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Journal ArticleDOI: 10.1016/J.ISATRA.2021.03.001

Metal-based additive manufacturing condition monitoring methods: From measurement to control

05 Mar 2021-Isa Transactions (Elsevier)-
Abstract: Compared with other additive manufacturing processes, the metal-based additive manufacturing (MAM) can build higher precision and higher density parts, and have unique advantages in the applications to automotive, medical, and aerospace industries. However, the quality defects of builds, such as dimensional accuracy, layer morphology, mechanical and metallurgical defects, have been hindering the wide applications of MAM technologies. These decrease the repeatability and consistency of build quality. In order to overcome these shortcomings and to produce high-quality parts, it is very important to carry out online monitoring and process control in the building process. A process monitoring system is demanded which can automatically optimize the process parameters to eliminate incipient defects, improve the process stability and the final build quality. In this paper, the current representative studies are selected from the literature, and the research progress of MAM process monitoring and control are surveyed. Taking the key components of the MAM monitoring system as the mainstream, this study investigates the MAM monitoring system, measurement and signal acquisition, signal and image processing, as well as machine learning methods for the process monitoring and quality classification. The advantages and disadvantages of their algorithmic implementations and applications are discussed and summarized. Finally, the prospects of MAM process monitoring researches are advised.

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Topics: Condition monitoring (54%), Process control (53%)

5 results found

Open accessPosted Content
Abstract: Dictionary methods for system identification typically rely on one set of measurements to learn governing dynamics of a system. In this paper, we investigate how fusion of output measurements with state measurements affects the dictionary selection process in Koopman operator learning problems. While prior methods use dynamical conjugacy to show a direct link between Koopman eigenfunctions in two distinct data spaces (measurement channels), we explore the specific case where output measurements are nonlinear, non-invertible functions of the system state. This setup reflects the measurement constraints of many classes of physical systems, e.g., biological measurement data, where one type of measurement does not directly transform to another. We propose output constrained Koopman operators (OC-KOs) as a new framework to fuse two measurement sets. We show that OC-KOs are effective for sensor fusion by proving that when learning a Koopman operator, output measurement functions serve to constrain the space of potential Koopman observables and their eigenfunctions. Further, low-dimensional output measurements can be embedded to inform selection of Koopman dictionary functions for high-dimensional models. We propose two algorithms to identify OC-KO representations directly from data: a direct optimization method that uses state and output data simultaneously and a sequential optimization method. We prove a theorem to show that the solution spaces of the two optimization problems are equivalent. We illustrate these findings with a theoretical example and two numerical simulations.

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Topics: Operator (computer programming) (52%), Sensor fusion (51%), Optimization problem (51%) ... read more

2 Citations

Open accessJournal ArticleDOI: 10.1016/J.ADDMA.2021.102454
Abstract: Additive manufacturing is a technology transforming traditional production timelines. Specifically, metal additive manufacturing (MAM) has been increasingly adopted by a variety of industries, not only to prototype, but also to fulfill full production scale applications with much lower lead times. Like any maturing manufacturing technology, developments in verifying and validating processes are necessary to support continuous growth. Due to the complex nature of MAM, part quality and repeatability remain integral challenges that inhibit further adoption of MAM for critical component production. In this study, we present data taken from a developing in-process monitoring system designed to measure and detect powder bed defects (PBDs) in powder bed fusion MAM systems using surface height maps created with structured light illumination. We showcase the feasibility of the monitoring technique for in-process implementation by detecting streak PBDs with varying severities (height, width) created in a lab environment. We present results of powder bed measurements for varying experimental parameters of the structured light system such as illumination angle, illumination pattern, and number of illuminations. We also present an expression used to determine experimental height noise based on input parameters for PBD detection based on the instrument transfer function of the structured light monitoring system for arbitrary pixel intensity noise contributions. With the results of PBD detection across across multiple experimental measurement parameters, we provide a best practices approach to in-process implementation of the monitoring system in powder bed fusion manufacturing.

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Open accessJournal ArticleDOI: 10.1088/1361-6501/AC2DBD
Dimitris K. Iakovidis1, Melanie Ooi2, Ye Chow Kuang2, Serge Demidenko2  +34 moreInstitutions (20)
Abstract: Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.

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Journal ArticleDOI: 10.1016/J.JMAPRO.2021.09.033
Yiming Wang1, Xingwang Xu1, Zhuang Zhao1, Wenxiang Deng1  +4 moreInstitutions (1)
Abstract: Wire arc additive manufacturing (WAAM) has been used extensively in metal manufacturing and other fields because of its low cost and high efficiency. In the manufacturing process, WAAM technology is often unable to be further applied due to issues such as component dimensional accuracy, layered morphology, and metallurgical defects. To overcome these problems, online monitoring and process control of the welding are necessary. Based on the monitoring of weld width and reinforcement, a regression network for extracting the global information of molten pool is proposed, and an active disturbance rejection control (ADRC) is designed to adjust the welding current. To the best of our knowledge, this is the first time to realize the coordinated monitoring and control of the width and reinforcement of the deposited layer in the WAAM process. Experiments show that the method can obtain satisfactory molten pool width and reinforcement control accuracy, which provides a feasible way for monitoring and control the weld shape in the WAAM welding process.

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Topics: Welding (54%), Process control (52%)

168 results found

Journal ArticleDOI: 10.1023/B:VISI.0000029664.99615.94
David G. Lowe1Institutions (1)
Abstract: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.

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Topics: 3D single-object recognition (64%), Haar-like features (63%), Feature (computer vision) (58%) ... read more

42,225 Citations

Open accessProceedings ArticleDOI: 10.1109/CVPR.2005.177
Navneet Dalal1, Bill Triggs1Institutions (1)
20 Jun 2005-
Abstract: We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.

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Topics: Histogram of oriented gradients (62%), Local binary patterns (57%), GLOH (56%) ... read more

28,803 Citations

Open accessBook
18 Nov 2016-
Abstract: Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

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Topics: Feature learning (61%), Deep learning (59%), Approximate inference (51%) ... read more

26,972 Citations

Open accessBook
Christopher M. Bishop1Institutions (1)
17 Aug 2006-
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

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Topics: Kernel method (60%), Kernel (statistics) (60%), Graphical model (58%) ... read more

22,762 Citations

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