Author: Mustafa Mamduh Mustafa Awd
Publisher: Springer Nature
ISBN: 3658402377
Category : Computers
Languages : en
Pages : 289
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Book Description
Fatigue failure of structures used in transportation, industry, medical equipment, and electronic components needs to build a link between cutting-edge experimental characterization and probabilistically grounded numerical and artificially intelligent tools. The physics involved in this process chain is computationally prohibitive to comprehend using traditional computation methods. Using machine learning and Bayesian statistics, a defect-correlated estimate of fatigue strength was developed. Fatigue, which is a random variable, is studied in a Bayesian-based machine learning algorithm. The stress-life model was used based on the compatibility condition of life and load distributions. The defect-correlated assessment of fatigue strength was established using the proposed machine learning and Bayesian statistics algorithms. It enabled the mapping of structural and process-induced fatigue characteristics into a geometry-independent load density chart across a wide range of fatigue regimes.
Author: Sharlotte L. B. Kramer
Publisher: Springer Nature
ISBN: 3031504747
Category :
Languages : en
Pages : 101
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Book Description
Author: Gurminder Singh
Publisher: Elsevier
ISBN: 0443221464
Category : Technology & Engineering
Languages : en
Pages : 0
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Book Description
Machine Learning for Powder-based Metal Additive Manufacturing outlines machine learning (ML) methods for additive manufacturing (AM) of metals that will improve product quality, optimize manufacturing processes, and reduce costs. The book combines ML and AM methods to develop intelligent models that train AM techniques in pre-processing, process optimization, and post-processing for optimized microstructure, tensile and fatigue properties, and biocompatibility for various applications. The book covers ML for design in AM, ML for materials development and intelligent monitoring in metal AM, both geometrical deviation and physics informed machine learning modeling, as well as data-driven cost estimation by ML. In addition, optimization for slicing and orientation, ML to create models of materials for AM processes, ML prediction for better mechanical and microstructure prediction, and feature extraction by sensing data are all covered, and each chapter includes a case study. Covers machine learning (ML) methods for additive manufacturing (AM) of metals that will improve product quality, optimize manufacturing processes, and reduce costs Combines ML and AM methods to develop intelligent models that train AM techniques in pre-processing, process optimization, and post-processing for optimized microstructure, tensile and fatigue properties, and biocompatibility for various applications Discusses algorithm development of ML for metal AM, metal AM process modeling and optimization, mathematical and simulation studies of metal AM, and pre- and post-processing smart methods for metal AM
Author: Filippo Berto
Publisher: Elsevier
ISBN: 0323998313
Category : Technology & Engineering
Languages : en
Pages : 321
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Book Description
Fatigue in Additive Manufactured Metals provides a brief overview of the fundamental mechanics involved in metal fatigue and fracture, assesses the unique properties of additive manufactured metals, and provides an in-depth exploration of how and why fatigue occurs in additive manufactured metals. Additional sections cover solutions for preventing it, best-practice design methods, and more. The book recommends cutting-edge evidence-based approaches for designing longer lasting additive manufactured metals, discusses the latest trends in the field and the various aspects of low cycle fatigue, and looks at both post-treatment and manufacturing process-based solutions. By providing international standards and testing procedures of additive manufactured metal parts and discussing the environmental impacts of additive manufacturing of metals and outlining simulation and modeling scenarios, this book is an ideal resource for users in industry. Discusses the underlying mechanisms controlling the fatigue behavior of additive manufactured metal components as well as how to improve the fatigue life of these components via both manufacturing processes and post-processing Studies the variability of properties in additive manufactured metals, the effects of different process conditions on mechanical reliability, probabilistic versus deterministic aspects, and more Outlines nondestructive failure analysis techniques and highlights the effects of unique microstructural characteristics on fatigue in additive manufactured metals
Author: Shyam Suresh
Publisher: Linköping University Electronic Press
ISBN: 9179298508
Category :
Languages : en
Pages : 41
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Book Description
