Monte Carlo simulation and analysis in modern optical tolerancing:
This Spotlight offers a perspective on the role of Monte Carlo simulation in the analysis and tolerancing of optical systems. The book concisely explores two overarching questions: (1) What principles can we adopt from a variety of statistical methods - such as the analysis of variance (ANOVA), &quo...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Bellingham, Washington, USA
SPIE Press
[2019]
|
| Schriftenreihe: | SPIE spotlight series
vol. SL53 |
| Schlagworte: | |
| Online-Zugang: | FHD01 Volltext |
| Zusammenfassung: | This Spotlight offers a perspective on the role of Monte Carlo simulation in the analysis and tolerancing of optical systems. The book concisely explores two overarching questions: (1) What principles can we adopt from a variety of statistical methods - such as the analysis of variance (ANOVA), "root sum of squares" (RSS), and Monte Carlo simulation - to analyze variability in complex optical systems? (2) When we assign perturbations to component variables (such as tilts and radii of curvatures) subject to arbitrary probability distributions, are the resulting distributions of system parameters (such as EFL, RMS spot size, and MTF) necessarily normal? These questions address the problem of analyzing and managing variability in modern product development, where many functions integrate to produce a complete instrument. By discussing key concepts from optics, multivariable calculus, and statistics, and applying them to two practical examples in modern technology, this book highlights the role Monte Carlo simulations play in the tolerancing of optical systems that comprise many components of variation |
| Beschreibung: | 1 Online-Ressource (v, 48 Seiten) |
| ISBN: | 9781510631656 |
| DOI: | 10.1117/3.2552933 |
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| 505 | 8 | |a Preface -- 1. Introduction: 1.1. What is "modern" about modern optical tolerancing? 1.2. What is a Monte Carlo simulation? 1.3. Significance of functions of random variables -- 2. Prelude to Monte Carlo: 2.1. Systematic versus random variability; 2.2. Standard deviation as error, uncertainty, and variability; 2.3. Error of the mean (why averaging reduces variability); 2.4. Error of the error (why variability also varies); 2.5. Sample variance: why we divide by "n -- 1"; 2.6. Concept of variance components and sensitivity analysis -- 3. Monte Carlo approach: 3.1. Influence of compensators on variability: correlations and covariances; 3.2. Sensitivity analysis by way of statistical tests of effects on distributions; 3.3. Do normal component distributions yield normal system distributions? 3.4. When is the central limit theorem applicable? 3.5. Example: miniature multifunctional relay lens system with liquid element; 3.6. Example: geometric optical fiber coupling efficiency; 3.7. Role of Monte Carlo simulation in Six Sigma, quality, and reliability; 3.8. Random numbers and the subtle art in the science of modern optical tolerancing -- References | |
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| contents | Preface -- 1. Introduction: 1.1. What is "modern" about modern optical tolerancing? 1.2. What is a Monte Carlo simulation? 1.3. Significance of functions of random variables -- 2. Prelude to Monte Carlo: 2.1. Systematic versus random variability; 2.2. Standard deviation as error, uncertainty, and variability; 2.3. Error of the mean (why averaging reduces variability); 2.4. Error of the error (why variability also varies); 2.5. Sample variance: why we divide by "n -- 1"; 2.6. Concept of variance components and sensitivity analysis -- 3. Monte Carlo approach: 3.1. Influence of compensators on variability: correlations and covariances; 3.2. Sensitivity analysis by way of statistical tests of effects on distributions; 3.3. Do normal component distributions yield normal system distributions? 3.4. When is the central limit theorem applicable? 3.5. Example: miniature multifunctional relay lens system with liquid element; 3.6. Example: geometric optical fiber coupling efficiency; 3.7. Role of Monte Carlo simulation in Six Sigma, quality, and reliability; 3.8. Random numbers and the subtle art in the science of modern optical tolerancing -- References |
| ctrlnum | (OCoLC)1141155658 (DE-599)BVBBV046418483 |
| doi_str_mv | 10.1117/3.2552933 |
| format | Electronic eBook |
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| id | DE-604.BV046418483 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:44:05Z |
| institution | BVB |
| isbn | 9781510631656 |
| language | English |
