Machine Learning Applied to Composite Materials (Composites Science and Technology)

個数：

Machine Learning Applied to Composite Materials (Composites Science and Technology)

Kushvaha, Vinod (EDT)/ M.R, Sanjay (EDT)/ Madhushri, Priyanka (EDT)

ウェブストア価格 ¥44,602（本体¥40,548）
Springer Verlag, Singapore（2022/11発売）
外貨定価 US$ 219.99
ポイント 405pt

提携先の海外書籍取次会社に在庫がございます。通常3週間で発送いたします。
【重要ご説明事項】
1. 納期遅延や、ご入手不能となる場合が若干ございます。
2. 複数冊ご注文の場合、分割発送となる場合がございます。
3. 美品のご指定は承りかねます。

●3Dセキュア導入とクレジットカードによるお支払いについて

【入荷遅延について】
世界情勢の影響により、海外からお取り寄せとなる洋書・洋古書の入荷が、表示している標準的な納期よりも遅延する場合がございます。
おそれいりますが、あらかじめご了承くださいますようお願い申し上げます。

◆画像の表紙や帯等は実物とは異なる場合があります。

◆ウェブストアでの洋書販売価格は、弊社店舗等での販売価格とは異なります。
また、洋書販売価格は、ご注文確定時点での日本円価格となります。
ご注文確定後に、同じ洋書の販売価格が変動しても、それは反映されません。

製本 Hardcover:ハードカバー版
言語 ENG
商品コード 9789811962776

Full Description

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.

Importance of machine learning in material science.- Machine Learning: A methodology to explain and predict material behavior.- Effect of aspect ratio on dynamic fracture toughness of particulate polymer composite using artificial neural network.- Methodology of K-Nearest Neighbor for predicting the fracture toughness of polymer composites.- Forward machine learning technique to predict dynamic fracture behavior of particulate composite.- Predictive modelling of fracture behavior in silica-filled polymer composite subjected to impact with varying loading rates.- Machine learning approach to determine the elastic modulus of Carbon fiber-reinforced laminates.- Effect of weight ratio on mechanical behaviour of natural fiber based biocomposite using machine learning.- Effect of natural fiber's mechanical properties and fiber matrix adhesion strength to design biocomposite.- Comparison of various machine learning algorithms to predict material behavior in GFRP.