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Process intermittent measurement for powder-bed based additive manufacturing
Abstract
Process intermittent measurements of parts fabricated by additive manufacturing (AM) can enable both process improvement and characterization of internal part geometries. The planar, layer-upon-layer nature of most AM processes allows two-dimensional geometric measurements with a vision system, because the part's current layer is continually in focus. Proof of this concept has been shown through measurement of parts made using a three-dimensional (3D) printer. Process intermittent measurements were compared to contact and non-contact measurements of the finished parts to characterize deviations in printed layer positions and changes in part dimensions resulting from post-process treatments.
... Among these process signatures, geometric signatures [11][12][13][14][15][16][17][18][19][20][21] are one of the main concerns in in situ monitoring techniques of the PBF AM process. In those early studies, several efforts used a monocular camera to implement 2D geometric measurement and defects detection [11][12][13][14][15][16]. ...
... Among these process signatures, geometric signatures [11][12][13][14][15][16][17][18][19][20][21] are one of the main concerns in in situ monitoring techniques of the PBF AM process. In those early studies, several efforts used a monocular camera to implement 2D geometric measurement and defects detection [11][12][13][14][15][16]. Moreover, some researchers also considered the categorization of the potential error sources [13,14] and the verification of the deposited powder or fusion layers [15]. ...
... However, the stated in situ monitoring methods [11][12][13][14][15][16] mostly worked with 2D images and did not yield quantitative 3D data. Investigations on in situ monitoring of 3D geometric signatures are limited, presumably because the measurement environment is challenging and directly-applicable sensors are not available. ...
Lack of monitoring of the in situ process signatures is one of the challenges that has been restricting the improvement of Powder-Bed-Fusion Additive Manufacturing (PBF AM). Among various process signatures, the monitoring of the geometric signatures is of high importance. This paper presents the use of vision sensing methods as a non-destructive in situ 3D measurement technique to monitor two main categories of geometric signatures: 3D surface topography and 3D contour data of the fusion area. To increase the efficiency and accuracy, an enhanced phase measuring profilometry (EPMP) is proposed to monitor the 3D surface topography of the powder bed and the fusion area reliably and rapidly. A slice model assisted contour detection method is developed to extract the contours of fusion area. The performance of the techniques is demonstrated with some selected measurements. Experimental results indicate that the proposed method can reveal irregularities caused by various defects and inspect the contour accuracy and surface quality. It holds the potential to be a powerful in situ 3D monitoring tool for manufacturing process optimization, close-loop control, and data visualization.
... Therefore, real-time, in-line metrology is commonly reported to be among the main challenges for AM development [9]. Contour verification could be carried out by means of different technologies, like structured light [7], conoscopic holography [10], coordinate measuring machine (CMM) optical probes [11] or ad hoc Charge-Couple Device (CCD) based instrumentation [12,13]. Nevertheless, computer vision based on flatbed scanner images should be considered as a serious candidate, since it can meet high scanning speeds at low prices. ...
... A similar system would thereafter be constructed for the Y coordinate (Figure 10b). Accordingly, the position of every point A with respect to the Cartesian system (x A ,y A ) calculated with Equations (11) and (12). ...
Industrial adoption of additive manufacturing (AM) processes demands improvement in the geometrical accuracy of manufactured parts. One key achievement would be to ensure that manufactured layer contours match the correspondent theoretical profiles, which would require integration of on-machine measurement devices capable of digitizing individual layers. Flatbed scanners should be considered as serious candidates, since they can achieve high scanning speeds at low prices. Nevertheless, image deformation phenomena reduce their suitability as two-dimensional verification devices. In this work, the possibilities of using flatbed scanners for AM contour verification are investigated. Image distortion errors are characterized and discussed and special attention is paid to the plication effect caused by contact imaging sensor (CIS) scanners. To compensate this phenomena, a new local distortion adjustment (LDA) method is proposed and its distortion correction capabilities are evaluated upon actual layer contours manufactured on a fused filament fabrication (FFF) machine. This proposed method is also compared to conventional global distortion adjustment (GDA). Results reveal quasi-systematic deformations of the images which could be minimized by means of distortion correction. Nevertheless, the irregular nature of such a distortion and the superposition of different errors penalize the use of GDA, to the point that it should not be used with CIS scanners. Conclusions indicate that LDA-based correction would enable the use of flatbed scanners in AM for on-machine verification tasks.
