Download or read book Model Learning and Predictive Control of Laser Powder Bed Fusion written by Yong Ren and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Machine Learning Modeling with Application to Laser Powder Bed Fusion Additive Manufacturing Process written by Yi Ming Ren and published by . This book was released on 2022 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Prediction of Meltpool Depth in Laser Powder Bed Fusion Using In process Sensor Data Part level Thermal Simulations and Machine Learning written by Grant Alan Maxwell King and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of this thesis is the prevention of flaw formation in laser powder bed fusion additive manufacturing process. As a step towards this goal, the objective of this work is to predict meltpool depth as a function of in-process sensor data, part-level thermal simulations, and machine learning. As motivated in NASA's Marshall Space Flight Center specification 3716, prediction of meltpool depth is important because: (1) it can serve as a surrogate to estimate process status without the need for expensive post-process characterization, and (2) the meltpool depth provides an avenue for rapid qualification of microstructure evolution. To achieve the aforementioned objective, twenty-one Inconel 718 samples were designed and built with a variety of processing parameters ranging from a power of 200 W to 370 W and a scan speed of 670 mm/s to 1250 mm/s. These parts were characterized and the meltpool depth was measured through optical microscopy. A combination of part-level thermal simulations from a spectral graph theory method and inprocess sensor data from infrared thermal camera and a meltpool imaging pyrometer were used as inputs to simple machine learning models to predict the meltpool depth. The meltpool depth was correctly predicted with an accuracy of F-Score 85.9%. This exploratory work provided an avenue for rapid prediction of microstructure evolution in metal additive manufacturing.

Download or read book Fundamentals of Laser Powder Bed Fusion of Metals written by Igor Yadroitsev and published by Elsevier. This book was released on 2021-05-23 with total page 678 pages. Available in PDF, EPUB and Kindle. Book excerpt: Laser powder bed fusion of metals is a technology that makes use of a laser beam to selectively melt metal powder layer-by-layer in order to fabricate complex geometries in high performance materials. The technology is currently transforming aerospace and biomedical manufacturing and its adoption is widening into other industries as well, including automotive, energy, and traditional manufacturing. With an increase in design freedom brought to bear by additive manufacturing, new opportunities are emerging for designs not possible previously and in material systems that now provide sufficient performance to be qualified in end-use mission-critical applications. After decades of research and development, laser powder bed fusion is now enabling a new era of digitally driven manufacturing. Fundamentals of Laser Powder Bed Fusion of Metals will provide the fundamental principles in a broad range of topics relating to metal laser powder bed fusion. The target audience includes new users, focusing on graduate and undergraduate students; however, this book can also serve as a reference for experienced users as well, including senior researchers and engineers in industry. The current best practices are discussed in detail, as well as the limitations, challenges, and potential research and commercial opportunities moving forward. - Presents laser powder bed fusion fundamentals, as well as their inherent challenges - Provides an up-to-date summary of this advancing technology and its potential - Provides a comprehensive textbook for universities, as well as a reference for industry - Acts as quick-reference guide

Download or read book Laser Powder Bed Fusion of Additive Manufacturing Technology written by Di Wang and published by Springer Nature. This book was released on 2023-10-06 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book systematically introduces the powder bed laser melting technology and its application and summarizes the author's team's experience in scientific research, engineering development, and data accumulation in recent 15 years. It includes in-depth theoretical analysis and a lot of engineering experience in equipment debugging, process development, and material testing. The book takes the powder bed laser melting technology as the object and divides the content into 15 chapters. It is used as technical learning materials for researchers and engineering development personnel engaged in metal 3D printing.

Download or read book Data driven Control of Laser Powder Bed Fusion written by Aleksandr Shkoruta and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Predictive Iterative Learning Control with Data driven Model for Near optimal Laser Power in Selective Laser Sintering written by Alexander J. Nettekoven and published by . This book was released on 2018 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building high-quality parts is still a key challenge for Selective Laser Sintering machines today due to a lack of sufficient process control. In order to improve process control, a Predictive Iterative Learning Control (PILC) controller is introduced that minimizes the deviation of the post-sintering temperature profile of a newly scanned part from a desired temperature. The controller achieves this by finding a near-optimal laser power profile and applying it to the plant in a feedforward manner. The PILC controller leverages machine learning models that capture the process’ temperature dynamics based on simulated data while still guaranteeing low computational cost. The controller’s performance is evaluated in regards to the control objective with heat transfer simulations by comparing the PILC-controlled laser power profiles to constant laser power profiles

Download or read book Big Data Analytics and Knowledge Discovery written by Robert Wrembel and published by Springer Nature. This book was released on with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Advancement of Selective Laser Melting by Laser Beam Shaping written by Tim Marten Wischeropp and published by Springer Nature. This book was released on 2021-11-30 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: Selective Laser Melting (SLM), also referred to as Laser Powder Bed Fusion (L-PBF), offers significant advantages for the manufacturing of complex, high-quality parts. However, its market share is still small compared to conventional manufacturing technologies. Major drawbacks hindering an industrial ramp-up are low productivity, high part costs and issues with quality and reproducibility. Comprehensive research has been done to overcome these challenges, but little attention has been paid to addressing them by optimizing the laser beam profile. Therefore, the author examines the effect of the laser beam profile on the productivity and process stability through both numerical and experimental investigations. The results show clear advantages an optimized laser beam profile offers.

