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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: 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: Gurminder Singh
Publisher: Elsevier
ISBN: 0443221464
Category : Technology & Engineering
Languages : en
Pages : 291
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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: Reza Mojahed Yazdi
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Laser powder bed fusion (LPBF) is a technique of additive manufacturing (AM) that is often used to construct a metal object layer-by-layer. The quality of AM builds depends to a great extent on the minimization of different defects such as porosity and cracks that could occur by process deviation during printing operation. Therefore, there is a need to develop new analytical methods and tools to equip the LPBF process with the inspection frameworks that assess the process condition and monitor the porosity defect in real-time. Advanced sensing is recently integrated with the AM machines to cope with process complexity and improve information visibility. This opportunity lays the foundation for online monitoring and assessment of the in-process build layer. This study presents a hybrid deep neural network structure with two types of input data to monitor the process parameters that result in porosity defect in cylinders' layers. Results demonstrate that statistical features extracted by wavelet transform and texture analysis along with original powder bed images, assist the model to reach a robust performance. In order to illustrate the fidelity of the proposed model, the capability of the main pipeline is examined and compared with different machine learning models. Eventually, the proposed framework identified the process conditions with an F-score of 97.14\%. This salient flaw detection ability is conducive to repair the defect in real-time and assure the quality of the final part before the completion of the process.
Author: Sachin Salunkhe
Publisher: Engineering Science Reference
ISBN: 9781799885160
Category : Additive manufacturing
Languages : en
Pages : 272
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Book Description
"This book provides introductory instruction on how to learn how to use artificial intelligence to produce additively manufactured parts, including a description of the starting points, what you can know, how it blends and how artificial intelligence in additive manufacturing apply"--
Author: Yi Zhang
Publisher: Elsevier
ISBN: 0128225599
Category : Technology & Engineering
Languages : en
Pages : 252
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Book Description
Multiscale Modeling of Additively Manufactured Metals: Application to Laser Powder Bed Fusion Process provides comprehensive coverage on the latest methodology in additive manufacturing (AM) modeling and simulation. Although there are extensive advances within the AM field, challenges to predictive theoretical and computational approaches still hinder the widespread adoption of AM. The book reviews metal additive materials and processes and discusses multiscale/multiphysics modeling strategies. In addition, coverage of modeling and simulation of AM process in order to understand the process-structure-property relationship is reviewed, along with the modeling of morphology evolution, phase transformation, and defect formation in AM parts. Residual stress, distortion, plasticity/damage in AM parts are also considered, with scales associated with the spatial, temporal and/or material domains reviewed. This book is useful for graduate students, engineers and professionals working on AM materials, equipment, process, development and modeling. Includes the fundamental principles of additive manufacturing modeling techniques Presents various modeling tools/software for AM modeling Discusses various design methods and how to optimize the AM process using these models
Author: Yong Ren
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Additive manufacturing (AM) provides a transformative approach for industrial applications, enabling the fabrication of near-net-shape components directly from computer-aided design files. As a subcategory of metal AM processes, Laser Powder Bed Fusion (L-PBF) utilizes a high-speed, fine-diameter laser heat source to melt layers of powder that have been evenly distributed by a recoater. While L-PBF has emerged as the most widely-used commercial metal AM technology, numerous critical challenges still persist in process modeling and control of this approach. Addressing these issues is crucial for enhancing the geometric accuracy and overall quality of additive manufactured components. The objective of this research is to employ machine learning and numerical techniques for the development of comprehensive multiscale models, facilitating prediction and control in the L-PBF process across various process conditions. Using machine learning algorithms allows for constructing robust computational models based on training data, offering accurate predictions and informed decision-making for a wide range of physical systems. In this research, a variety of machine learning techniques were primarily used for fine-scale modeling and control of L-PBF processes. This was demonstrated through single-layer multi-track cases as a proof-of-concept study. In order to accurately model the relationship between process parameters and melt-pool sizes, a physics-informed method was adopted to identify critical input features for machine learning models. A two-level architecture was implemented for both model training and validation. Notably, the initial temperature at the melting point was recognized as a crucial variable in characterizing the thermal history for precise melt-pool size predictions. To achieve consistent melt-pool distribution during multi-track laser processing, a physics-informed optimal control method was devised to adjust laser power based on Gaussian process regression. The study's findings demonstrate that nonlinear regression analysis techniques, such as Gaussian process regression, are effective in predicting melt-pool geometry. When these techniques are further combined with optimal control, they can regulate the melt-pool size to a desired reference value. Regarding the evolution of temperature at the part-scale level, a novel finite-difference model was introduced, providing fast predictions of interlayer temperature and facilitating model-based thermal control. Interlayer temperature, defined as the layer temperature after powder spreading but before scanning a new layer, serves as the initial condition for the subsequent scan and thus plays an important role in the melt-pool morphology and the final build quality. The effectiveness of the proposed modeling method was evaluated through thermal analysis of a square-canonical geometry made from Inconel 718. Based on the part-scale thermal model, an optimal control utilizing layer-wise laser power adjustments was further developed to regulate the interlayer temperature below a preset threshold, thereby mitigating excessive heat buildup during the build process. The optimized laser power profiles, initially obtained by solving a convex program based on the finite-difference model, were then programmed on the EOS M280 system for a feedforward control to build the square-canonical parts. In-situ, real-time measurements of interlayer temperature were collected using infrared (IR) thermal imaging during the build process to validate the model and control. Post-process optical micrographs were also captured to compare the melt-pool morphology under optimized laser power profiles with that obtained under the default constant laser power. The control performance was evaluated through numerical simulations and experimental studies. Research findings confirm the efficacy of the proposed optimal thermal control in reducing overheating during the L-PBF build process.