... Recently, there have been numerous reports concerning in-situ sensing methods which may be grouped into two major categories: co-axial sensing approach and off-axial sensing approach. In those papers [3][4][5][6][7], they use different sensors (e.g., photodiode sensors, mechanical sensors, and industrial cameras) to get the process signatures (e.g., melt-pool shape and temperature, residual stress, and geometric signatures) during the build. Owing to the layer-wise production paradigm, a part's shape is composed by thousands of slicing contours, and the fusion area contour is of great importance to parts geometrical quality. ...
... Cooke et al. [4]. proposed an edge detection method based on the interpolation of intensities between pixel values to detect the edges within each layer of the part. ...
... Image analysis There are not many publications dealing with analysis of layer images from LBM processes but there are preliminary studies for three-dimensional printing (3DP), i.e., the process of printing liquid binder to a powder bed. Cooke and Moylan [12] published a proof of concept for 2D measurements with a vision system using process-intermittent measurements with image resolutions from 10 to 49 μm/pixel. They installed a camera on top of a 3D printing system and extracted part contour measurements using Laplacian edge detection with cubic-spline interpolation for a resolution of 1/10 px. ...
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).
... Even if the technology were feasible, it remains a major obstacle to utilize the significant amount of image data for real-time feedback control. Such an issue is causing major challenges in additive manufacturing [2,3]. Similar scenarios also appear in many other processes such as high-speed vision servo and discrete manufacturing. ...
A fundamental challenge in digital and sampled-data control arises when the continuous-time plant is subject to fast disturbances that possess significant frequency components beyond Nyquist frequency. Such intrinsic difficulties are more and more encountered in modern manufacturing applications, where the measurement speed of the sensor is physically limited compared to the plant dynamics. The paper analyzes the spectral properties of the closed-loop signals under such scenarios, and uncovers several fundamental limitations in the process.
... IR images allow temperature variations in the powder bed to be measured that can contribute to thermal stress within a part [64]. Visual camera images taken after the completion of laser scanning on a given layer may be used to evaluate errors related to the part geometry, superelevation of the part above the powder bed due to thermal stresses, and support connection errors in a that layer [65,66]. Imaging after a powder recoat process but before laser scanning of a given layer can also provide a means of detecting irregularities in the recoat process [58]. ...
Additive manufacturing and specifically metal selective laser melting (SLM) processes are rapidly being industrialized. In order for this technology to see more widespread use as a production modality, especially in heavily regulated industries such as aerospace and medical device manufacturing, there is a need for robust process monitoring and control capabilities to be developed that reduce process variation and ensure quality. The current state of the art of such process monitoring technology is reviewed in this paper. The SLM process itself presents significant challenges as over 50 different process input variables impact the characteristics of the finished part. Understanding the impact of feed powder characteristics remains a challenge. Though many powder characterization techniques have been developed, there is a need for standardization of methods most relevant to additive manufacturing. In-process sensing technologies have primarily focused on monitoring melt pool signatures, either from a Lagrangian reference frame that follows the focal point of the laser or from a fixed Eulerian reference frame. Correlations between process measurements, process parameter settings, and quality metrics to date have been primarily qualitative. Some simple, first-generation process control strategies have also been demonstrated based on these measures. There remains a need for connecting process measurements to process models to enable robust model-based control.
... A fundamental challenge arises in feedback control if the sampling is not fast enough to capture the major frequency components of d c (t)-or more specifically, when significant disturbances occur beyond Nyquist frequency. Such a scenario, however, is becoming increasingly important in modern control systems, due to, on the one hand the continuous pursuit of higher performance and robustness using slow or limited sensing mechanisms (e.g., vision servo [1], chemical process [2], human-machine interaction, etc); and on the other hand, the significant interest in novel applications such as insitu sensing in advanced additive manufacturing [3], [4] and virtual and mixed reality [5]. In these applications and the like, significant disturbances beyond Nyquist frequency are unattended under conventional servo design. ...
A fundamental challenge in digital and sampled-data control arises when the controlled plant is subjected to fast disturbances with, however, a slow or limited sensor measurement. Such intrinsic difficulties are commonly encountered in many novel applications such as advanced and additive manufacturing, human-machine interaction, etc. This paper introduces a discrete-time regulation scheme for exact sampled-data rejection of disturbances beyond Nyquist frequency. By using a model-based multirate predictor and
a forward-model disturbance observer, we show that the intersample
disturbances can be fully attenuated despite the limitations in sampling and sensing. In addition, sharing the main properties of all-stabilizing control, the proposed control scheme offers several advantages in stability assurance and lucid design intuitions.
