Optimization of Complex Process, Based on Design Of Experiments, a Generic Methodology
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866917819527987200 |
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| author | Baderot, Julien Cauchepin, Yann Seiller, Alexandre Fontanges, Richard Martinez, Sergio Foucher, Johann Fuchs, Emmanuel Daanoune, Mehdi Grenier, Vincent Barra, Vincent Guillin, Arnaud |
| author_facet | Baderot, Julien Cauchepin, Yann Seiller, Alexandre Fontanges, Richard Martinez, Sergio Foucher, Johann Fuchs, Emmanuel Daanoune, Mehdi Grenier, Vincent Barra, Vincent Guillin, Arnaud |
| contents | MicroLED displays are the result of a complex manufacturing chain. Each stage of this process, if optimized, contributes to achieving the highest levels of final efficiencies. Common works carried out by Pollen Metrology, Aledia, and Universit{é} Clermont-Auvergne led to a generic process optimization workflow. This software solution offers a holistic approach where stages are chained together for gaining a complete optimal solution. This paper highlights key corners of the methodology, validated by the experiments and process experts: data cleaning and multi-objective optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_21294 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimization of Complex Process, Based on Design Of Experiments, a Generic Methodology Baderot, Julien Cauchepin, Yann Seiller, Alexandre Fontanges, Richard Martinez, Sergio Foucher, Johann Fuchs, Emmanuel Daanoune, Mehdi Grenier, Vincent Barra, Vincent Guillin, Arnaud Neural and Evolutionary Computing Optimization and Control MicroLED displays are the result of a complex manufacturing chain. Each stage of this process, if optimized, contributes to achieving the highest levels of final efficiencies. Common works carried out by Pollen Metrology, Aledia, and Universit{é} Clermont-Auvergne led to a generic process optimization workflow. This software solution offers a holistic approach where stages are chained together for gaining a complete optimal solution. This paper highlights key corners of the methodology, validated by the experiments and process experts: data cleaning and multi-objective optimization. |
| title | Optimization of Complex Process, Based on Design Of Experiments, a Generic Methodology |
| topic | Neural and Evolutionary Computing Optimization and Control |
| url | https://arxiv.org/abs/2410.21294 |