Open access peer-reviewed Edited Volume

Response Surface Methodology - Research Advances and Applications

Palanikumar Kayaroganam

Sri Sai Ram Institute of Technology

A pioneering researcher in materials, modelling and optimization, working as head of the Institute in Sri Sairam Institute of Technology, National Best Teacher Awardee, and holder of 6 granted patents. He is among the world's top 2% of scientists on a list compiled by Stanford University. Dr. Kayaroganam has numerous journal publications to his credit as well as several awards, including the AICTE-Visvesvaraya Best Teacher award and a Global Peer Review Award from Publons Web of Science.


Design of Experiments Central Composite Design Taguchi's Design First-order Model Second-order Model Effect Graph Contour Graphs Response Optimization Desirability Analysis Neural Network Fuzzy Logic Comparison

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About the book

The first-rate quality is the preferable aspect in the fields of Science, Engineering and Technology. Accordingly, Response Surface Methodology (RSM) lays its foundation of quality in any experiment and it aims to optimize the response. Also, it is the statistical and mathematical technique used for gaining important results through an array of experiments. Normally, it is an experimental design with several factors for optimizing the target variables. Mostly, it is considered as the key component for producing the most appropriate results through exclusive experiments.

Predominantly, it is used by researchers and technocrats for accurate estimation because the main intention of it is to advance the response time. Here, quantitative data is used to fit the regression model from the experiments and for optimizing the response. So, the linear, polynomial or square functions are adopted to study the system and explore the suitable experimental condition for optimization. This book will intend to provide the reader with a comprehensive analysis of response surface methods, which is capable of being applied in all the fields of science, engineering and technology including the machine learning models in relation to the response surface method.

Publishing process

Book initiated and editor appointed

Date completed: April 15th 2022

Applications to edit the book are assessed and a suitable editor is selected, at which point the process begins.

Chapter proposals submitted and reviewed

Deadline Extended: Open for Submissions

Potential authors submit chapter proposals ready for review by the academic editor and our publishing review team.

Approved chapters written in full and submitted

Deadline for full chapters: July 12th 2022

Once approved by the academic editor and publishing review team, chapters are written and submitted according to pre-agreed parameters

Full chapters peer reviewed

Review results due: September 30th 2022

Full chapter manuscripts are screened for plagiarism and undergo a Main Editor Peer Review. Results are sent to authors within 30 days of submission, with suggestions for rounds of revisions.

Book compiled, published and promoted

Expected publication date: November 29th 2022

All chapters are copy-checked and typesetted before being published. IntechOpen regularly submits its books to major databases for evaluation and coverage, including the Clarivate Analytics Book Citation Index in the Web of ScienceTM Core Collection. Other discipline-specific databases are also targeted, such as Web of Science's BIOSIS Previews.

About the editor

Palanikumar Kayaroganam

Sri Sai Ram Institute of Technology

Prof. Dr. Palanikumar Kayaroganamis currently a professor and principal at Sri Sai Ram Institute of Technology, Chennai, India. He is involved in teaching, research, development, and innovation in the field of higher technical education. He is among the world's top 2% of scientists on a list compiled by Stanford University. He has numerous journal publications to his credit as well as several awards, including the AICTE-Visvesvaraya Best Teacher award and a Global Peer Review Award from Publons Web of Science. His areas of interest include statistical techniques, modeling and optimization, total quality management, composite materials, machining, and additive manufacturing.

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