... Initial studies have examined particular aspects of the control problem. Cooke and Moylan [5] studied layer registration issues that arose due to oscillations during the placement of each layer such that further control was needed. A vision system was suggested to take measurements after each layer to guarantee an accurate reproduction of the intended geometry. ...
The extension of electrophotographic (EP) printing into the additive manufacturing space has been seen as a natural step for this technology; however, the self-insulating nature of the process has prevented the creation of structures beyond a limited number of layers where surface defects are evident. This paper examines two control strategies for EP-based three-dimensional (EP3D) printing that minimize the surface defects to obtain the accurate reproduction of the intended 3D geometry. The strategies rely not on material deposition control but rather on progressively compensating layer after layer for irregularities forming on the surface. This represents an important step toward the development and future commercialization of EP3D printing.
... As is evident in Fig. 12, the smaller hole will be finished before the larger hole (datum reference for smaller hole) is completed. Therefore, tolerance transfer and process measurement for each layer [75] will be critical for achieving the geometric quality specified by the designer. ...
Additive manufacturing (AM) has increasingly gained attention in the last decade as a versatile manufacturing process for customized products. AM processes can create complex, freeform shapes while also introducing features, such as internal cavities and lattices. These complex geometries are either not feasible or very costly with traditional manufacturing processes. The geometric freedoms associated with AM create new challenges in maintaining and communicating dimensional and geometric accuracy of parts produced. This paper reviews the implications of AM processes on current geometric dimensioning and tolerancing (GD&T) practices, including specification standards, such as ASME Y14.5 and ISO 1101, and discusses challenges and possible solutions that lie ahead. Various issues highlighted in this paper are classified as (a) AM-driven specification issues and (b) specification issues highlighted by the capabilities of AM processes. AM-driven specification issues may include build direction, layer thickness, support structure related specification, and scan/track direction. Specification issues highlighted by the capabilities of AM processes may include region-based tolerances for complex freeform surfaces, tolerancing internal functional features, and tolerancing lattice and infills. We introduce methods to address these potential specification issues. Finally, we summarize potential impacts to upstream and downstream tolerancing steps, including tolerance analysis, tolerance transfer, and tolerance evaluation.
... Cooke and Moylan showed that process intermittent measurements can be viable for both process improvement and characterization of internal part geometries. Process intermittent measurements were compared to contact and non-contact measurements of the finished parts to characterize deviations in printed layer positions and changes in part dimensions resulting from post-process treatments [72]. ...
Additive Manufacturing is increasingly used in the development of new products: from
conceptual design to functional parts and tooling. However, today, variability in part quality due to
inadequate dimensional tolerances, surface roughness, and defects, limits its broader acceptance for highvalue
or mission-critical applications. While process control in general can limit this variability, it is
impeded by a lack of adequate process measurement methods. Process control today is based on heuristics
and experimental data, yielding limited improvement in part quality. The overall goal is to develop the
measurement science1 necessary to make in-process measurement and real-time control possible in
additive manufacturing. Traceable dimensional and thermal metrology methods must be developed for
real-time closed-loop control of additive manufacturing processes. As a precursor, this report presents a
review on the additive manufacturing control schemes, process measurements, and modeling and
simulation methods as it applies to the powder bed fusion process, though results from other processes are
reviewed where applicable. The aim of the review is to identify and summarize the measurement science
needs that are critical to real-time process control. We organize our research findings to identify the
correlations between process parameters, process signatures, and product quality. The intention of this
report is to serve as a background reference and a go-to place for our work to identify the most suitable
measurement methods and corresponding measurands for real-time control.