Download or read book Multivariable Process Control written by Pradeep B. Deshpande and published by Isa. This book was released on 1989-01-01 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Proceedings of the 7th International Conference on Electrical Control and Computer Engineering Volume 1 written by Zainah Md. Zain and published by Springer Nature. This book was released on with total page 637 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Laser based Technologies for Sustainable Manufacturing written by Avinash Kumar and published by CRC Press. This book was released on 2023-07-26 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides scientific and technological insights on novel techniques of design and manufacturing using laser technologies. It showcases applications of laser micromachining in the biomedical industry, laser-based manufacturing processes in aerospace engineering, and high-precision laser-cutting in the home appliance sector. Features: Each chapter discusses a specific engineering problem and showcases its numerical, and experimental solution Provides scientific and technological insights on novel routes of design and manufacturing using laser technologies Synergizes exploration related to the various properties and functionalities through extensive theoretical and numerical modeling Highlights current issues, developments, and constraints in additive manufacturing Discusses applications of laser cutting machines in the manufacturing industry and laser micromachining for the biomedical industry The text discusses optical, and laser-based green manufacturing technologies and their application in diverse engineering fields including mechanical, electrical, biomedical, and computer. It further covers sustainability issues in laser-based manufacturing technologies and the development of laser-based ultra-precision manufacturing techniques. The text also discusses the use of artificial intelligence and machine learning in laser-based manufacturing techniques. It will serve as an ideal reference text for senior undergraduate, graduate students, and researchers in fields including mechanical engineering, aerospace engineering, manufacturing engineering, and production engineering.

Download or read book Springer Handbook of Additive Manufacturing written by Eujin Pei and published by Springer Nature. This book was released on 2023-11-25 with total page 994 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Handbook is the ultimate definitive guide that covers key fundamentals and advanced applications for Additive Manufacturing. The Handbook has been structured into seven sections, comprising of a thorough Introduction to Additive Manufacturing; Design and Data; Processes; Materials; Post-processing, Testing and Inspection; Education and Training; and Applications and Case Study Examples. The general principles and functional relationships are described in each chapter and supplemented with industry use cases. The aim of this book is to help designers, engineers and manufacturers understand the state-of-the-art developments in the field of Additive Manufacturing. Although this book is primarily aimed at students and educators, it will appeal to researchers and industrial professionals working with technology users, machine or component manufacturers to help them make better decisions in the implementation of Additive Manufacturing and its applications.

Download or read book Laser Path Optimization Strategies for Laser Powder Bed Fusion written by Gradey Wang and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Metal additive manufacturing promises to enable the production of complex geometries with potentially exotic material properties. One leading paradigm of metal additive manufacturing is laser powder bed fusion, where a laser shines on a powder bed to selectively fuse powder together via heating, layer by layer, to build up a part. The path of the laser strongly affects the temperature field history of the part, which in turn determines the part's microstructure, as well as the presence of defects like porosity, warping, and residual stresses/strains. With the goal of elucidating how good or optimal laser paths can be identified, this thesis presents three main contributions. After an introduction and review of existing laser path optimization strategies in Chapter 1, Chapter 2 demonstrates the viability of modeling the optimal control problem as a board game and applying deep reinforcement learning a la AlphaGo Zero (albeit with major modifications) as an optimization technique. Then in Chapter 3, we show that by making the assumption that the effect of laser heating is finite in time, our optimal control problem can be modeled as an Traveling Salesperson Problem with History, which we prove can be transformed into equivalent Equality-Generalized Traveling Salesperson Problems and Traveling Salesperson Problems. Chapter 4 demonstrates how the structure of the Traveling Salesperson Problem with History can be used to develop a dynamic programming algorithm for solving and how it affects different solvers' performances. Finally, this thesis concludes with reflections on the potential and limitations of our work to improve laser powder bed fusion beyond computational models.

Download or read book Additive Manufacturing Technologies written by Ian Gibson and published by Springer. This book was released on 2014-11-26 with total page 509 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers in detail the various aspects of joining materials to form parts. A conceptual overview of rapid prototyping and layered manufacturing is given, beginning with the fundamentals so that readers can get up to speed quickly. Unusual and emerging applications such as micro-scale manufacturing, medical applications, aerospace, and rapid manufacturing are also discussed. This book provides a comprehensive overview of rapid prototyping technologies as well as support technologies such as software systems, vacuum casting, investment casting, plating, infiltration and other systems. This book also: Reflects recent developments and trends and adheres to the ASTM, SI, and other standards Includes chapters on automotive technology, aerospace technology and low-cost AM technologies Provides a broad range of technical questions to ensure comprehensive understanding of the concepts covered

Download or read book Engineering of Additive Manufacturing Features for Data Driven Solutions written by Mutahar Safdar and published by Springer Nature. This book was released on 2023-06-01 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Optical Measurement of Surface Topography written by Richard Leach and published by Springer Science & Business Media. This book was released on 2011-03-31 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: The measurement and characterisation of surface topography is crucial to modern manufacturing industry. The control of areal surface structure allows a manufacturer to radically alter the functionality of a part. Examples include structuring to effect fluidics, optics, tribology, aerodynamics and biology. To control such manufacturing methods requires measurement strategies. There is now a large range of new optical techniques on the market, or being developed in academia, that can measure areal surface topography. Each method has its strong points and limitations. The book starts with introductory chapters on optical instruments, their common language, generic features and limitations, and their calibration. Each type of modern optical instrument is described (in a common format) by an expert in the field. The book is intended for both industrial and academic scientists and engineers, and will be useful for undergraduate and postgraduate studies.