Robust design optimization of dynamic and static manufacturing processes using the stochastic frontier model
The paper discusses a novel method, which addresses robust design optimization of dynamic and static multi-objective processes. For a dynamic process, the optimal setting of the graded signal and input parameters are sought so that it is least sensitive to internal and external noises. In addition t...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | Mechanics & Industry |
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| Online Access: | https://www.mechanics-industry.org/articles/meca/full_html/2025/01/mi230118/mi230118.html |
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| _version_ | 1841554712865800192 |
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| author | Trabelsi Ali Rezgui Mohamed-Ali Amdouni Marwan Dokkar Atef Jmal Hamdi |
| author_facet | Trabelsi Ali Rezgui Mohamed-Ali Amdouni Marwan Dokkar Atef Jmal Hamdi |
| author_sort | Trabelsi Ali |
| collection | DOAJ |
| description | The paper discusses a novel method, which addresses robust design optimization of dynamic and static multi-objective processes. For a dynamic process, the optimal setting of the graded signal and input parameters are sought so that it is least sensitive to internal and external noises. In addition to addressing planned and unplanned experiments (cross-sectional and panel data), the method estimates the random and nonrandom variance components variably (i.e., returns a non-constant uncertainty at each combination level or treatment). The stochastic frontier model is utilized to ensure this purpose. For dynamic processes, the method operates in three main steps, (i) data preparation by transforming the outputs to maximization functions, (ii) estimate of the composed variation (random and non-random error components), (iii) and, composition of the process uncertainty array for each output across the signal levels. The robust design optimization solution corresponds to the levels combination of the signal and the input factors, which adds up to the lowest global uncertainty score. The applicability of the approach is then illustrated with a case study that uses one signal factor at two levels and four input factors (x1, x2, x3, and x4) at three levels each. The process responses, Y1, Y2, and Y3 are of types Dynamic Larger the Best (DLB), Dynamic Nominal the Best (DNB), and Dynamic Smaller the Best (DSB), respectively. |
| format | Article |
| id | doaj-art-7ab303aa91564058a8c7965fa50d629f |
| institution | Kabale University |
| issn | 2257-7777 2257-7750 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Mechanics & Industry |
| spelling | doaj-art-7ab303aa91564058a8c7965fa50d629f2025-01-08T11:12:28ZengEDP SciencesMechanics & Industry2257-77772257-77502025-01-0126110.1051/meca/2024034mi230118Robust design optimization of dynamic and static manufacturing processes using the stochastic frontier modelTrabelsi Ali0Rezgui Mohamed-Ali1Amdouni Marwan2Dokkar Atef3Jmal Hamdi4Laboratory of Mechanics, Production and Energetics (LR18ES01), University of Tunis, Higher National Engineering School of TunisLaboratory of Mechanics, Production and Energetics (LR18ES01), University of Tunis, Higher National Engineering School of TunisLaboratory of Mechanics, Production and Energetics (LR18ES01), University of Tunis, Higher National Engineering School of TunisLaboratory of Mechanics, Production and Energetics (LR18ES01), University of Tunis, Higher National Engineering School of TunisICube Laboratory, UMR 7357 CNRS, Mechanics Department, University of StrasbourgThe paper discusses a novel method, which addresses robust design optimization of dynamic and static multi-objective processes. For a dynamic process, the optimal setting of the graded signal and input parameters are sought so that it is least sensitive to internal and external noises. In addition to addressing planned and unplanned experiments (cross-sectional and panel data), the method estimates the random and nonrandom variance components variably (i.e., returns a non-constant uncertainty at each combination level or treatment). The stochastic frontier model is utilized to ensure this purpose. For dynamic processes, the method operates in three main steps, (i) data preparation by transforming the outputs to maximization functions, (ii) estimate of the composed variation (random and non-random error components), (iii) and, composition of the process uncertainty array for each output across the signal levels. The robust design optimization solution corresponds to the levels combination of the signal and the input factors, which adds up to the lowest global uncertainty score. The applicability of the approach is then illustrated with a case study that uses one signal factor at two levels and four input factors (x1, x2, x3, and x4) at three levels each. The process responses, Y1, Y2, and Y3 are of types Dynamic Larger the Best (DLB), Dynamic Nominal the Best (DNB), and Dynamic Smaller the Best (DSB), respectively.https://www.mechanics-industry.org/articles/meca/full_html/2025/01/mi230118/mi230118.htmlrobust design optimizationdynamic and static systems/processestaguchi methodstochastic frontier modelmulti-objective processesexternal and internal noises |
| spellingShingle | Trabelsi Ali Rezgui Mohamed-Ali Amdouni Marwan Dokkar Atef Jmal Hamdi Robust design optimization of dynamic and static manufacturing processes using the stochastic frontier model Mechanics & Industry robust design optimization dynamic and static systems/processes taguchi method stochastic frontier model multi-objective processes external and internal noises |
| title | Robust design optimization of dynamic and static manufacturing processes using the stochastic frontier model |
| title_full | Robust design optimization of dynamic and static manufacturing processes using the stochastic frontier model |
| title_fullStr | Robust design optimization of dynamic and static manufacturing processes using the stochastic frontier model |
| title_full_unstemmed | Robust design optimization of dynamic and static manufacturing processes using the stochastic frontier model |
| title_short | Robust design optimization of dynamic and static manufacturing processes using the stochastic frontier model |
| title_sort | robust design optimization of dynamic and static manufacturing processes using the stochastic frontier model |
| topic | robust design optimization dynamic and static systems/processes taguchi method stochastic frontier model multi-objective processes external and internal noises |
| url | https://www.mechanics-industry.org/articles/meca/full_html/2025/01/mi230118/mi230118.html |
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