Layer Contour Research Articles

Layer Contour Geometric Characterization in MEX/P through CIS-Based Adaptive Edge Detection

The industrial adoption of material extrusion of polymers (MEX/P) is hindered by the geometric quality of manufactured parts. Contact image sensors (CISs), commonly used in flatbed scanners, have been proposed as a suitable technology for layer-wise characterization of contour deviations, paving the way for the application of corrective measures. Nevertheless, despite the high resolution of CIS digital images, the accurate characterization of layer contours in MEX/P is affected by contrast patterns between the layer and the background. Conventional edge-recognition algorithms struggle to comprehensively characterize layer contours, thereby diminishing the reliability of deviation measurements. In this work, we introduce a novel approach to precisely locate contour points in the context of MEX/P based on evaluating the similarity between the grayscale pattern near a particular tentative contour point and a previously defined gradient reference pattern. Initially, contrast patterns corresponding to various contour orientations and layer-to-background distances are captured. Subsequently, contour points are identified and located in the images, with coordinate measuring machine (CMM) verification serving as a ground truth. This information is then utilized by an adaptive edge-detection algorithm (AEDA) designed to identify boundaries in manufactured layers. The proposed method has been evaluated on test targets produced through MEX/P. The results indicate that the average deviation of point position compared to that achievable with a CMM in a metrology laboratory ranges from 8.02 µm to 13.11 µm within the experimental limits. This is a substantial improvement in the reliability of contour reconstruction when compared to previous research, and it could be crucial for implementing routines for the automated detection and correction of geometric deviations in AM parts.

3D Printing of Optical Lenses Assisted by Precision Spin Coating

AbstractThough 3D printing shows potential in fabricating complex optical components rapidly, its poor surface quality and dimensional accuracy render it unqualified for industrial optics applications. The layer steps in the building direction and the pixelated steps on each layer's contour result in inevitable microscale defects on the 3D‐printed surface, far away from the nanoscale roughness required for optics. This paper reports a customized vat photopolymerization‐based lens printing process, integrating unfocused image projection and precision spin coating to solve lateral and vertical stair‐stepping defects. A precision aspherical lens with less than 1 nm surface roughness and 1 µm profile accuracy is demonstrated. The 3D‐printed convex lens achieves a maximum MTF resolution of 347.7 lp mm−1. A mathematical model is established to predict and control the spin coating process on 3D‐printed surfaces precisely. Leveraging this low‐cost yet highly robust and repeatable 3D printing process, the precision fabrication of multi‐scale spherical, aspherical, and axicon lenses are showcased with sizes ranging from 3 to 70 mm using high clear photocuring resins. Additionally, molds are also printed to form multi‐scale PDMS‐based lenses.

Height consistency compensation in laser-directed energy deposition of thin-walled parts

Geometric defects, especially for the abnormal height variability of the layers, tend to occur in the laser-directed energy deposition (LDED) forming of thin-walled parts, which can severely affect the final accuracy and quality. Facing this problem, this work proposes a framework to implement the real-time layer height prediction and parameter feedback for the compensation on the height inconsistency during the thin-walled part forming. First, the deposited layer contours are extracted in succession and the corresponding height values are recorded using machine vision, including melt pool recognition, spatter removal and melt pool edge localization, etc. The height consistency of the deposited layer, as a data basis, is evaluated by calculating the newly introduced flatness rate. In addition, the mapping relationship between layer height and scanning speed (h-v) is established as the quantitative foundation of the parameter feedback algorithm. By iterating the h-v function with the height data of the previous deposition, the prediction of the next layer height can be achieved. Meanwhile, the parameter feedback can be performed to output a set of deposition interval specific scanning speeds, to be adopted for the height consistency compensation in the next layer deposition. Finally, through experimental validation, it is demonstrated that the proposed method has practicality and applicability in real-time height consistency compensation, and the flatness rate of the thin-walled part was improved by a maximum of 22.8 %. This study presents an in-situ instant process monitoring and defect repair approach to ensure geometric accuracy, aiming to inspire more stable and intelligent LDED forming.

