Advances in Welding Metal Alloys, Dissimilar Metals and Additively Manufactured Parts / / edited by Giuseppe Casalino

• Record Nr.: UNINA9910674026803321
• Pubbl/distr/stampa: Basel : , : MDPI - Multidisciplinary Digital Publishing Institute, , 2018; ©2018
• Descrizione fisica: 1 online resource (ix, 222 pages) : illustrations
• Disciplina: 671.5023
• Soggetti: Welding; Welding - Vocational guidance
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: About the Special Issue Editor vii -- Preface to "Advances in Welding Metal Alloys, Dissimilar Metals and Additively Manufactured Parts" ix -- Giuseppe Casalino Advances in Welding Metal Alloys, Dissimilar Metals and Additively Manufactured Parts Reprinted from: Metals 2017, 7, 32, doi:10.3390/met7020032 1 -- Jeong Yeol Park, Jae Myung Lee and Myung Hyun Kim An Investigation of the Mechanical Properties of a Weldment of 7% Nickel Alloy Steels Reprinted from: Metals 2016, 6, 285, doi:10.3390/met6110285 . 6 -- Francisco-Javier C´arcel-Carrasco, Miguel-Angel P ´erez-Puig, Manuel Pascual-Guillam ´on and Rafael Pascual-Mart´ınez An Analysis of the Weldability of Ductile Cast Iron Using Inconel 625 for the Root Weld and Electrodes Coated in 97.6% Nickel for the Filler Welds Reprinted from: Metals 2016, 6, 283, doi:10.3390/met6110283 . 16 -- Ameth Fall, Mostafa Hashemi Fesharaki, Ali Reza Khodabandeh and Mohammad Jahazi Tool Wear Characteristics and Effect on Microstructure in Ti-6Al-4V Friction Stir Welded Joints Reprinted from: Metals 2016, 6, 275, doi:10.3390/met6110275 . 30 -- Pasquale Russo Spena, Manuela De Maddis, Gianluca D'Antonio and Franco Lombardi Weldability and Monitoring of Resistance Spot Welding of Q&P and TRIP Steels Reprinted from: Metals 2016, 6, 270, doi:10.3390/met6110270 . 42 -- Tingfeng Song, Xiaosong Jiang, Zhenyi Shao, Defeng Mo, Degui Zhu and Minhao Zhu The Interfacial Microstructure and Mechanical Properties of Diffusion-; Bonded Joints of 316L Stainless Steel and the 4J29 Kovar Alloy Using Nickel as an Interlayer Reprinted from: Metals 2016, 6, 263, doi:10.3390/met6110263 . 57 -- Kapil Gangwar, M. Ramulu, Andrew Cantrell and Daniel G. Sanders Microstructure and Mechanical Properties of Friction Stir Welded Dissimilar Titanium Alloys: TIMET-54M and ATI-425 Reprinted from: Metals 2016, 6, 252, doi:10.3390/met6100252 . 69 -- Yufeng Sun, Nobuhiro Tsuji and Hidetoshi Fujii Microstructure and Mechanical Properties of Dissimilar Friction Stir Welding between Ultrafine Grained 1050 and 6061-T6 Aluminum Alloys Reprinted from: Metals 2016, 6, 249, doi:10.3390/met6100249 . 83 -- Celalettin Yuce, Mumin Tutar, Fatih Karpat and Nurettin Yavuz The Optimization of Process Parameters and Microstructural Characterization of Fiber Laser Welded Dissimilar HSLA and MART Steel Joints Reprinted from: Metals 2016, 6, 245, doi:10.3390/met6100245 . 95 -- Hui-Jun Yi, Yong-Jun Lee and Kwang-O Lee TIG Dressing Effects on Weld Pores and Pore Cracking of Titanium Weldments Reprinted from: Metals 2016, 6, 243, doi:10.3390/met6100243 . 112 -- Hafiz Waqar Ahmad, Jeong Ho Hwang, Ju Hwa Lee and Dong Ho Bae An Assessment of the Mechanical Properties and Microstructural Analysis of Dissimilar Material Welded Joint between Alloy 617 and 12Cr Steel Reprinted from: Metals 2016, 6, 242, doi:10.3390/met6100242 . 124 -- Dongsheng Chai, Dongdong Wu, Guangyi Ma, Siyu Zhou, Zhuji Jin and Dongjiang Wu The Effects of Pulse Parameters on Weld Geometry and Microstructure of a Pulsed Laser Welding Ni-Base Alloy Thin Sheet with Filler Wire Reprinted from: Metals 2016, 6, 237, doi:10.3390/met6100237 . 135 -- Baohua Chang, Dong Du, Chenhui Yi, Bin Xing and Yihong Li Influences of Laser Spot Welding on Magnetic Property of a Sintered NdFeB Magnet Reprinted from: Metals 2016, 6, 202, doi:10.3390/met6090202 . 149 -- Rocku Oh, Duck Young Kim and Darek Ceglarek The Effects of Laser Welding Direction on Joint Quality for Non-Uniform Part-to-Part Gaps Reprinted from: Metals 2016, 6, 184, doi:10.3390/met6080184 . 158 -- Minjung Kang, Youngnam Ahn and Cheolhee Kim Gas Metal Arc Welding Using Novel CaO-Added Mg Alloy Filler Wire Reprinted from: Metals 2016, 6, 155, doi:10.3390/met6070155 . 173 -- Yunxia Chen, Xulei Wu, Xiaojing Wang and Hai Huang Effects of Reflow Time on the Interfacial Microstructure and Shear Behavior of the SAC/FeNi-Cu Joint Reprinted from: Metals 2016, 6, 109, doi:10.3390/met6050109 . 181 -- Yingping Ji, Sujun Wu and Dalong Zhao Microstructure and Mechanical Properties of Friction Welding Joints with Dissimilar Titanium Alloys Reprinted from: Metals 2016, 6, 108, doi:10.3390/met6050108 . 188 -- Rando Tungga Dewa, Seon Jin Kim, Woo Gon Kim and Eung Seon Kim Low Cycle Fatigue Behaviors of Alloy 617 (INCONEL 617) Weldments for High Temperature Applications Reprinted from: Metals 2016, 6, 100, doi:10.3390/met6050100 . 199.
• Sommario/riassunto: Welding technology has been taken for granted as a mature and established technology for too long. However, many new welding technologies have been included among the alternatives to joining materials. They come both from the areas of fusion and solid-state welding. Moreover, a recent approach has offered one more alternative. This is hybrid welding, which couples two or more welding sources in a cooperative or synergic welding mode. Welding engineers and scientists have the task to understand which is the best technology for a specific application. This task requires deep knowledge and great intelligence to tackle the challenge of producing light and smart structures and products.In this book, a glimpse of recent developments in metal alloy welding is presented. Laser, friction, and arc welding are the main protagonists of the papers that are included. Processes, ; materials, and tools are described and studied along with investigation procedures and numerical simulations.This book will make you aware of most of the subjects discussed in the scientific community and new potentialities of welding as a leading technology in manufacturing.I hope you enjoy reading this Special Issue, "Advances in Welding Metal Alloys, Dissimilar Metals and Additively Manufactured Parts".

