Reliability of Selective Laser Melted Alsi12 Alloy for Quasistatic and Fatigue Applications (Werkstofftechnische Berichte │ Reports of Materials Sc") / Siddique, Shafaqat

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Reliability of Selective Laser Melted Alsi12 Alloy for Quasistatic and Fatigue Applications (Werkstofftechnische Berichte │ Reports of Materials Sc")

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Reliability of Selective Laser Melted Alsi12 Alloy for Quasistatic and Fatigue Applications (Werkstofftechnische Berichte │ Reports of Materials Sc")

Siddique, Shafaqat

ウェブストア価格 ¥11,535（本体¥10,487）
Springer Vieweg（2018/09発売）
外貨定価 US$ 54.99
ポイント 104pt

オンデマンド(OD/POD)版です。キャンセルは承れません。

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製本 Paperback:紙装版/ペーパーバック版／ページ数 146 p.
言語 ENG
商品コード 9783658234249

Full Description

Selective laser melting (SLM) has established itself as the most prominent additive manufacturing (AM) process for metallic structures in aerospace, automotive and medical industries. For a reliable employment of this process, it has to conform to the demanding requirements of these industries in terms of quasistatic and, especially, fatigue performance. Shafaqat Siddique identifies the influence of SLM processing conditions on the microstructural features, and their corresponding influence on the mechanical behavior of the processed AlSi12 alloy structures. The author also gives insight into integrated manufacturing by combining conventional and SLM processes to get the synergic benefits. Requirements for fatigue-resistant designs in additive manufacturing are highlighted, and a novel method is developed for agile fatigue life prediction. About the Author

Shafaqat Siddique worked as Scientific Assistant at TU Dortmund University, Department of Materials Test Engineering (WPT), headed by Prof. Dr.-Ing. Frank Walther, and completed his Ph.D. research in cooperation with Laser Zentrum Nord (LZN) in Hamburg. He continues his post-doctoral research at TU Dortmund University, Germany.

State of the art and investigation methodology.- Characterization, quasistatic and fatigue behavior of AlSi12 alloy.- Hybrid AlSi12 alloy structures.- Fatigue prediction methodology.