Experimental study on boundary layer of internal flow visible supersonic nozzle

The high-frequency pulsation noise generated by the turbulent boundary layer on the wall of a Laval nozzle can significantly affect the quality of the flow field at the nozzle outlet. In this study, a supersonic wind tunnel with visible internal flow is designed and fabricated to observe the development and evolution of the boundary layer on the contraction and expansion surfaces of a Laval nozzle, as well as to study the flow field inside the supersonic nozzle. The subsonic, transonic and supersonic profiles of the nozzle are designed by bicubic curve, Hall method and classical characteristic line method respectively. The results of numerical calculation and total pressure measurement show that the flow field at the nozzle outlet of the wind tunnel is uniform and stable, and the deviation of Mach-number-root mean square is better than the qualified level of China's national military standard. Nanoparticle-tracer based planar laser scattering (NPLS) technology is used to carry out the flow display test of the internal flow visual supersonic nozzle, and the fine structure image of the whole flow field in the nozzle is obtained. The image clearly shows the development and evolution of the boundary layer in the nozzle. The interface between boundary layer and main stream and the wall curve of nozzle transition region are extracted by image processing technology. The fractal dimension of the extracted boundary layer contour is calculated, thereby establishing the corresponding relationship between the fractal dimension and the boundary layer state, and determining the transition position of the boundary layer. The results show that the transition position of the nozzle profile is closer to downstream than that of the nozzle straight wall. The fractal dimension can qualitatively judge the flow state of the boundary layer; however, it is necessary to distinguish between laminar boundary layers and hairpin vortices in the initial transition stage by considering the thickness of boundary layer.

On-Machine CIS SoC-Based Layerwise Inspection System for MEX Additive Manufacturing

Additive manufacturing processes build three-dimensional objects usually following a layer-upon-layer strategy. An interesting feature of this strategy is that each layer could be inspected before the next one is deposited. On-machine integration of layerwise inspection systems would not only allow for early characterization of the dimensional and geometric quality of the part, but also for the detection of intralayer defects. Contact image sensors (CIS), such as those used in desktop flatbed scanners, could be used for this purpose since they would provide bi-dimensional digital images of the whole layer and its neighborhood. CIS images combine high resolutions with a reduced acquisition time. In this work, a material extrusion (MEX) additive manufacturing system, with layerwise inspection capabilities is proposed. The system has been equipped with the CIS that Epson uses in its Perfection V39 flatbed scanner. The sensor provides two analog output signals, each one consisting on 2584 voltage levels, that represent the amount of light reflected by the material. This analog information is sent to a parallel AD converter, where an 8-bit encoding is assigned to each one of the pixels on the digitized image. To overcome microcontroller-related problems, a Zynq®-7000 system-on-chip (SoC) has been used. This SoC integrates an ARM® based processor, with the hardware programming of a field programmable gate array (FPGA). This architecture ensures an accurate and controlled readout of the various AD converters. The resultant digital image of each layer could then be then processed using different algorithms to detect defects, extract the geometry of the layer contour and characterize the dimensional and geometric quality of the object. In the example provided, a forced error consisting on 0.2 mm height local deviations, caused by a variation in extrusion temperature, was identified from 2D grayscale images obtained with the CIS sensor.

Development of mathematical model for surface roughness estimation in material jetting 3D printed parts

This research investigated the modeling of three relatively different surface profiles, based on outer layer contour, internal raster, and a combination of these two, to estimate the surface roughness of material jetting 3D printed parts. Theoretical and empirical modeling was used to determine the relationship between build orientation and layer thickness with surface roughness. Results indicated that the developed surface roughness model can be utilized to estimate the surface roughness value of other surface profiles built through the same machine using material jetting technology or even other additive manufacturing systems like fused filament fabrication. Also, the validation using existing models showed a significant reduction in the prediction error (1.008%) and provides a more accurate surface roughness model for predicting surface roughness values. To minimize the required surface roughness value in the material jetting 3D printed parts, a surface roughness optimization methodology based on changing the build orientation is suggested in this article. An expression of the direction of build orientation is presented and the relation between the surface roughness and build orientation is investigated taking color coding environment into consideration. The validity and effectiveness of the proposed optimization methodology are tested by simulation and experimental results.

Design of A Support Automatic Generation Algorithm Based on Layer Information

3D printing technology as a new type of manufacturing technology application field is getting larger and larger, and it also plays an important role in the field of large-scale construction machinery maintenance. In this chapter, the algorithm of support automatic generation of STL model is studied, and the algorithm of support automatic generation is designed based on the layer contour information. The problem of identification of suspension area is solved by the method of intersecting and comparing the contours of adjacent layers, which provides the algorithm basis for the application of subsequent improvement technology in the field of maintenance.

