Author: Reza Mojahed Yazdi
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
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: Ying Zhang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
"Additive manufacturing (AM), which is also widely known as three-dimensional (3D) printing, has been a new trend in the manufacturing process in recent years. It can produce parts following a generated 3D model by adding layers of materials and fusing them. The main advantage of AM is the ability to enable customization and fabrication of complex geometries such as lattice structures, which are extremely difficult to manufacture in the subtractive manufacturing process. Although AM has been employed in many industrial applications, it is still difficult for beginning users to ensure the success of every print. It requires users to have a deep understanding of AM techniques to fully utilize this technology. The printing may fail owing to many factors such as the poor selection of the build orientation, materials, process settings, and insufficient geometric support for overhangs. It is difficult for non-AM experts to determine whether their designs are printable through a selected AM process, and it is even more difficult for them to make proper modifications without expert guidance before the fabrication. To fill these knowledge gaps, this study investigated the use of machine learning (ML) to assess the manufacturability of designs for AM processes. A web-based automated manufacturability analyzer and recommender for AM was developed as the implementation of the developed hybrid ML models. This tool can be used for the first-level evaluation of designs for novice AM users such as designers to ensure efficiency in terms of time and cost required for AM fabrications.The major contributions of this thesis are listed as follows: 1.Establishment of a unique database for the laser-based powder bed fusion (LPBF) process and fused deposition modeling (FDM) process.2.Development of a novel approach on manufacturability analysis of LPBF using hybrid ML models. The models consider both process information and design perspectives. 3.Development of a hybrid sparse convolutional neural network (CNN) to predict manufacturability to increase the efficiency and effectiveness of the ML models.4.Development of a recommendation system to provide potential modifications to assist users on AM printing.5.A web-based application of analyzer and recommender was implemented to provide a comprehensive and easy-to-access manufacturability analysis to novice AM users.6.Demonstration of how data-driven approaches can help on design and manufacturing processes and the framework can be extended to any process where parts can be classified based on visual inspection and basic labeling"--
Author: Mutahar Safdar
Publisher: Springer Nature
ISBN: 3031321545
Category : Technology & Engineering
Languages : en
Pages : 151
Get Book Here
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.
Author: Gurminder Singh
Publisher: Elsevier
ISBN: 0443221464
Category : Technology & Engineering
Languages : en
Pages : 291
Get Book Here
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: Tuhin Mukherjee
Publisher: John Wiley & Sons
ISBN: 139420227X
Category : Technology & Engineering
Languages : en
Pages : 453
Get Book Here
Book Description
Theory and Practice of Additive Manufacturing Discover the ins and outs of additive manufacturing in this student-friendly textbook Also known as 3D printing, additive manufacturing is a process by which layers of material are added to create three-dimensional objects guided by a digital model. It has revolutionized the design and manufacture of customized products, facilitating the rapid, flexible production of a huge range of goods. It promises to revolutionize manufacturing engineering, shorten industrial supply chains, and more. Theory and Practice of Additive Manufacturing provides the first introduction to this subject designed specifically for students. Balancing the underlying theories behind additive manufacturing with concrete applications, it guides readers through basic processes, essential tools and materials, and more. The result is ideal for readers looking to bring additive manufacturing to bear on engineering or industry careers of almost any kind. Theory and Practice of Additive Manufacturing features: Over 100 worked-out example problems Detailed discussion of the emerging digital tools including mechanistic modeling, machine learning, and more Commitment to pedagogy and reinforcement geared toward student learning outcomes Theory and Practice of Additive Manufacturing is ideal for undergraduate and graduate students and instructors in introductory additive manufacturing courses, as well as practicing engineers and researchers working in industries that use additive manufacturing technologies, including aerospace, automotive, and consumer goods.
Author: António Torres Marques
Publisher: Springer Nature
ISBN: 3030445224
Category : Technology & Engineering
Languages : en
Pages : 346
Get Book Here
Book Description
This book focuses on the emerging additive manufacturing technology and its applications beyond state-of-the-art, fibre-reinforced thermoplastics. It also discusses the development of a hybrid, integrated process that combines additive and subtractive operations in a single-step platform, allowing CAD-to-Part production with freeform shapes using long or continuous fibre-reinforced thermoplastics. The book covers the entire value chain of this next-generation technology, from part design and materials composition to transformation stages, product evaluation, and end-of-life studies. Moreover, it addresses the following engineering issues: • Design rules for hybrid additive manufacturing; • Thermoplastic compounds for high-temperature and -strength applications; • Advanced extrusion heads and process concepts; • Hybridisation strategies; • Software ecosystems for hAM design, pre-processing, process planning, emulating and multi-axis processing; • 3D path generators for hAM based on a multi-objective optimisation algorithm that matches the recent curved adaptive slicing method with a new transversal scheme; • hAM parameters, real-time monitoring and closed-loop control; • Multiparametric nondestructive testing (NDT) tools customised for FRTP AM parts; • Sustainable manufacturing processes validated by advanced LCA/LCC models.
