Buch, Englisch, 230 Seiten, Format (B × H): 183 mm x 260 mm, Gewicht: 639 g
Simulation Analysis and Industrial Applications
Buch, Englisch, 230 Seiten, Format (B × H): 183 mm x 260 mm, Gewicht: 639 g
ISBN: 978-1-032-33198-0
Verlag: CRC Press
This book investigates human–machine systems through the use of case studies such as crankshaft maintenance, liner piston maintenance, and biodiesel blend performance. Through mathematical modelling and using various case studies, the book provides an understanding of how a mathematical modelling approach can assist in working out problems in any industrial-oriented activity.
Mathematical Modelling: Simulation Analysis and Industrial Applications details a data analysis approach using mathematical modelling sensitivity. This approach helps in the processing of any type of data and can predict the result so that based on the result, the activity can be controlled by knowing the most influencing variables or parameters involved in the phenomenon. This book helps to solve field and experimental problems of any research activity using a data-based modelling concept to assist in solving any type of problem.
Students in manufacturing, mechanical, and industrial engineering programs will find this book very useful. This topic has continued to advance and incorporate new concepts so that the manufacturing field continues to be a dynamic and exciting field of study.
Zielgruppe
Academic, Postgraduate, Professional, and Undergraduate Advanced
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Mathematische Analysis
- Technische Wissenschaften Technik Allgemein Industrial Engineering
- Technische Wissenschaften Technik Allgemein Technik: Allgemeines
- Geisteswissenschaften Design Produktdesign, Industriedesign
- Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik Fertigungstechnik
- Technische Wissenschaften Maschinenbau | Werkstoffkunde Maschinenbau
Weitere Infos & Material
1. Evaluation of the System. 2. Concept of Field Data Based Modelling. 3.Design of Experimentation 4.Experimentation. 5. Formulation of Mathematical Model. 6. Artificial Neural Network Simulation 7. Sensitivity Analysis. 8. Interpretation of Mathematical Models