Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing

Despite continuous technological enhancements of metal Additive Manufacturing (AM) systems, the lack of process repeatability and stability still represents a barrier for the industrial breakthrough. The most relevant metal AM applications currently involve industrial sectors (e.g., aerospace and bio-medical) where defects avoidance is fundamental. Because of this, there is the need to develop novel in-situ monitoring tools able to keep under control the stability of the process on a layer-by-layer basis, and to detect the onset of defects as soon as possible. On the one hand, AM systems must be equipped with in-situ sensing devices able to measure relevant quantities during the process, a.k.a. process signatures. On the other hand, in-process data analytics and statistical monitoring techniques are required to detect and localize the defects in an automated way. This paper reviews the literature and the commercial tools for insitu monitoring of Powder Bed Fusion (PBF) processes. It explores the different categories of defects and their main causes, the most relevant process signatures and the in-situ sensing approaches proposed so far. Particular attention is devoted to the development of automated defect detection rules and the study of process control strategies, which represent two critical fields for the development of future smart PBF systems.

https://iopscience.iop.org/article/10.1088/1361-6501/aa5c4f/pdf

Process defects and in situ monitoring methods in metal powder bed fusion: a review

Laser based additive manufacturing in industry and academia

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.

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

Despite the rapid adoption of laser powder bed fusion (LPBF) Additive Manufacturing by industry, current processes remain largely open-loop, with limited real-time monitoring capabilities. While some machines offer powder bed visualization during builds, they lack automated analysis capability. This work presents an approach for in-situ monitoring and analysis of powder bed images with the potential to become a component of a real-time control system in an LPBF machine. Specifically, a computer vision algorithm is used to automatically detect and classify anomalies that occur during the powder spreading stage of the process. Anomaly detection and classification are implemented using an unsupervised machine learning algorithm, operating on a moderately-sized training database of image patches. The performance of the final algorithm is evaluated, and its usefulness as a standalone software package is demonstrated with several case studies.

Anomaly Detection and Classification in a Laser Powder Bed Additive Manufacturing Process using a Trained Computer Vision Algorithm

Laser Beam Melting (LBM) allows the fabrication of three-dimensional parts from metallic powder with almost unlimited geometrical complexity and very good mechanical properties. LBM works iteratively: a thin powder layer is deposited onto the build platform which is then melted by a laser according to the desired part geometry. Today, the potential of LBM in application areas such as aerospace or medicine has not yet been exploited due to the lack of process stability and quality management. For that reason, we present a high resolution imaging system for inspection of LBM systems which can be easily integrated into existing machines. A container file stores calibration images and all layer images of one build process (powder and melt result) with corresponding metadata (acquisition and process parameters) for documentation and further analysis. We evaluate the resolving power of our imaging system and show that it is able to inspect the process result on a microscopic scale. Sample images of a part built with varied process parameters are provided, which show that our system can detect topological flaws and is able to inspect the surface quality of built layers. The results can be used for flaw detection and parameter optimization, for example in material qualification.

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High resolution imaging for inspection of Laser Beam Melting systems

Laser beam melting (LBM) processes enable layer-based production of geometrically complex metallic parts with very good mechanical properties for Rapid Manufacturing. Collisions between powder coating mechanism and elevated part regions pose a major risk to process stability, which is crucial for industrial application. Minimizing elevated region area usually involves parameter tuning in a trial-and-error approach, as the process outcome is the only measure of stability. One published approach to quantifying elevated region area utilizes an imaging system, which acquires layer images of the powder bed after powder deposition and detects elevated regions using image analysis. We extend the image-based analysis to each part region, create quantitative visualizations of elevated region area for quick assessment/comparison and compute a figure of merit. In experimental build jobs with overhanging structures and different support junction parameters we gain insight into problematic part regions, which can be used as feedback in job design. The presented method helps to improve LBM process stability, which is strongly linked to process efficiency. Introduction In laser beam melting processes, parts are built in a powder bed by selectively melting the current powder layer according to the part geometry. After lowering the build platform a recoater blade or roller moves powder from the powder container onto the build platform (Figure 1a) before the laser melts the next layer. All steps are repeated to build the entire part. The possibility to produce complex and individual parts in a tool-less manufacturing process on the basis of CAD data was identified as the main driver of innovation in [1]. For industrial applications laser beam melting (LBM) is of special interest, due to the possibility to produce complex metal components with suitable mechanical properties. First LBM production lines are already established in the sector of medical technologies [2] and aerospace [3]. A problematic effect during LBM build jobs is the buildup of elevations, which stand out from the part layer (Figure 1b). As the powder layer is very thin (20 to 100 μm) these elevations are not covered by metal powder and may collide with the recoater blade causing