Author:
Publisher:
ISBN:
Category : Additive manufacturing
Languages : en
Pages : 0
Get Book Here
Book Description
Additive manufacturing (AM), which builds a single part directly from a 3D CAD model in a layer-by-layer manner, can fabricate complex component with intricate geometry in a time- and cost-saving manner.AM is thus gaining ever-increasing popularity across many industries. However, accompanied with its unique building manner and benefits thereof are the significantly complicated physics behind the AM process. This fact poses great challenges in modeling and understanding the underlying process-structure-property (P-S-P) relationship, which however is vital to efficient AM process optimization and quality control. With the advancement of machine learning (ML) models and increasing availability of AM-related digital data, ML-based data-driven modeling has recently emerged as a promising approach towards exhaustively exploring and fully understanding AM P-S-P relationship. Nonetheless, many of existing ML-based AM modeling severely under-utilize the powerful ML models by using them as simple regression tools, and largely neglect their distinct advantage in explicitly handling complex-data (e.g., image and sequence) involved data-driven modeling problems and other versatilities. To further explore and unlock the tremendous potential of ML, this research aims to attack two significant research problems: (1) from the data or pre-data-driven-modeling aspect: can we use ML to improve AM data via ML-assisted data collection, processing and acquirement? (2) from the data driven modeling aspect: can we use ML to build more capable data-driven models, which can act as a full (or maximum) substitute of physics-based model for high-level AM modeling or even realistic AM simulation? To adequately address the above questions, the current research presents a ML-based data-driven AM modeling framework. It attempts to provide a comprehensive ML-based solution to data-driven modeling and simulation of various physical events throughout the AM lifecycle, from process to structure and property. A variety of ML models, including Gaussian process (GP), multilayer perceptron (MLP), convolutional neural network (CNN), recurrent neural network (RNN) and their variants, are leveraged to handle representative data-driven modeling problems with different quantities of interest (QoI). They include data-driven process modeling (melt pool, temperature field), structure modeling (porosity structure) and property modeling (stress field, stress-strain curve). The results show that this research can break existing limitations of those five data-driven AM modeling in terms of modeling fidelity, accuracy and/or efficiency. It thus well addresses the two research questions that are key in significantly advancing data-driven AM modeling. In addition, although the current research uses five representative physical events in AM as examples, the data-driven methodologies developed should shed light on data-driven modeling of many other physical events in AM and beyond.
Author: Mihail Ionescu
Publisher: Trans Tech Publications Ltd
ISBN: 3036410112
Category : Technology & Engineering
Languages : en
Pages : 172
Get Book Here
Book Description
Special topic volume with invited peer-reviewed papers only
Author: Seyyed Hadi Seifi Shishavan
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Additive manufacturing (AM) is a novel fabrication technique capable of producing highly complex parts. Nevertheless, a major challenge is improving the quality of the fabricated parts. While there are several ways of approaching this problem, developing data-driven methods that use AM process signatures to identify these part anomalies can be rapidly applied to improve the overall part quality during the build. The objective of this dissertation is to model multiple processes within the AM to quantify the quality of the parts and reduced the uncertainty due to variation in input process parameters. The objective of first study is to build a new layer-wise process signature model to characterize the thermal-defect relationship. Based on melt pool images, we propose novel layer-wise key process signatures, which are calculated using multilinear principal component analysis (MPCA) and are directly correlated with layer-wise quality of the part. Second study broadens the spectrum of the dissertation to include mechanical properties, where a novel two-phase modeling methodology is proposed for fatigue life prediction based on in-situ monitoring of thermal history. In final study, our objective is to pave the way toward a better understanding of the uncertainty in the process-defect-structures relationship using an inverse robust design exploration method. The method involves two steps. In the first step, mathematical models are developed to characterize and model the forward flow of information in the intended additive manufacturing process. In the second step, inverse robust design exploration is carried out to investigate satisfying design solutions that meet multiple AM goals.
Author: L. Jyothish Kumar
Publisher: Springer
ISBN: 9811303053
Category : Technology & Engineering
Languages : en
Pages : 308
Get Book Here
Book Description
This book presents a selection of papers on advanced technologies for 3D printing and additive manufacturing, and demonstrates how these technologies have changed the face of direct, digital technologies for the rapid production of models, prototypes and patterns. Because of their wide range of applications, 3D printing and additive manufacturing technologies have sparked a powerful new industrial revolution in the field of manufacturing. The evolution of 3D printing and additive manufacturing technologies has changed design, engineering and manufacturing processes across such diverse industries as consumer products, aerospace, medical devices and automotive engineering. This book will help designers, R&D personnel, and practicing engineers grasp the latest developments in the field of 3D Printing and Additive Manufacturing.
