Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging.
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.
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TL;DR: A new part authentication framework is proposed by leveraging layer-wise in-situ videos and the multilinear principal component analysis (MPCA) algorithm is used to extract low-dimensional features from the joint distribution of geometric features.
6 citations
TL;DR: In this article , an in-situ FPP method was developed to measure the dynamic topography of powder bed and printed layer during laser powder bed fusion (LPBF) additive manufacturing process.
6 citations
TL;DR: In this article, a computational model based on artificial neural networks was developed to optimize process parameters for DED-processed Ti-6Al-4V alloy, and the model was used to estimate the density and build height (the actual build height realized after fabrication) of the sample as a function of power, scan speed, powder feed rate, and layer thickness.
Abstract: Direct energy deposition (DED) is a highly applicable additive manufacturing (AM) method and, therefore, widely employed in industrial repair-based applications to fabricate defect-free and high degree precision components. To obtain high-quality products by using DED, it is necessary to understand the influence of the process parameters on the product quality. The optimization of such processing parameters provides several advantages such as minimization of the loss of material and time. However, the optimization of the complex relationship between the process parameters-geometry-properties of the fabricated sample is difficult to realize and requires significant experimentation. Herein, a computational model based on artificial neural networks was developed to optimize process parameters for DED-processed Ti-6Al-4V alloy. The model was developed to estimate the density and build height (the actual build height realized after fabrication) of the sample as a function of power, scan speed, powder feed rate, and layer thickness. The optimum model with high-accuracy predictions was employed to construct the process maps for the DED-processed Ti-6Al-4V. The relative importance indices of process parameters on the build height and density were investigated. Further, the effect of power and scan speed on the microstructure of Ti-6Al-4V alloy was discussed. Finally, based on the obtained results, the optimum fabrication conditions for the DED-processed Ti-6Al-4V alloy were determined.
6 citations
TL;DR: In this article, the authors presented an approach for in-situ process monitoring by leveraging the process-inherent recoater movement to obtain highly resolving images of the complete process area with a recoaters-based line camera.
Abstract: The multitude of influences on the Laser Powder Bed Fusion (LPBF) process and local deviations from the intended process conditions can lead to the occurrence of quality deviations. The cyclic nature of the process could enable in-situ process monitoring systems to detect those deviations and to reduce the need for post-process quality control or even prevent defects by utilizing the extracted data for closed-loop or intermittent control. Most existing approaches are focused on the thermal observation of the melt pool or use matrix cameras to view the solidified material from fixed positions. This work presents an approach for in-situ process monitoring by leveraging the process-inherent recoater movement to obtain highly resolving images of the complete process area with a recoater-based line camera. A monitoring system is designed and integrated into a LPBF-machine. The system is calibrated and benchmarked to obtain images with a width of 97.67 mm at a resolution of 5.97 µm/px and a recoater speed of up to 100 mm/s. Details as small as 12.40 µm can be identified according to a USAF1951 test and the focal depth is sufficient to encompass the whole process area (part surface & powder) of a typical LPBF process. Samples with varied conditions are produced and layer-wise images are acquired to evaluate the potential of the system under actual process conditions. Features relating to the brightness and periodicity of the images are defined and extracted from the image data. The relationships between the extracted features on the one hand and the laser power, scanning speed, layer thickness, shielding gas flow and laser defocus on the other hand are investigated and correlations are found. Overall, the findings indicate a high potential of the investigated system for the detection of deviations in the LPBF process and its future application for in-situ quality control.
6 citations
TL;DR: A review and analysis of the state of the art of Nondestructive Testing applied in additive manufacturing is presented in this paper , highlighting the most relevant works and the challenges that each technique should face.
5 citations
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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.
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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
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
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
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