Microstructure and Surface Topography Study of Nanolayered TiAlN/CrN Hard Coating

The microstructure and surface topography of PVD hard coatings are among the most important properties, as they significantly determine their mechanical, tribological and other properties. In this study, we systematically analyzed the microstructure and topography of a TiAlN/CrN nanolayer coating (NL-TiAlN/CrN), not only because such coatings possess better mechanical and tribological properties than TiAlN and CrN monolayer coatings, mainly because the contours of the individual layers, in the cross-sectional STEM or SEM images of such coatings, make it easier to follow topographic and microstructural changes that occurred during its growth. We investigated the effects of the substrate rotation modes on the microstructure and surface topography of the NL-TiAlN/CrN coating, as well as on the periodicity of the nanolayer structure. The influence of the substrate material and the ion etching methods were also studied, while special attention was given to the interlayer roughness and influence of non-metallic inclusions in the steel substrates on the growth of the coating. The topographical features of the NL-TiAlN/CrN coating surface are correlated with the observations from the cross-sectional TEM and FIB analysis. Selected non-metallic inclusions, covered by the NL-TiAlN/CrN coating, were prepared for SEM and STEM analyses by the focused ion beam. The same inclusions were analyzed prior to and after deposition. We found that substrate rotation modes substantially influence the microstructure, surface topography and periodicity of the NL-TiAlN/CrN layer. Non-metallic inclusions in the substrates cause the formation of shallow craters or protrusions, depending on their net removal rates during the substrate pretreatment (polishing and ion etching), as compared to the matrix.

Geometry-Based Radiation Prediction of Laser Exposure Area for Laser Powder Bed Fusion Using Deep Learning

Laser powder bed fusion (LPBF) is a promising technique used to manufacture complex geometries in a layer-wised manner. Radiation during the LPBF process is influenced by the part geometry, e.g., the overhang angle and the wall thickness. Locally varying radiation can cause deformation of the product after manufacturing. Thus, the prediction of the geometry-caused radiation before the manufacturing can support the evaluation of the design printability to achieve first-time-right printing. In this paper, we present a framework to predict the geometry-based radiation information using a deep learning (DL) algorithm based on the part geometry from computer-aided design (CAD). The algorithm was trained using data from an LPBF-print job consisting of parts with varying overhang angles. Image data, which include the information of radiation, were captured with an optical tomography (OT) camera system that was installed on a LPBF machine used in a laboratory environment. For the DL algorithm, a U-Net based network with mean absolute error (MAE) loss was applied. The training input was binarized OT data representing the contour of the designed geometry. Complementary, the OT data were used as ground truth for the model training. For the application, the design contours of multiple layers were extracted from the CAD file. The result shows the applicability to predict the OT-like radiation by its contour, which has the possibility to show the anomaly due to the part geometry.

An Additive Manufacturing Direct Slicing Algorithm Based on a STEP Model

The Standard Template Library (STL) file is the most common data format for the description of an additive manufacturing (AM) geometric model, but it has some disadvantages, such as large errors of the geometric model description, the easy loss of topology information, data duplication, large file sizes, and so on. Aiming at these problems, a direct slicing algorithm based on a Standard for the Exchange of Product Model Data (STEP) model was proposed. For the parts composed of basic types of surfaces such as boundary curves, spherical surfaces and cylindrical surfaces, the traditional geometric method was used to calculate the intersection. For the parts with complex surfaces, the three-dimensional models were described based on Non-Uniform Rational B-Spline (NURBS) surfaces. The NURBS surfaces were layered using a discrete tracking algorithm, the tracking starting point was determined, the intersection line between the tangent plane and each NURBS sub-surface was obtained, and the closed layer contour was formed. Finally, the slicing simulations and printing experiments of solid parts were carried out using the direct slicing algorithm based on the STEP model. It was shown that the dimensional accuracy and surface quality of the printed parts from this algorithm had been significantly improved.

Research on recognition method of laser cladding dilution rate and contour quality based on Semantic Segmentation Visual Geometry Group network

Dilution rate and contour quality are important indicators for studying the forming quality of laser cladding technology. Currently, the acquisition of the dilution rate mainly relies on manual marking under the metallographic assistant software, and the evaluation of contour quality mainly relies on human observation. In order to solve the problem that the manual labeling of the dilution rate and the manual evaluation of the cladding layer contour quality rate are low and subjectively affected, this paper proposes the use of artificial intelligence to identify the cladding layer dilution rate and perform a parameterized evaluation of the cladding layer contour. The Semantic Segmentation Visual Geometry Group (SSVGG) network is proposed by improving the VGG16, and the improvement of the convolutional layer reduces the calculation of the convolutional network. Adding a deconvolution layer enables the features to be displayed to the original image dimension. The feature fusion layer is added to increase semantic information. The experiment proves that the accuracy of Intersection over Union for extracting cladding topography by the SSVGG network is 93.89%. The neural network can accurately output the size of the cladding layer and the dilution zone, the accuracy rate of obtaining the dilution rate is 93.33%, and the dilution rate obtained by the area method can better characterize the cladding effect compared with the length method. The arc of the cladding layer output by the neural network can be parameterized to indicate the quality of contour forming.