Additive Manufacturing (AM) is gaining popularity in aerospace and automotive industries. This is a versatile manufacturing process, where highly complex structures are fabricated and together with topology optimization, a powerful design tool, it shares the property of providing a very large freedom in geometrical form. The main focus of this work is to introduce new developments of Topology Optimization (TO) for metal AM. The thesis consists of two parts. The first part introduces background and theory, where TO and adjoint sensitivity analysis are described. Furthermore, methodology used to identify surface layer and high-cycle fatigue are introduced. In the second part, three papers are appended, where the first paper presents the treatment of surface layer effects, while the second and third papers provide high-cycle fatigue constraint formulations. In Paper I, a TO method is introduced to account for surface layer effects, where different material properties are assigned to bulk and surface regions. In metal AM, the fabricated components in as-built surface conditions significantly affect mechanical properties, particularly fatigue properties. Furthermore, the components are generally in-homogeneous and have different microstructures in bulk regions compared to surface regions. We implement two density filters to account for surface effects, where the width of the surface layer is controlled by the second filter radius. 2-D and 3-D numerical examples are treated, where the structural stiffness is maximized for a limited mass. For Papers II and III, a high-cycle fatigue constraint is implemented in TO. A continuous-time approach is used to predict fatigue-damage. The model uses a moving endurance surface and the development of damage occurs only if the stress state lies outside the endurance surface. The model is applicable not only for isotropic materials (Paper II) but also for transversely isotropic material properties (Paper III). It is capable of handling arbitrary load histories, including non-proportional loads. The anisotropic model is applicable for additive manufacturing processes, where transverse isotropic properties are manifested not only in constitutive elastic response but also in fatigue properties. Two optimization problems are solved: In the first problem the structural mass is minimized subject to a fatigue constraint while the second problem deals with stiffness maximization subjected to a fatigue constraint and mass constraint. Several numerical examples are tested with arbitrary load histories.
Author: Yi Ming Ren
Publisher:
ISBN:
Category :
Languages : en
Pages : 193
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Book Description
Big data plays an important role in the fourth industrial revolution, which requires engineersand computers to fully utilize data to make smart decisions to optimize industrial processes. In the additive manufacturing (AM) industry, laser powder bed fusion (LPBF) and direct metal laser solidification (DMLS) have been receiving increasing interest in research because of their outstanding performance in producing mechanical parts with ultra-high precision and variable geometries. However, due to the thermal and mechanical complexity of these processes, printing failures are often encountered, resulting in defective parts and even destructive damage to the printing platform. For example, heating anomalies can result in thermal and mechanical stress on the building part and eventually lead to physical problems such as keyholing and lack of fusion. Many of the aforementioned process errors occur during the layer-to-layer printing process, which makes in-situ process monitoring and quality control extremely important. Although in-situ sensors are extensively developed to investigate and record information from the real-time printing process, the lack of efficient in-situ defect detection techniques specialized for AM processes makes real-time process monitoring and data analysis extremely difficult. Therefore, to help process engineers analyze sensor information and efficiently filter monitoring data for transport and storage, machine learning and data processing algorithms are often implemented. These algorithms integrate the functionality of automated data processing, transferring, and analytics. In particular, sensor data often takes the form of images, and thus, a prominent approach to conducting image analytics is through the use of convolutional neural networks (CNN). Nevertheless, the industrial utilization of machine learning methods often encounters problems such as limited and biased training datasets. Hence, simulation methods, such as the finite-element method (FEM), are used to augment and improve the training of the deep learning process monitoring algorithm. Motivated by the above considerations, this dissertation presents the use of machine learning techniques in process monitoring, data analytics, and data transfer for additive manufacturing processes. The background, motivation, and organization of this dissertation are first presented in the Introduction chapter. Then, the use of FEM to model and replicate in-situ sensor data is presented, followed by the use of machine learning techniques to conduct real-time process monitoring trained from a mixture of experimental and replicated sensor image data. In particular, a cross-validation algorithm is developed through the exploitation of different sensor advantages and is integrated into the machine learning-assisted process monitoring algorithm. Next, an application of machine learning (ML) to non-image sensor data is presented as a neural network model that is developed to estimate in-situ powder thickness to account for recoater arm interactions. Subsequently, an integrated AM smart manufacturing framework is proposed which connects the different manufacturing hierarchies, particularly at the local machine, factory, and cloud level. Finally, in addition to the AM industry, the use of machine learning, specifically neural networks, in model predictive control (MPC) for dynamic nonlinear processes is reviewed and discussed.
