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Journal ArticleDOI

Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging.

01 May 2018-Additive manufacturing (Elsevier BV)-Vol. 21, pp 517-528
TL;DR: In this article, an in- situ defect detection strategy for powder bed fusion (PBF) AM using supervised machine learning is described, where multiple images were collected at each build layer using a high resolution digital single-lens reflex (DSLR) camera.
Abstract: Process monitoring in additive manufacturing (AM) is a crucial component in the mission of broadening AM industrialization. However, conventional part evaluation and qualification techniques, such as computed tomography (CT), can only be utilized after the build is complete, and thus eliminate any potential to correct defects during the build process. In contrast to post-build CT, in situ defect detection based on in situ sensing, such as layerwise visual inspection, enables the potential for in-process re-melting and correction of detected defects and thus facilitates in-process part qualification. This paper describes the development and implementation of such an in situ defect detection strategy for powder bed fusion (PBF) AM using supervised machine learning. During the build process, multiple images were collected at each build layer using a high resolution digital single-lens reflex (DSLR) camera. For each neighborhood in the resulting layerwise image stack, multi-dimensional visual features were extracted and evaluated using binary classification techniques, i.e. a linear support vector machine (SVM). Through binary classification, neighborhoods are then categorized as either a flaw, i.e. an undesirable interruption in the typical structure of the material, or a nominal build condition. Ground truth labels, i.e. the true location of flaws and nominal build areas, which are needed to train the binary classifiers, were obtained from post-build high-resolution 3D CT scan data. In CT scans, discontinuities, e.g. incomplete fusion, porosity, cracks, or inclusions, were identified using automated analysis tools or manual inspection. The xyz locations of the CT data were transferred into the layerwise image domain using an affine transformation, which was estimated using reference points embedded in the part. After the classifier had been properly trained, in situ defect detection accuracies greater than 80% were demonstrated during cross-validation experiments.
Citations
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Journal ArticleDOI
TL;DR: In this article, the authors investigated the utility of nonlinear resonance ultrasonic spectroscopy (NRUS) for predicting the fatigue life of cylindrical Ti-6Al-4V samples manufactured using powder bed fusion on the same build.
Abstract: Additive manufacturing (AM) is becoming increasingly popular due to its ability to build parts with customer-designed complex geometries. However, the lack of established quality control/assurance practices for the built parts poses a challenge to a wider adoption of additive manufacturing by some industries. In this study, we investigate the utility of nonlinear resonance ultrasonic spectroscopy (NRUS) for predicting the fatigue life of cylindrical Ti–6Al–4V samples manufactured using powder bed fusion on the same build. We use NRUS to measure several linear and nonlinear parameters for the samples before and after hot isostatic pressing (HIPing). The samples are then fatigue tested. We report the correlation between NRUS parameters and fatigue life. Our findings suggest that resonance frequency, quality factor, and the hysteretic nonlinearity of the samples are correlated to their fatigue life. These correlations are promising because the measurements are obtained from distinct samples as opposed to collecting data on the same sample undergoing increasing fatigue cycles. This study suggests that NRUS has the potential to be used as a nondestructive evaluation (NDE) tool for AM part quality control.

7 citations

Journal ArticleDOI
TL;DR: In this paper , a self-organizing map (SOM) was used to detect porosity induced during laser powder bed fusion (LPBF) due to the lack of fusion (LoF) phenomenon.

7 citations

Proceedings ArticleDOI
Han Wang1, Joseph Polden1, Josiah Jirgens1, Ziping Yu1, Zengxi Pan1 
01 Jul 2019
TL;DR: The proposed automatic rebar counting system makes use of several image processing techniques including Canny edge detection, Circle Hough Transform calculation and a machine learning system to accurately identify the number of individual rebar in a given bundle under various lighting conditions.
Abstract: In this paper, an automatic rebar counting system based on image processing and machine learning techniques is proposed. The system makes use of several image processing techniques including Canny edge detection, Circle Hough Transform (CHT) calculation and a machine learning system to accurately identify the number of individual rebar in a given bundle under various lighting conditions. This work includes a study of a number of different machine learning algorithms including decision tree, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), traditional neural network and Convolutional Neural Network (CNN). The proposed system is able to transfer the original object detection problem into a more easily solvable image classification problem and is hence achieve an overall accuracy of 95.99% in the presence of reasonable lighting conditions.

7 citations

Journal ArticleDOI
TL;DR: An image processing technique that analyzes the amplitude and phase variations of the print head platform arising through induced system manipulations that has the potential to enhance the robustness of cyber-physical systems such as 3D printers is introduced.
Abstract: Three-dimensional printing systems have expanded the access to low cost, rapid methods for attaining physical prototypes or products. However, a cyber attack, system error, or operator error on a 3D-printing system may result in catastrophic situations, ranging from complete product failure, to small types of defects which weaken the structural integrity of the product. Such defects can be introduced early-on via solid models or through G-codes for printer movements at a later stage. Previous works have studied the use of image classifiers to predict defects in real-time and offline. However, a major restriction in the functionality of these methods is the availability of a dataset capturing diverse attacks on printed entities or the printing process. This paper introduces an image processing technique that analyzes the amplitude and phase variations of the print head platform arising through induced system manipulations. The method uses an image sequence of the printing process to perform an offline spatio-temporal video decomposition to amplify changes attributable to a change in system parameters. The authors hypothesize that a change in the amplitude envelope and instantaneous phase response as a result of a change in the end-effector translational instructions to be correlated with an AM system compromise. Two case studies are presented, one verifies the hypothesis with statistical evidence in support of the method while the other studies the effectiveness of a conventional tensile test to identify system compromise. The method has the potential to enhance the robustness of cyber-physical systems such as 3D printers.

