Artificial Intelligence-Aided Materials Design
AI-Algorithms and Case Studies on Alloys and Metallurgical Processes
- Available for pre-order. Item will ship after February 17, 2022
Artificial Intelligence-Aided Materials Design: AI-Algorithms and Case Studies on Alloys and Metallurgical Processes describes the application of artificial intelligence (AI)/machine learning (ML) concepts to develop predictive models that can be used to design alloy materials, including magnetic alloys, nickel-base superalloys, titanium-base alloys, and aluminum-base alloys. Readers new to AI/ML algorithms can use this book as a starting point and use the included MATLAB and Python implementation of AI/ML algorithms through included case studies. Experienced AI/ML researchers who want to try new algorithms can use this book and study the case studies for reference.
- Offers advantages and limitations of several AI concepts and their proper implementation in various data types generated through experiments and computer simulations and from industries in different file formats
- Helps readers develop predictive models through AI/ML algorithms by writing their own computer code or using resources where they do not have to write code
- Provides downloadable resources such as MATLAB GUI/APP and Python implementation that can be used on common mobile devices
- Discusses the CALPHAD approach and ways to use data generated from it
- Features a chapter on metallurgical/materials concepts to help readers understand the case studies and thus proper implementation of AI/ML algorithms under the framework of data-driven materials science
This book is written for materials scientists and metallurgists interested in the application of AI, ML, and data science in the development of new materials.
Table of Contents
1. Introduction. 2. Metallurgical/Materials Concepts. 3. Artificial Intelligence Algorithms. 4. Case Study 1: Nanomechanics and Nanotribology: Combined Machine Learning-Experimental Approach. 5. Case Study 2: Design of Hard Magnetic Alnico Alloys: Combined Machine Learning-Experimental Approach. 6. Case Study 3: Design of Soft Magnetic Finemet Type Alloys: Combined Machine Learning-CALPHAD Approach. 7. Case Study 4: Design of Nickel-Base Superalloys: Combined Machine Learning-CALPHAD Approach. 8. Case Study 5: Design of Aluminum Alloys: Combined Machine Learning-CALPHAD Approach. 9. Case Study 6: Design of Titanium Alloys for High-Temperature Application: Combined Machine Learning-CALPHAD Approach. 10. Case Study 7: Design of Titanium Based Biomaterials: Combined Machine Learning-CALPHAD Approach. 11. Case Study 8: Industrial Furnaces I: Application of Machine Learning on an Industrial Iron Making Blast Furnace Data. 12. Case Study 9: Industrial Furnaces II: Application of Machine Learning Algorithms on an Industrial LD Steel Making Furnace Data. 13. Software/Codes Included with this Book. 14. Conclusion.
Rajesh Jha is a postdoctoral researcher at the Department of Mechanical and Materials Engineering, College of Engineering and Computing, Florida International University (FIU). Prior to FIU, he worked as a postdoctoral researcher at the Department of Mechanical Engineering, Colorado School of Mines, Golden, Colorado . He graduated with a Ph.D. in Materials Science and Engineering from Florida International University, Miami, FL in 2016, Master’s in Technology from Indian Institute of Technology, Kharagpur, India in 2012 and B.Sc. (Metallurgy) from Bihar Institute of Technology (BIT), Sindri, Jharkhand, India in 2009. Since 2010, he has been working extensively on Machine Learning (ML)/Artificial Intelligence (AI) algorithms along with Multi-objective Optimization algorithms. He has applied AI/ML algorithms in the field of materials and alloy design and process metallurgy. Dr. Jha also has a strong background as an experimentalist and has worked as a Project Assistant at National Metallurgical Laboratory, Jamshedpur, India, working on microscopy, additive manufacturing, coatings, and corrosion. He has published over 30 publications, including journal articles, one book chapter, and peer reviewed international conference proceedings. He has developed software for simulating nanoindentation through ML and is currently working on patenting his software and is in direct communication with a company interested in licensing it. He has reviewed articles for 10 international journals on multi-disciplinary topics. Dr Bimal Kumar Jha, Former Executive Director, Research and Development Centre for Iron & Steel, SAIL, Ranchi, graduated in Metallurgical Engineering from University of Roorkee in 1978 with unique distinction of being awarded with all the five medals of the Metallurgical Engineering Department. He joined RDCIS, SAIL in 1980 after completing M.Tech at IIT Kanpur. He completed his Ph.D. on TRIP Steels from University of Roorkee in 1996 as an External Candidate. Dr Jha in his various capacities in RDCIS spearheaded product development and commercialization activities of SAIL from 2005 to 2015. His keen interest in research and development is amply manifested through more than 140 numbers of publications in the journals of national and international repute and filing of 45 patents to his credit. He has been involved in setting up the national platform SRTMI (Steel Research and Technology Mission of India), a Ministry of Steel, Govt. of India. Also as a Steel Industry-Academia Interface (SIAI) convener he has pursued activities related to formation of SRTMI and Operating Committees on R&D to foster steel research and major technology developments of national importance in the steel sector. In 2015, Dr B K Jha was conferred the “National Metallurgist Award (Industry)” by Ministry of Steel, the highest honour in India for the metallurgical profession. He has also received the prestigious “Metallurgist of the Year Award” in 2002 from Ministry of Steel and O. P. Jindal Gold Medal in 2014 from Indian Institute of Metals (IIM). He was visiting professor IIT, Roorkee before joining National Institute of Foundry and Forge Technology (NIFFT), Ranchi, India as a professor.