Linear Algebra and Optimization for Machine Learning : A Textbook / / by Charu C. Aggarwal

• Record Nr.: UNINA9910410040003321
• Autore: Aggarwal Charu C.
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020
• ISBN: 3-030-40344-0
• Edizione: [1st ed. 2020.]
• Descrizione fisica: 1 online resource (507 pages) : illustrations
• Disciplina: 512.5
• Soggetti: Machine learning; Matrix theory; Algebra; Computers; Machine Learning; Linear and Multilinear Algebras, Matrix Theory; Information Systems and Communication Service
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Preface -- 1 Linear Algebra and Optimization: An Introduction -- 2 Linear Transformations and Linear Systems -- 3 Eigenvectors and Diagonalizable Matrices -- 4 Optimization Basics: A Machine Learning View -- 5 Advanced Optimization Solutions -- 6 Constrained Optimization and Duality -- 7 Singular Value Decomposition -- 8 Matrix Factorization -- 9 The Linear Algebra of Similarity -- 10 The Linear Algebra of Graphs -- 11 Optimization in Computational Graphs -- Index.
• Sommario/riassunto: This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout the book. A solution manual for the exercises at the end of each chapter is available to teaching instructors. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows: 1. Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, matrix factorization, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts. 2. Optimization and its applications: Much of machine learning is posed as an optimization problem in which we try to maximize the accuracy of regression and classification models. The “parent problem” of optimization-centric machine learning is least-squares regression. Interestingly, this problem arises in both linear algebra and optimization, and is one of the key connecting problems of the two fields. Least-squares regression is also the starting point for support vector machines, logistic regression, and recommender systems. Furthermore, the methods for dimensionality reduction and matrix factorization also require the development of optimization methods. A general view of optimization in computational graphs is discussed together with its applications to back propagation in neural networks. A frequent challenge faced by beginners in machine learning is the extensive background required in linear algebra and optimization. One problem is that the existing linear algebra and optimization courses are not specific to machine learning; therefore, one would typically have to complete more course material than is necessary to pick up machine learning. Furthermore, certain types of ideas and tricks from optimization and linear algebra recur more frequently in machine learning than other application-centric settings. Therefore, there is significant value in developing a view of linear algebra and optimization that is better suited to the specific perspective of machine learning.