Methods and Experiments for Collecting Information and Constructing Models of Bottom Layer Contours in Paddy Fields

Methods and Experiments for Collecting Information and Constructing Models of Bottom Layer Contours in Paddy Fields

Layer contour characterization in additive manufacturing through image binarization

On-Machine Measurement adoption will be key to dimensional and geometrical improvement of additively manufactured parts. One possible approach based on OMM aims at using digital images of manufactured layers to characterize actual contour deviations with respect to their theoretical profile. This strategy would also allow for in-process corrective actions. This work describes a layer-contour characterization procedure based on binarization of digital images acquired with a flat-bed scanner. This procedure has been tested off-line to evaluate the influence of two of the parameters for image treatment, the median filter size (S f ) and the threshold value (T), on the dimensional/geometrical reliability of the contour characterization. Results showed that an appropriate selection of configuration parameters allowed to characterize the proposed test-target with excellent coverage and reasonable accuracy.

On Clinical Agreement on the Visibility and Extent of Anatomical Layers in Digital Gonio Photographs.

PurposeTo quantitatively evaluate the inter-annotator variability of clinicians tracing the contours of anatomical layers of the iridocorneal angle on digital gonio photographs, thus providing a baseline for the validation of automated analysis algorithms.MethodsUsing a software annotation tool on a common set of 20 images, five experienced ophthalmologists highlighted the contours of five anatomical layers of interest: iris root (IR), ciliary body band (CBB), scleral spur (SS), trabecular meshwork (TM), and cornea (C). Inter-annotator variability was assessed by (1) comparing the number of times ophthalmologists delineated each layer in the dataset; (2) quantifying how the consensus area for each layer (i.e., the intersection area of observers’ delineations) varied with the consensus threshold; and (3) calculating agreement among annotators using average per-layer precision, sensitivity, and Dice score.ResultsThe SS showed the largest difference in annotation frequency (31%) and the minimum overall agreement in terms of consensus size (∼28% of the labeled pixels). The average annotator's per-layer statistics showed consistent patterns, with lower agreement on the CBB and SS (average Dice score ranges of 0.61–0.7 and 0.73–0.78, respectively) and better agreement on the IR, TM, and C (average Dice score ranges of 0.97–0.98, 0.84–0.9, and 0.93–0.96, respectively).ConclusionsThere was considerable inter-annotator variation in identifying contours of some anatomical layers in digital gonio photographs. Our pilot indicates that agreement was best on IR, TM, and C but poorer for CBB and SS.Translational RelevanceThis study provides a comprehensive description of inter-annotator agreement on digital gonio photographs segmentation as a baseline for validating deep learning models for automated gonioscopy.

The Influence of Image Processing and Layer-to-Background Contrast on the Reliability of Flatbed Scanner-Based Characterisation of Additively Manufactured Layer Contours

Flatbed scanners (FBSs) provide non-contact scanning capabilities that could be used for the on-machine verification of layer contours in additive manufacturing (AM) processes. Layer-wise contour deviation assessment could be critical for dimensional and geometrical quality improvement of AM parts, because it would allow for close-loop error compensation strategies. Nevertheless, contour characterisation feasibility faces many challenges, such as image distortion compensation or edge detection quality. The present work evaluates the influence of image processing and layer-to-background contrast characteristics upon contour reconstruction quality, under a metrological perspective. Considered factors include noise filtering, edge detection algorithms, and threshold levels, whereas the distance between the target layer and the background is used to generate different contrast scenarios. Completeness of contour reconstruction is evaluated by means of a coverage factor, whereas its accuracy is determined by comparison with a reference contour digitised in a coordinate measuring machine. Results show that a reliable contour characterisation can be achieved by means of a precise adjustment of image processing parameters under low layer-to-background contrast variability. Conversely, under anisotropic contrast conditions, the quality of contour reconstruction severely drops, and the compromise between coverage and accuracy becomes unbalanced. These findings indicate that FBS-based characterisation of AM layers will demand developing strategies that minimise the influence of anisotropy in layer-to-background contrast.

