Christian Gobert

Bio: Christian Gobert is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Support vector machine & Discontinuity (geotechnical engineering). The author has an hindex of 4, co-authored 5 publications receiving 168 citations. Previous affiliations of Christian Gobert include Oak Ridge Associated Universities & United States Army Research Laboratory.

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

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.

Porosity segmentation in X-ray computed tomography scans of metal additively manufactured specimens with machine learning

Abstract: X-ray computed tomography (XCT) is widely used in additive manufacturing (AM) to obtain discrete analysis of internal material discontinuities, especially the porosity of AM specimens. XCT uses X-ray penetration to generate 3D digital reconstructions that enable non-destructive evaluations of specimens and their internal structures. The process of segmenting XCT images for porosity analysis can be time consuming, affected by XCT scan quality, and subject to segmentation methods. OTSU thresholding and a convolutional neural network were combined into a machine learning tool to automatically segment porosity from XCT images of metallic AM specimens. Multiple XCT specialists and AM specimens were used to investigate how various segmentation methodologies, used to create ground-truth labels of porosity, impacted machine learning performance. XCT specialists segmenting a control specimen established a benchmark for machine learning performance measured through classification and descriptive statistics. Discrepancies in the machine learning tool segmentations were similar to or better than the discrepancies among the XCT specialist themselves, indicating a high capability for automated porosity segmentation.

Where is the machine looking? Locating discriminative light-scattering features by class-activation mapping

Abstract: We explore a technique called class-activation mapping (CAM) to investigate how a Machine Learning (ML) architecture learns to classify particles based on their light-scattering signals. We release our code, and also find that different regions of the light-scattering signals play different roles in ML classification. These regions depend on the type of particles being classified and on the nature of the data obtained and trained. For instance, the Mueller-matrix elements S 11 * , S 12 * and S 21 * had the greatest classification activation in the diffraction region. Linear polarization elements S 12 * and S 21 * were most accurate in the backscattering region for clusters of spheres and spores, and was most accurate in the diffraction region for other particle classes. The CAM technique was able to highlight light-scattering angles that maximize the potential for discrimination of similar particle classes. Such information is useful for designing detector systems to classify particles where limited space or resources are available, including flow cytometry and satellite remote sensing.

Pattern Recognition and Machine Learning

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.

An overview of residual stresses in metal powder bed fusion

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.

Machine learning in additive manufacturing: State-of-the-art and perspectives

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.

A review on machine learning in 3D printing: applications, potential, and challenges

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.

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics

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.