An increasing number of additive manufacturing (AM) applications leads to rising challenges for the process-accompanying quality assurance. Beside post-processing measurement systems, in situ monitoring systems in particular are currently requested to ensure feedback controlling during AM processes. For data acquisition and subsequent evaluation, a high data quality is of importance. It depends on a high resolution and accuracy of measurement systems, adapted measurement conditions and a reference to the powder bed or component for geometric measurements. Within this scientific study, a new reference system has been implemented into the powder bed to reduce measurement deviations by an abbreviated metrological loop. After data acquisition and image processing layer by layer, the position stability of the reference system has been analysed in relation to the optical measuring system. Based on a contour detection of the reference markers, the evaluation of geometrical process deviations is presented as an essential basis for a closed-loop controlling system. Thermally induced and mechanical drifts within the manufacturing process can be verified by the reference system in the powder bed. As an outlook, two methods are suggested for a process-accompanying referenced detection of the melting pool and resulting contour displacements during additive manufacturing.
Additive manufacturing technologies are further developing from prototype to serial production.
This trend requires rising challenges to the process-accompanying quality assurance.
Optical in situ quality control approaches show great potential to generate accurate measurement data, which are essential for feedback control.
If a reliable referencing concept for the layer-by-layer measured data is guaranteed, contour information can be used during the manufacturing to correct occurring geometrical deviations.
Within this scientific study, two methods of optical, referenced in situ control of lateral displacements of additive manufactured contours are presented.
In the first approach the 2-D contour of the melting pool is analysed in relation to a position-stable reference system implemented in the powder bed.
The second approach uses the translucent contour of deeper layers covered with powder as a reference.
Within the image evaluation several pre-processing steps like calibration, undistortion, rectification, illumination correction and low-pass filtering are essential for reliable and correct geometric measurements.
The following adapted contour detection and position determination of the referenced melting pool contours are based on an extended edge detection algorithm according to Canny (1986).
With the evaluation of further manufacturing layers of already lowered powder bed levels, it is possible to specify the influence of powder application on geometrical displacements separately.
This is done by a comparison of the position of the detected powder-covered melting pool contours with the previously applied melted region.
Consequently a better understanding of lateral contour displacements within the additive manufacturing process is the goal, which is important for a process-accompanying correction of geometrical deviations.
In-situ detection of processing defects is a critical challenge for Laser Powder Bed Fusion Additive Manufacturing. Many of these defects are related to interactions between the recoater blade, which spreads the powder, and the powder bed. This work leverages Deep Learning, specifically a Convolutional Neural Network (CNN), for autonomous detection and classification of many of these spreading anomalies. Importantly, the input layer of the CNN is modified to enable the algorithm to learn both the appearance of the powder bed anomalies as well as key contextual information at multiple size scales. These modifications to the CNN architecture are shown to improve the flexibility and overall classification accuracy of the algorithm while mitigating many human biases. A case study is used to demonstrate the utility of the presented methodology and the overall performance is shown to be superior to that of methodologies previously reported by the authors.
Selective laser sintering (SLS) is an efficient process in additive manufacturing that enables rapid part production from computer-based designs. However, SLS is limited by its notable lack of in situ process monitoring when compared with other manufacturing processes. We report the incorporation of optical coherence tomography (OCT) into an SLS system in detail and demonstrate access to surface and subsurface features. Video frame rate cross-sectional imaging reveals areas of sintering uniformity and areas of excessive heat error with high temporal resolution. We propose a set of image processing techniques for SLS process monitoring with OCT and report the limitations and obstacles for further OCT integration with SLS systems.
Additive manufacturing, unlike subtractive manufacturing, consist in the addition of material by layers, allowing to create objects with complex geometries, which would be complicated to realize using traditional manufacturing process. Even though the 3D printers are a great technological innovation, there are still challenges to overcome. Because material, time and energy required to fabricate an object are so valuable, it is desired to reproduce the intended geometry minimizing defects and material waste. This task result particularly difficult in open loop control strategies such as the ones in most printing systems. This work aims to improve the 3D printing process by proposing a supervision system that can close the control loop for the material output, reducing the printing defects produced by interruptions in the material flow, tangling of filaments and other problems that result in unwanted discontinuities. A more robust printing platform is also reported in which the supervision system will be implemented, aiming to provide a more reliable platform for research and testing working parameters, materials, etc. The paper concludes suggesting the tasks ahead to achieve the overall objective of improving the 3D printing process.
