An Integrated Approach to Modeling and Optimization in Engineering and Science

1. Auflage 2024
ISBN: 978-1-032-78279-9
Verlag: CRC Press

Buch, Englisch, 343 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 626 g

An Integrated Approach to Modeling and Optimization in Engineering and Science examines the effects of experimental design, mathematical modeling, and optimization processes for solving many different problems. The Experimental Design Method, Central Composite, Full Factorial, Taguchi, Box-Behnken, and D-Optimal methods are used, and the effects of the datasets obtained by these methods on mathematical modeling are investigated.

This book will help graduates and senior undergraduates in courses on experimental design, modeling, optimization, and interdisciplinary engineering studies. It will also be of interest to research and development engineers and professionals working in scientific institutions based on design, modeling, and optimization.

Zielgruppe

Professional Reference

Autoren/Hrsg.

Savran, Melih

Aydin, Levent

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik Fertigungstechnik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
• Geisteswissenschaften Design Produktdesign, Industriedesign

Inhaltsverzeichnis

1. Introduction. 2. Design of Experiment, Mathematical Modeling, and Optimization. 3. Comparison of ANN and Neuro Regression Methods in Mathematical Modeling. 4. Evaluation of R2 as a Model Assessment Criterion. 5. Questioning the Adequacy of Using Polynomial Structures. 6. The Effect of Using the Taguchi Method in Experimental Design on Mathematical Modeling. 7. Comparison of Different Test and Validation Methods Used in Mathematical Modeling. 8. Comparison of Different Model Assessment Criteria Used in Mathematical Modeling. 9. Comparison of the Effects of Experimental Design Methods on Mathematical Modeling. 10. Special Functions in Mathematical Modeling. 11. Conclusion.

Über Autor(innen)

Melih Savran earned a BS degree in mechanical engineering at Manisa Celal Bayar University in 2013. He earned MS and PhD degrees in mechanical engineering at Izmir Katip Çelebi University in 2017 and 2023, respectively. He continues to work as a researcher at the same university. His research areas include mechanics of solids, design and mathematical modeling, machine learning, stochastic optimization, and hybrid natural/synthetic composites. He has international publications on stochastic optimization and modeling in engineering, including book chapters, journal articles, and conference papers.

Levent Aydin is an Associate Professor of Mechanical Engineering at Izmir Katip Çelebi University. He earned a PhD degree in mechanical engineering at Izmir Institute of Technology in 2011. His main research interests are stochastic optimization, mechanics of solids, biocomposites, biosensors, advanced engineering mathematics, hybrid neuro regression, and artificial intelligence modeling. Dr. Aydin has written more than 100 international publications on stochastic optimization and modeling in engineering, including book chapters, journal articles, and conference papers. He is also a consultant for many industrial research and development projects of international engineering firms. Dr. Aydin is the founder of the Optimization, Modeling and Applied Math Research Group (OMA-RG). He is the editor or author of Designing Engineering Structures Using Stochastic Optimization Methods, Bioelectrochemical Interface Engineering, Hybrid Natural Fiber Composites, Vegetable Fiber Composites and Their Technological Applications, and Fiber Technology for Fiber-Reinforced Composites.