Author: John O. Milewski
Publisher: Springer
ISBN: 3319582054
Category : Technology & Engineering
Languages : en
Pages : 343
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Book Description
This engaging volume presents the exciting new technology of additive manufacturing (AM) of metal objects for a broad audience of academic and industry researchers, manufacturing professionals, undergraduate and graduate students, hobbyists, and artists. Innovative applications ranging from rocket nozzles to custom jewelry to medical implants illustrate a new world of freedom in design and fabrication, creating objects otherwise not possible by conventional means. The author describes the various methods and advanced metals used to create high value components, enabling readers to choose which process is best for them. Of particular interest is how harnessing the power of lasers, electron beams, and electric arcs, as directed by advanced computer models, robots, and 3D printing systems, can create otherwise unattainable objects. A timeline depicting the evolution of metalworking, accelerated by the computer and information age, ties AM metal technology to the rapid evolution of global technology trends. Charts, diagrams, and illustrations complement the text to describe the diverse set of technologies brought together in the AM processing of metal. Extensive listing of terms, definitions, and acronyms provides the reader with a quick reference guide to the language of AM metal processing. The book directs the reader to a wealth of internet sites providing further reading and resources, such as vendors and service providers, to jump start those interested in taking the first steps to establishing AM metal capability on whatever scale. The appendix provides hands-on example exercises for those ready to engage in experiential self-directed learning.
Author: Eric Ott
Publisher: Springer
ISBN: 3319894803
Category : Technology & Engineering
Languages : en
Pages : 1118
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Book Description
This technical meeting will focus on Alloy 718 and Superalloys in this class relative to alloy and process development, production, product applications, trends and the development of advanced modeling tools. The symposium provides an opportunity for authors to present technical advancements relative to a broad spectrum of areas while assessing their impact on related fields associated with this critical alloy group. There are continuing innovations relative to these alloys as well as novel processing techniques which continue to extend applications in very challenging environments ranging from corrosion resistance in the deep sea to high-stressed space applications.
Author: James C. Sobotka
Publisher:
ISBN:
Category : Additive manufacturing
Languages : en
Pages : 14
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Book Description
Additive manufacturing (AM) is a novel fabrication process with the potential to create unique parts beyond the capabilities of conventional manufacturing methods. However, because the AM process produces both the part and the material simultaneously, certification of safety-critical structural metallic parts requires new approaches that more explicitly quantify the influence of the AM process on the quality of the part. For example, the AM process can, when not properly designed and controlled, create defects, such as porosity, lack of fusion, or severe surface roughness. If these defects occur in a region of significant cyclic stress, they can promote the nucleation of cracks that, in turn, can grow to cause fracture of the part. This paper summarizes recent investigations into analytical characterization of defects and their effect on the structural integrity of additive manufactured parts. Specifically, this work documents predictive mechanics models that characterize the effects of pores on fatigue crack formation. These results provide insight into the effects of pore size and shape on fatigue lives under uniaxial tension.
Author: Rachael C Tighe
Publisher: Springer Nature
ISBN: 3031174755
Category : Technology & Engineering
Languages : en
Pages : 96
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Book Description
Thermomechanics & Infrared Imaging, Inverse Problem Methodologies and Mechanics of Additive & Advanced Manufactured Materials, Volume 6 of the Proceedings of the 2022 SEM Annual Conference & Exposition on Experimental and Applied Mechanics, the sixth volume of six from the Conference, brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on a wide range of areas, including: Test Design and Inverse Method Algorithms Inverse Problems: Virtual Fields Method Material Characterizations Using Thermography Fatigue, Damage & Fracture Evaluation Using Infrared Thermography Residual Stress Mechanics of Additive & Advanced Manufactured Materials