/pdf/elevated-region-area-measurement-for-quantitative-analysis-2qlkcjljk6.pdf

Elevated Region Area Measurement for Quantitative Analysis of Laser Beam Melting Process Stability

Laser Beam Melting (LBM) is a promising Additive Manufacturing technology that allows the layer-based production of complex metallic components suitable for industrial applications. Widespread application of LBM is hindered by a lack of quality management and process control. Elevated regions in produced layers pose a major risk to process stability as collisions between the powder coating mechanism and the part may occur, which cause damages to either one or even both. We train a classifier-based detector for elevated regions in laser exposure result images. For this purpose we acquire two high resolution layer images: one after laser exposure and another one after powder deposition for the next layer. Ground truth labels for critical regions are obtained from analysis of the latter, where elevated regions are not covered by powder. We compute dense descriptors (HOG, DAISY, LBP) on the surface image after laser exposure and compare their predictive power. The top five descriptor configurations are used to optimize parameters of Random Forest, Support Vector Machine and Stochastic Gradient Descent (SGD) classifiers. We validate the detectors with optimized parameters using cross-validation on 281 images from three build jobs. Using a DAISY descriptor with a SGD classifier we achieve a F1-score of 0.670. The presented method enables detection of elevated regions before powder coating is performed and can be extended to other surface inspection tasks in LBM layer images. Detection results can be used to assess LBM process parameters with respect to process stability during process design and for quality management in production.

/pdf/detection-of-elevated-regions-in-surface-images-from-laser-3k8bh02zxm.pdf

Detection of elevated regions in surface images from laser beam melting processes

Laser Beam Melting (LBM) is an additive manufacturing process, which enables the layer-based production of complex parts from metal powder, ie “3D printing” with metal In previous publications, we presented a high-resolution imaging system for inspection of LBM processes, which uses a high-resolution camera to acquire images of each powder layer and laser exposure result The external camera position necessitates perspective correction, which is based on calibration markers which are “drawn” onto the powder by the LBM system's laser in the first layer As movements of the powder deposition mechanism cause vibrations, the orientation of the camera may be changed, which would invalidate the calibration results and lead to imprecise measurements or segmentations To evaluate the effect of these disturbances, we placed calibrations markers in multiple layers and determined the position offset using template matching We analyze the relative marker drift in three LBM processes and determine the spatial acquisition error The maximum distance is 491 pixels (1561 μm on the part), while most detected markers deviate by less than 15 pixels (46 μm) Compared to the pixel size of 20 μm to 32μm, these deviations are significant and require a repeated calibration in higher layers for valid high-resolution image-based measurements

/pdf/robustness-analysis-of-imaging-system-for-inspection-of-27brhbne16.pdf

Robustness analysis of imaging system for inspection of laser beam melting systems

Laser beam melting (LBM) enables production of three-dimensional parts from metallic powder with very high geometrical complexity and very good mechanical properties. In LBM, a thin layer of metallic powder is deposited onto the build platform and melted by a laser according to the desired part geometry. Until today, the potential of LBM for critical applications such as medical devices and aerospace has not been exploited due to the lack of build stability and quality management. We present an image analysis method, which segments part contours in high-resolution images of LBM-produced layers. Based on the reference contour from 2D slices of the 3D part model and edge-detection results, a graph model is built and segmented using Graph Cuts (min-cut max-flow algorithm). Our method is evaluated on 124 part contours from 5 build jobs with different part geometries. Iterative GrabCut segmentation on nonlinearly smoothed images achieves the best results with a median Jaccard distance of 0.035 (32 % improvement over the reference geometry masks) and a mean contour distance below 2.4 px (36.4 % improvement).

Joschka zur Jacobsmuhlen

Papers

High resolution imaging for inspection of Laser Beam Melting systems

Elevated Region Area Measurement for Quantitative Analysis of Laser Beam Melting Process Stability

Detection of elevated regions in surface images from laser beam melting processes

Robustness analysis of imaging system for inspection of laser beam melting systems

In situ measurement of part geometries in layer images from laser beam melting processes