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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

AbstractProcess 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.

Topics: Support vector machine (52%)

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Citations
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Journal ArticleDOI
Abstract: Metal additive manufacturing (AM) has garnered tremendous research and industrial interest in recent years; in the field, powder bed fusion (PBF) processing is the most common technique, with selective laser melting (SLM) dominating the landscape followed by electron beam melting (EBM). Through continued process improvements, these methods are now often capable of producing high strength parts with static strengths exceeding their conventionally manufactured counterparts. However, PBF processing also results in large and anisotropic residual stresses (RS) that can severely affect fatigue properties and result in geometric distortion. The dependence of RS formation on processing variables, material properties and part geometry has made it difficult to predict efficiently and has hindered widespread acceptance of AM techniques. Substantial investigations have been conducted with regards to RS in PBF processing, which have illuminated a number of important relationships, yet a review encompassing this information has not been available. In this review, we survey and assemble the knowledge existing in the literature regarding RS in PBF processes. A discussion of background mechanics for RS development in AM is provided along with methods of measurement, highlighting the anisotropic nature of the stress fields. We then review modeling efforts and in-process experimental measurements made to advance process understanding, followed by a thorough analysis and summary of the known relationships of both material properties and processing variables to resulting RS. The current state of knowledge and future research needs for the field are discussed.

153 citations


Journal ArticleDOI
TL;DR: It is shown that experiment- and simulation-based data mining in combination with machine leaning tools provide exceptional opportunities to enable highly reliant identification of fundamental interrelations within materials for characterization and optimization in a scale-bridging manner.
Abstract: Machine learning tools represent key enablers for empowering material scientists and engineers to accelerate the development of novel materials, processes and techniques. One of the aims of using such approaches in the field of materials science is to achieve high-throughput identification and quantification of essential features along the process-structure-property-performance chain. In this contribution, machine learning and statistical learning approaches are reviewed in terms of their successful application to specific problems in the field of continuum materials mechanics. They are categorized with respect to their type of task designated to be either descriptive, predictive or prescriptive; thus to ultimately achieve identification, prediction or even optimization of essential characteristics. The respective choice of the most appropriate machine learning approach highly depends on the specific use-case, type of material, kind of data involved, spatial and temporal scales, formats, and desired knowledge gain as well as affordable computational costs. Different examples are reviewed involving case-by-case dependent application of different types of artificial neural networks and other data-driven approaches such as support vector machines, decision trees and random forests as well as Bayesian learning, and model order reduction procedures such as principal component analysis, among others. These techniques are applied to accelerate the identification of material parameters or salient features for materials characterization, to support rapid design and optimization of novel materials or manufacturing methods, to improve and correct complex measurement devices, or to better understand and predict fatigue behavior, among other examples. Besides experimentally obtained datasets, numerous studies draw required information from simulation-based data mining. Altogether, it is shown that experiment- and simulation-based data mining in combination with machine leaning tools provide exceptional opportunities to enable highly reliant identification of fundamental interrelations within materials for characterization and optimization in a scale-bridging manner. Potentials of further utilizing applied machine learning in materials science and empowering significant acceleration of knowledge output are pointed out.

106 citations


Journal ArticleDOI
TL;DR: The infrastructure under development for specification standards in AM is presented, and the research on geometrical dimensioning and tolerancing for AM is reviewed, and post-process metrology is covered, including the measurement of surface form, texture and internal features.
Abstract: The needs, requirements, and on-going and future research issues in geometrical metrology for metal additive manufacturing are addressed. The infrastructure under development for specification standards in AM is presented, and the research on geometrical dimensioning and tolerancing for AM is reviewed. Post-process metrology is covered, including the measurement of surface form, texture and internal features. In-process requirements and developments in AM are discussed along with the materials metrology that is pertinent to geometrical measurement. Issues of traceability, including benchmarking artefacts, are presented. The information in the review sections is summarized in a synthesis of current requirements and future research topics.

102 citations


Journal ArticleDOI
TL;DR: In the authors’ perspective, in situ monitoring of AM processes will significantly benefit from the object detection ability of ML, and data sharing of AM would enable faster adoption of ML in AM.
Abstract: Additive manufacturing (AM) or 3D printing is growing rapidly in the manufacturing industry and has gained a lot of attention from various fields owing to its ability to fabricate parts with complex features. The reliability of the 3D printed parts has been the focus of the researchers to realize AM as an end-part production tool. Machine learning (ML) has been applied in various aspects of AM to improve the whole design and manufacturing workflow especially in the era of industry 4.0. In this review article, various types of ML techniques are first introduced. It is then followed by the discussion on their use in various aspects of AM such as design for 3D printing, material tuning, process optimization, in situ monitoring, cloud service, and cybersecurity. Potential applications in the biomedical, tissue engineering and building and construction will be highlighted. The challenges faced by ML in AM such as computational cost, standards for qualification and data acquisition techniques will also be discussed. In the authors’ perspective, in situ monitoring of AM processes will significantly benefit from the object detection ability of ML. As a large data set is crucial for ML, data sharing of AM would enable faster adoption of ML in AM. Standards for the shared data are needed to facilitate easy sharing of data. The use of ML in AM will become more mature and widely adopted as better data acquisition techniques and more powerful computer chips for ML are developed.

66 citations


Additional excerpts

  • ...The feature vector is then fed to SVM image claissification algorithm to learn the defects such as under-melting, keyholing, and balling (Scime and Beuth 2019)....

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  • ...For instance, a hybrid ML algorithm was devised which uses hierarchical clustering to classify AM design features and support vector machine (SVM) to enhance the hierarchical clustering result in pursuit of finding the recommended AM design features (Yao et al....

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  • ...2019), and support vector machine (SVM) (Gobert et al....

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  • ...%) and the combination of SVM and principle component analysis (PCA) (90.1%)(Zhang et al....

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  • ...%) was found to have higher classification accuracy as compared to SVM (89.6...

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Journal ArticleDOI
Abstract: Additive manufacturing (AM) has emerged as a disruptive digital manufacturing technology. However, its broad adoption in industry is still hindered by high entry barriers of design for additive manufacturing (DfAM), limited materials library, various processing defects, and inconsistent product quality. In recent years, machine learning (ML) has gained increasing attention in AM due to its unprecedented performance in data tasks such as classification, regression and clustering. This article provides a comprehensive review on the state-of-the-art of ML applications in a variety of AM domains. In the DfAM, ML can be leveraged to output new high-performance metamaterials and optimized topological designs. In AM processing, contemporary ML algorithms can help to optimize process parameters, and conduct examination of powder spreading and in-process defect monitoring. On the production of AM, ML is able to assist practitioners in pre-manufacturing planning, and product quality assessment and control. Moreover, there has been an increasing concern about data security in AM as data breaches could occur with the aid of ML techniques. Lastly, it concludes with a section summarizing the main findings from the literature and providing perspectives on some selected interesting applications of ML in research and development of AM.

64 citations


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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.

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Journal ArticleDOI
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.

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