N. Muthukumar; K. Ganesh; Sanjay Mohapatra; K. Tamizhjyothi; R. M. Nachiappan Emerald Publishing Limited (2022) Saatavuus: Tilaustuote Kovakantinen kirja
Nishikant A. Raut; Dadasaheb M. Kokare; Bharat A. Bhanvase; Kirtikumar R. Randive; Sanjay J. Dhoble Elsevier - Health Sciences Division (2023) Saatavuus: Tilaustuote Pehmeäkantinen kirja
Nishikant A. Raut; Dadasaheb M. Kokare; Bharat Bhanvase; Kirtikumar R. Randive; Sanjay J. Dhoble Elsevier - Health Sciences Division (2023) Saatavuus: Tilaustuote Pehmeäkantinen kirja
Sanjay Rajagopalan; Steven M. Dean; Emile R. Mohler; Debabrata Mukherjee Lippincott Williams and Wilkins (2011) Saatavuus: Tilaustuote Pehmeäkantinen kirja
Springer Verlag, Singapore Sivumäärä: 198 sivua Asu: Kovakantinen kirja Painos: 1st ed. 2022 Julkaisuvuosi: 2022, 30.11.2022 (lisätietoa) Kieli: Englanti
This book introduces the approach of Machine Learning (ML) based predictive models in the design of composite materials to achieve the required properties for certain applications. ML can learn from existing experimental data obtained from very limited number of experiments and subsequently can be trained to find solutions of the complex non-linear, multi-dimensional functional relationships without any prior assumptions about their nature. In this case the ML models can learn from existing experimental data obtained from (1) composite design based on various properties of the matrix material and fillers/reinforcements (2) material processing during fabrication (3) property relationships. Modelling of these relationships using ML methods significantly reduce the experimental work involved in designing new composites, and therefore offer a new avenue for material design and properties. The book caters to students, academics and researchers who are interested in the field of materialcomposite modelling and design.