7 citations

References
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Journal ArticleDOI
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Abstract: The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data. High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.

37,861 citations

Journal ArticleDOI
01 Jun 2010
TL;DR: A brief overview of clustering is provided, well known clustering methods are summarized, the major challenges and key issues in designing clustering algorithms are discussed, and some of the emerging and useful research directions are pointed out.
Abstract: Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms into a system of ranked taxa: domain, kingdom, phylum, class, etc. Cluster analysis is the formal study of methods and algorithms for grouping, or clustering, objects according to measured or perceived intrinsic characteristics or similarity. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes data clustering (unsupervised learning) from classification or discriminant analysis (supervised learning). The aim of clustering is to find structure in data and is therefore exploratory in nature. Clustering has a long and rich history in a variety of scientific fields. One of the most popular and simple clustering algorithms, K-means, was first published in 1955. In spite of the fact that K-means was proposed over 50 years ago and thousands of clustering algorithms have been published since then, K-means is still widely used. This speaks to the difficulty in designing a general purpose clustering algorithm and the ill-posed problem of clustering. We provide a brief overview of clustering, summarize well known clustering methods, discuss the major challenges and key issues in designing clustering algorithms, and point out some of the emerging and useful research directions, including semi-supervised clustering, ensemble clustering, simultaneous feature selection during data clustering, and large scale data clustering.

6,601 citations

Journal ArticleDOI
TL;DR: The state-of-the-art of additive manufacturing (AM) can be classified into three categories: direct digital manufacturing, free-form fabrication, or 3D printing as discussed by the authors.
Abstract: This paper reviews the state-of-the-art of an important, rapidly emerging, manufacturing technology that is alternatively called additive manufacturing (AM), direct digital manufacturing, free form fabrication, or 3D printing, etc. A broad contextual overview of metallic AM is provided. AM has the potential to revolutionize the global parts manufacturing and logistics landscape. It enables distributed manufacturing and the productions of parts-on-demand while offering the potential to reduce cost, energy consumption, and carbon footprint. This paper explores the material science, processes, and business consideration associated with achieving these performance gains. It is concluded that a paradigm shift is required in order to fully exploit AM potential.

4,055 citations

Journal ArticleDOI
TL;DR: Future directions such as the "print-it-all" paradigm, that have the potential to re-imagine current research and spawn completely new avenues for exploration are pointed out.
Abstract: Additive manufacturing (AM) is poised to bring about a revolution in the way products are designed, manufactured, and distributed to end users. This technology has gained significant academic as well as industry interest due to its ability to create complex geometries with customizable material properties. AM has also inspired the development of the maker movement by democratizing design and manufacturing. Due to the rapid proliferation of a wide variety of technologies associated with AM, there is a lack of a comprehensive set of design principles, manufacturing guidelines, and standardization of best practices. These challenges are compounded by the fact that advancements in multiple technologies (for example materials processing, topology optimization) generate a "positive feedback loop" effect in advancing AM. In order to advance research interest and investment in AM technologies, some fundamental questions and trends about the dependencies existing in these avenues need highlighting. The goal of our review paper is to organize this body of knowledge surrounding AM, and present current barriers, findings, and future trends significantly to the researchers. We also discuss fundamental attributes of AM processes, evolution of the AM industry, and the affordances enabled by the emergence of AM in a variety of areas such as geometry processing, material design, and education. We conclude our paper by pointing out future directions such as the "print-it-all" paradigm, that have the potential to re-imagine current research and spawn completely new avenues for exploration. The fundamental attributes and challenges/barriers of Additive Manufacturing (AM).The evolution of research on AM with a focus on engineering capabilities.The affordances enabled by AM such as geometry, material and tools design.The developments in industry, intellectual property, and education-related aspects.The important future trends of AM technologies.

1,792 citations

Journal ArticleDOI
TL;DR: In this article, a review of additive manufacturing (AM) techniques for producing metal parts are explored, with a focus on the science of metal AM: processing defects, heat transfer, solidification, solid-state precipitation, mechanical properties and post-processing metallurgy.
Abstract: Additive manufacturing (AM), widely known as 3D printing, is a method of manufacturing that forms parts from powder, wire or sheets in a process that proceeds layer by layer. Many techniques (using many different names) have been developed to accomplish this via melting or solid-state joining. In this review, these techniques for producing metal parts are explored, with a focus on the science of metal AM: processing defects, heat transfer, solidification, solid-state precipitation, mechanical properties and post-processing metallurgy. The various metal AM techniques are compared, with analysis of the strengths and limitations of each. Only a few alloys have been developed for commercial production, but recent efforts are presented as a path for the ongoing development of new materials for AM processes.

1,713 citations