In-process monitoring of part geometry in fused filament fabrication using computer vision and digital twins

Abstract On-machine, optical imaging of the layer is bound to play an important role in the development of future in-process monitoring solutions for fused filament fabrication. In addition to detecting layer internal defects, layer imaging offers the possibility to retrieve the outer contours of a layer and thus three-dimensionally reconstruct the side walls of a part by vertically stacking contours belonging to sequential layers. The current open challenges for layer imaging relate to the compromise between imaging resolution and extension of covered area, and to the complexity of image interpretation, exacerbated by optically difficult materials, complex filament patterns and non-uniform environmental conditions. In this work we propose an original optical imaging solution for layer contour identification and stacking. Our solution overcomes the current challenges related to range/resolution and image interpretation by making use of digital twins, i.e. geometric simulations of the layer, based on the current manufacturing process parameters. Thanks to the use of digital twins, our vision system is able to: use high-resolution imaging only where it is needed, by following the predicted positions of the layer contours while acquiring a sequence of images; algorithmically interpret each image by concentrating only on interesting regions as suggested by the simulated prediction, thus reducing the risk for false positives; and finally monitor contour quality by performing a more accurate comparison between the measurement and a reference contour obtained by a simulation which replicates the actual fingerprint of the process, as opposed to simply relying on the edge obtained by slicing the CAD model. The in-process monitoring capability of the computer vision system is demonstrated through the fabrication of acceptable and defected instances of a test geometry. Fabrication issues are effectively and efficiently detected by the system and alarms are triggered that can be used to command early termination of the build process or to initiate corrective actions.

Flue gas layer feature segmentation based on multi-channel pixel adaptive

It is very difficult to accurately separate the smoke contour in fire video. Because the scene of a fire is complex and changeable, and there are often many interference factors, such as continuously changing light and fast switching scenes, it is difficult to accurately guarantee the separation of smoke contours. In this paper, an aggregate channel feature algorithm that combines color, saturation and texture is designed, and a fast pixel block feature matching method is used to build the background model. In order to overcome the error caused by scene switching, a dynamic threshold control method based on the background switching speed is proposed, which eliminates the interference caused by the dynamic background update, and effectively extracts the foreground smoke pixels and smoke contour map. The experimental results show that the algorithm can accurately extract the smoke layer contour map, and compared with the traditional foreground extraction algorithms, the algorithm is faster and more accurate.

Effect of Direct Slicing on Precision Additive Manufacturing

An intelligent process planning for additive manufacturing (AM) is proposed in a paper presented at IFAC-IMS 20191. An important aspect of the intelligent process planning is to directly slice CAD models to generate paths for AM machines. In this paper an experimental approach is carried out to investigate the improvements in the results of fabrication using direct slicing approach. In the proposed process, a CAD model is directly sliced and layer contours are extracted from the ideal surface. The curvature based parametrization using a multi-step method is implemented for finding the position of layers. The dimensional errors are investigated first by comparing the errors generated during the tessellation process with the ideal surfaces. Then the results of printing using paths generated from STL files and the ideal surface defined in an IGES file are compared. The results show considerable improvements in surface continuity and dimensional accuracy of the parts fabricated form the direct slicing approach.

Layer Contour Verification in Additive Manufacturing by Means of Commercial Flatbed Scanners.

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.

A slicing algorithm to guarantee non-negative error of additive manufactured parts

Despite the potential benefits of additive manufacturing over traditional subtractive manufacturing, there are still several intrinsic problems that affect the accuracy and quality of the additive manufactured parts adversely, one of which is the undercut, i.e., the additive manufactured part cannot enclose the corresponding designed model fully, according to existing slicing methods. Therefore, the accuracy of the post-processed parts cannot be guaranteed as post processing is generally a process of subtractive manufacturing. In this paper, we propose a slicing algorithm to generate a new kind of layer contours. In this algorithm, not only the input model is intersected with a group of parallel slicing planes to get the layer contours but also the local geometry between two adjacent slicing planes of a layer is integrated into the final layer contours to prevent the undercut. Consequently, a non-negative error is guaranteed on the whole surface of the manufactured part in theory in order to ensure the final accuracy of the post-processed part. Several slicing results are given to demonstrate the validity of the proposed algorithm.