Laser beam melting (LBM) systems produce parts by melting metal powder according to the sliced 3D geometry using a laser. After each layer, new powder is deposited and the process is repeated. Process monitoring via acquisition and analysis of layer images during the build job is a promising approach to thorough quality control for LBM. Image analysis requires orthographic images, which are usually not available as the camera cannot be placed directly above the build layer due to the position of the laser window. The resulting perspective distortions have to be corrected before analysis. To this end we compute a homography from four circular markers which are " drawn " into the powder bed by the machine's laser and detected in the acquired images. In this work we present a robust method for the automatic detection of calibration markers, which deals with the noise-like powder regions, disconnected lines, visible support structures and blurred image regions. Our homography estimation method minimizes the shape error between transformed circular reference marker shapes and detected elliptical markers yielding an image with correct aspect ratio and minimal distortions. Our method achieves a detection rate of 96.3 % and a spatial detection error of 2.0 pixels (median, 95 %-percentile: 5.17 pixels) compared to a manually created ground truth.
Many Internet networks have limited internal diversity, making them vulnerable to serious malware spreading. A proposed malware-halting technique that uses software diversity can halt infectious outbreaks on these networks.
Recent cyberattacks have highlighted the risk of physical equipment operating outside designed tolerances to produce catastrophic failures. A related threat is cyberattacks that change the design and manufacturing of a machine's part, such as an automobile brake component, so it no longer functions properly. These risks stem from the lack of cyber-physical models to identify ongoing attacks as well as the lack of rigorous application of known cybersecurity best practices. To protect manufacturing processes in the future, research will be needed on a number of critical cyber-physical manufacturing security topics.
In computer vision and image processing, edge detection concerns the localization of significant variations of the grey level image and the identification of the physical phenomena that originated them. This information is very useful for applications in 3D reconstruction, motion, recognition, image enhancement and restoration, image registration, image compression, and so on. Usually, edge detection requires smoothing and differentiation of the image. Differentiation is an ill-conditioned problem and smoothing results in a loss of information. It is difficult to design a general edge detection algorithm which performs well in many contexts and captures the requirements of subsequent processing stages. Consequently, over the history of digital image processing a variety of edge detectors have been devised which differ in their mathematical and algorithmic properties. This paper is an account of the current state of our understanding of edge detection. We propose an overview ...
This paper describes the results of a study on the variability in the geometric accuracy of a metal test part manufactured by several service providers using either an electron beam or laser beam powder bed thermal fusion process. The part was a circle-diamond-square test part with an inverted cone that is used to evaluate the performance of five-axis milling machines. The study was conducted to aid development of standardized parameters and test methods to specify and evaluate the performance of additive manufacturing systems. Without standards for performance characterization, it is difficult to match system capabilities with part requirements and ensure consistent and predictable part quality across systems, operators, and manufacturing facilities.
Purpose
– Three‐dimensional printing (3DP) is an increasingly popular additive manufacturing (AM) process. The structure produced in 3DP comprises of two main elements: an external “shell” and an inner “core”. The variation of this structure and print strategy dictates many factors, such as the final physical properties, the final weight of the part, the total material usage, effects on warpage and the build speed. As such, the accuracy and repeatability of these geometric structures is of importance. The measurement and validation of the actual printed structure is especially challenging due to the nature of the materials system. The purpose of this paper is to present an effective method to analyze the internal structure of a 3DP.
Design/methodology/approach
– A dedicated video‐monitoring system has been developed to capture and characterize the 3DP build structure layer‐by‐layer. A significant image‐processing phase involved image calibration, filtering, thresholding and segmentation. The investigation is composed by three substudies. First, the reliability of the developed system was determined by comparing nominal dimensions of a benchmark part with video and contact measurements. The two studies have focused on the “shell” and “core” characterization, respectively.
Findings
– A resolution of 508 pixel per inch was determined. From the first studies, benchmark elements of 0.5 mm presented a deviation between 0.29 and 0.44 mm from their normal dimension. The thickness of the external shell was analyzed, in both clear and coloured modes. Dimensions ranged between 1.51 and 1.58 mm for a clear part, and 1.59 and 1.69 mm for the coloured version. A further study resulted in a 3D virtual model of the internal mesh structure, which had been printed at high saturation.
Originality/value
– The internal structure of a 3D printed part has been successfully analyzed by in‐process monitoring providing information and data not available through conventional analysis of the final part. This system provides a solution for real‐time non‐destructive analysis, which is currently absent in various forms of AM.
Evaluation of measurement data -Guide to the expression of uncertainty in measurement (GUM)
Joint Committee for Guides in Metrology, 2008, Evaluation of measurement data -Guide
to the expression of uncertainty in measurement (GUM).