Mathematical Modelling
eBook - ePub

Mathematical Modelling

Simulation Analysis and Industrial Applications

  1. 240 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Mathematical Modelling

Simulation Analysis and Industrial Applications

About this book

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.

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Yes, you can access Mathematical Modelling by Pramod Belkhode,Prashant Maheshwary,Kanchan Borkar,J.P. Modak in PDF and/or ePUB format, as well as other popular books in Mathematics & Mathematics General. We have over one million books available in our catalogue for you to explore.

Information

Publisher
CRC Press
Year
2023
Print ISBN
9781032331980
eBook ISBN
9781000840155

Table of contents

  1. Cover Page
  2. Half Title page
  3. Title Page
  4. Copyright Page
  5. Contents
  6. Author Biographies
  7. 1 Evaluation of the System
  8. 2 Concept of Field Data-Based Modelling
  9. 3 Design of Experimentation
  10. 4 Experimentation
  11. 5 Formulation of Mathematical Model
  12. 6 Artificial Neural Network Simulation
  13. 7 Sensitivity Analysis
  14. 8 Interpretation of Mathematical Models
  15. Bibliography