Author: IEEE Staff
Publisher:
ISBN: 9781538652992
Category :
Languages : en
Pages :
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Book Description
IPSN (part of CPSWEEK) brings together researchers from academia, industry, and government to present and discuss recent advances in both theoretical and experimental research Its scope includes signal and image processing, information and coding theory, databases and information management, distributed algorithms, networks and protocols, wireless communications, collaborative objects and the Internet of Things, machine learning, mobile and social sensing, and embedded systems design Of special interest are contributions at the confluence of multiple of these areas
Author: Balasubramanian, K.R.
Publisher: IGI Global
ISBN: 1799840557
Category : Technology & Engineering
Languages : en
Pages : 348
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Book Description
Additive manufacturing (AM) of metals and composites using laser energy, direct energy deposition, electron beam methods, and wire arc melting have recently gained importance due to their advantages in fabricating the complex structure. Today, it has become possible to reliably manufacture dense parts with certain AM processes for many materials, including steels, aluminum and titanium alloys, superalloys, metal-based composites, and ceramic matrix composites. In the near future, the AM material variety will most likely grow further, with high-performance materials such as intermetallic compounds and high entropy alloys already under investigation. Additive Manufacturing Applications for Metals and Composites is a pivotal reference source that provides vital research on advancing methods and technological developments within additive manufacturing practices. Special attention is paid to the material design of additive manufacturing of parts, the choice of feedstock materials, the metallurgical behavior and synthesis principle during the manufacturing process, and the resulted microstructures and properties, as well as the relationship between these factors. While highlighting topics such as numerical modeling, intermetallic compounds, and statistical techniques, this publication is ideally designed for students, engineers, researchers, manufacturers, technologists, academicians, practitioners, scholars, and educators.
Author: Geoffrey J. McLachlan
Publisher: John Wiley & Sons
ISBN: 0470191600
Category : Mathematics
Languages : en
Pages : 399
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Book Description
The only single-source——now completely updated and revised——to offer a unified treatment of the theory, methodology, and applications of the EM algorithm Complete with updates that capture developments from the past decade, The EM Algorithm and Extensions, Second Edition successfully provides a basic understanding of the EM algorithm by describing its inception, implementation, and applicability in numerous statistical contexts. In conjunction with the fundamentals of the topic, the authors discuss convergence issues and computation of standard errors, and, in addition, unveil many parallels and connections between the EM algorithm and Markov chain Monte Carlo algorithms. Thorough discussions on the complexities and drawbacks that arise from the basic EM algorithm, such as slow convergence and lack of an in-built procedure to compute the covariance matrix of parameter estimates, are also presented. While the general philosophy of the First Edition has been maintained, this timely new edition has been updated, revised, and expanded to include: New chapters on Monte Carlo versions of the EM algorithm and generalizations of the EM algorithm New results on convergence, including convergence of the EM algorithm in constrained parameter spaces Expanded discussion of standard error computation methods, such as methods for categorical data and methods based on numerical differentiation Coverage of the interval EM, which locates all stationary points in a designated region of the parameter space Exploration of the EM algorithm's relationship with the Gibbs sampler and other Markov chain Monte Carlo methods Plentiful pedagogical elements—chapter introductions, lists of examples, author and subject indices, computer-drawn graphics, and a related Web site The EM Algorithm and Extensions, Second Edition serves as an excellent text for graduate-level statistics students and is also a comprehensive resource for theoreticians, practitioners, and researchers in the social and physical sciences who would like to extend their knowledge of the EM algorithm.
Author: Mutahar Safdar
Publisher: Springer Nature
ISBN: 3031321545
Category : Technology & Engineering
Languages : en
Pages : 151
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Book Description
This book is a comprehensive guide to the latest developments in data-driven additive manufacturing (AM). From data mining and pre-processing to signal processing, computer vision, and more, the book covers all the essential techniques for preparing AM data. Readers willl explore the key physical and synthetic sources of AM data throughout the life cycle of the process and learn about feature engineering techniques, pipelines, and resulting features, as well as their applications at each life cycle phase. With a focus on featurization efforts from reviewed literature, this book offers tabular summaries for major data sources and analyzes feature spaces at the design, process, and structure phases of AM to uncover trends and insights specific to feature engineering techniques. Finally, the book discusses current challenges and future directions, including AI/ML/DL readiness of AM data. Whether you're an expert or newcomer to the field, this book provides a broader summary of the status and future of data-driven AM technology.