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| spelling | Siew, Ronian Verfasser aut Monte Carlo simulation and analysis in modern optical tolerancing by Ronian Siew Bellingham, Washington, USA SPIE Press [2019] 1 Online-Ressource (v, 48 Seiten) txt rdacontent c rdamedia cr rdacarrier SPIE spotlight series vol. SL53 Preface -- 1. Introduction: 1.1. What is "modern" about modern optical tolerancing? 1.2. What is a Monte Carlo simulation? 1.3. Significance of functions of random variables -- 2. Prelude to Monte Carlo: 2.1. Systematic versus random variability; 2.2. Standard deviation as error, uncertainty, and variability; 2.3. Error of the mean (why averaging reduces variability); 2.4. Error of the error (why variability also varies); 2.5. Sample variance: why we divide by "n -- 1"; 2.6. Concept of variance components and sensitivity analysis -- 3. Monte Carlo approach: 3.1. Influence of compensators on variability: correlations and covariances; 3.2. Sensitivity analysis by way of statistical tests of effects on distributions; 3.3. Do normal component distributions yield normal system distributions? 3.4. When is the central limit theorem applicable? 3.5. Example: miniature multifunctional relay lens system with liquid element; 3.6. Example: geometric optical fiber coupling efficiency; 3.7. Role of Monte Carlo simulation in Six Sigma, quality, and reliability; 3.8. Random numbers and the subtle art in the science of modern optical tolerancing -- References This Spotlight offers a perspective on the role of Monte Carlo simulation in the analysis and tolerancing of optical systems. The book concisely explores two overarching questions: (1) What principles can we adopt from a variety of statistical methods - such as the analysis of variance (ANOVA), "root sum of squares" (RSS), and Monte Carlo simulation - to analyze variability in complex optical systems? (2) When we assign perturbations to component variables (such as tilts and radii of curvatures) subject to arbitrary probability distributions, are the resulting distributions of system parameters (such as EFL, RMS spot size, and MTF) necessarily normal? These questions address the problem of analyzing and managing variability in modern product development, where many functions integrate to produce a complete instrument. By discussing key concepts from optics, multivariable calculus, and statistics, and applying them to two practical examples in modern technology, this book highlights the role Monte Carlo simulations play in the tolerancing of optical systems that comprise many components of variation Optical instruments / Design and construction / Simulation methods Monte Carlo method Monte Carlo method fast Erscheint auch als Online-Ausgabe, epub 978-1-5106-3166-3 Erscheint auch als Online-Ausgabe, mobi 978-1-5106-3167-0 https://doi.org/10.1117/3.2552933 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Siew, Ronian Monte Carlo simulation and analysis in modern optical tolerancing Preface -- 1. Introduction: 1.1. What is "modern" about modern optical tolerancing? 1.2. What is a Monte Carlo simulation? 1.3. Significance of functions of random variables -- 2. Prelude to Monte Carlo: 2.1. Systematic versus random variability; 2.2. Standard deviation as error, uncertainty, and variability; 2.3. Error of the mean (why averaging reduces variability); 2.4. Error of the error (why variability also varies); 2.5. Sample variance: why we divide by "n -- 1"; 2.6. Concept of variance components and sensitivity analysis -- 3. Monte Carlo approach: 3.1. Influence of compensators on variability: correlations and covariances; 3.2. Sensitivity analysis by way of statistical tests of effects on distributions; 3.3. Do normal component distributions yield normal system distributions? 3.4. When is the central limit theorem applicable? 3.5. Example: miniature multifunctional relay lens system with liquid element; 3.6. Example: geometric optical fiber coupling efficiency; 3.7. Role of Monte Carlo simulation in Six Sigma, quality, and reliability; 3.8. Random numbers and the subtle art in the science of modern optical tolerancing -- References Optical instruments / Design and construction / Simulation methods Monte Carlo method Monte Carlo method fast |
| title | Monte Carlo simulation and analysis in modern optical tolerancing |
| title_auth | Monte Carlo simulation and analysis in modern optical tolerancing |
| title_exact_search | Monte Carlo simulation and analysis in modern optical tolerancing |
| title_full | Monte Carlo simulation and analysis in modern optical tolerancing by Ronian Siew |
| title_fullStr | Monte Carlo simulation and analysis in modern optical tolerancing by Ronian Siew |
| title_full_unstemmed | Monte Carlo simulation and analysis in modern optical tolerancing by Ronian Siew |
| title_short | Monte Carlo simulation and analysis in modern optical tolerancing |
| title_sort | monte carlo simulation and analysis in modern optical tolerancing |
| topic | Optical instruments / Design and construction / Simulation methods Monte Carlo method Monte Carlo method fast |
| topic_facet | Optical instruments / Design and construction / Simulation methods Monte Carlo method |
| url | https://doi.org/10.1117/3.2552933 |
| work_keys_str_mv | AT siewronian montecarlosimulationandanalysisinmodernopticaltolerancing |