained using a Genetic Algorithm (GA) for comparison. Advantages and drawbacks are pointed out.
KW - Curve fitting
KW - Manufacturing applications
KW - Particle Swarm Optimization
KW - Engineering
UR - http://www.scopus.com/inward/record.url?scp=85130279372&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/43901d95-2f2d-3bd1-a0f1-8e1d540724ff/
U2 - 10.1007/978-3-031-05767-0_4
DO - 10.1007/978-3-031-05767-0_4
M3 - Article in conference proceedings
AN - SCOPUS:85130279372
SN - 978-3-031-05766-3
T3 - Communications in Computer and Information Science
SP - 41
EP - 50
BT - Soft Computing and its Engineering Applications
A2 - Patel, Kanubhai K.
A2 - Doctor, Gayatri
A2 - Patel, Atul
A2 - Lingras, Pawan
PB - Springer Nature Switzerland AG
CY - Cham
T2 - 3rd International Conference on Soft Computing and its Engineering Applications - icSoftComp 2021
Y2 - 10 December 2021 through 11 December 2021
ER -