Printing Defects Research Articles

Fabrication, Mechanics, and Reliability Analysis for Three-Dimensional Printed Lattice Designs

Abstract The use of three-dimensional (3D) printing for lattice structures has led to advances in diverse applications benefitting from mechanically efficient designs. Three-dimensional printed lattices are often used to carry loads, however, printing defects and inconsistencies potentially hinder performance. Here, we investigate the design, fabrication, mechanics, and reliability of lattices with repeating cubic unit cells using probabilistic analysis. Lattices were designed with 500 μm diameter beams and unit cell lengths from 0.8 mm to 1.6 mm. Designs were printed with stereolithography and had average beam diameters from 509 μm to 622 μm, thereby demonstrating a deviation from design intentions. Mechanical experiments were conducted and demonstrated an exponential increase in yield stress for lattice relative density that facilitated probabilistic failure analysis. Sensitivity analysis demonstrated lattice mechanics were most sensitive to fluctuations for beam diameter (74%) and second to lattice yield stress (8%) for lattices with 1.6 mm unit cells, while lattices with smaller 1.0 mm unit cells were most sensitive to yield stress (48%) and second to beam diameter (43%). The methodological framework is generalizable to further 3D printed lattice systems, and findings provide new insights linking design, fabrication, mechanics, and reliability for improved system design that is crucial for engineers to consider as 3D printing becomes more widely adopted.

Effects of Printing Parameters on the Fatigue Behaviour of 3D-Printed ABS under Dynamic Thermo-Mechanical Loads.

Fused deposition modelling (FDM) is the most widely used additive manufacturing process in customised and low-volume production industries due to its safe, fast, effective operation, freedom of customisation, and cost-effectiveness. Many different thermoplastic polymer materials are used in FDM. Acrylonitrile butadiene styrene (ABS) is one of the most commonly used plastics owing to its low cost, high strength and temperature resistance. The fabricated FDM ABS parts commonly work under thermo-mechanical loads in actual practice. For producing FDM ABS components that show high fatigue performance, the 3D printing parameters must be effectively optimized. Hence, this study evaluated the bending fatigue performance for FDM ABS beams under different thermo-mechanical loading conditions with varying printing parameters, including building orientations, nozzle size, and layer thickness. The combination of three building orientations (0°, ±45°, and 90°), three nozzle sizes (0.4, 0.6, and 0.8 mm) and three-layer thicknesses (0.05, 0.1, and 0.15 mm) were tested at different environmental temperatures ranging from 50 to 70 °C. The study attempted to find the optimal combination of the printing parameters to achieve the best fatigue behaviour of the FDM ABS specimen. The experiential results showed that the specimen with 0° building orientation, 0.8 mm filament width, and 0.15 mm layer thickness vibrated for the longest time before the fracture at each temperature. Both a larger nozzle size and thicker layer height can increase the fatigue life. It was concluded that printing defects significantly decreased the fatigue life of the 3D-printed ABS beam.

Metallization of Thermoplastic Polymers and Composites 3D Printed by Fused Filament Fabrication

Fused filament fabrication allows the direct manufacturing of customized and complex products although the layer-by-layer appearance of this process strongly affects the surface quality of the final parts. In recent years, an increasing number of post-processing treatments has been developed for the most used materials. Contrarily to other additive manufacturing technologies, metallization is not a common surface treatment for this process despite the increasing range of high-performing 3D printable materials. The objective of this work is to explore the use of physical vapor deposition sputtering for the chromium metallization of thermoplastic polymers and composites obtained by fused filament fabrication. The thermal and mechanical properties of five materials were firstly evaluated by means of differential scanning calorimetry and tensile tests. Meanwhile, a specific finishing torture test sample was designed and 3D printed to perform the metallization process and evaluate the finishing on different geometrical features. Furthermore, the roughness of the samples was measured before and after the metallization, and a cost analysis was performed to assess the cost-efficiency. To sum up, the metallization of five samples made with different materials was successfully achieved. Although some 3D printing defects worsened after the post-processing treatment, good homogeneity on the finest details was reached. These promising results may encourage further experimentations as well as the development of new applications, i.e., for the automotive and furniture fields.

The 3D/4D Printing Defects and Their Influence on the Functional Behavior of the Achieved Items from Renewable Compounds. (I)

The paper is part of a series in which the influence of the manufacturing defects on the functional behavior in biodegradation medium of some items obtained, both by 3D printing and by classical procedure (pressing), from an originaly renwable matrials based on polylatic acid will be presented. The first results regarding the correlation of the defects appeared at manufacturing into plates with the biodegradation behavior in an Aspergillus Niger(A.niger) medium, studied by SEM microscopy, are presented. These results demonstrated that the development of the A. Niger microorganism is related manly to the defects appeared at the melt processing of renewable polymeric material into finished product. A notable role in controlling the appearance of the manufacturing defects belongs both to the melt rheological properties which are responsible for the continuous or discontinuous flow and to the technical performance of the used equipement, 3D printer or classic hydraulic press. If the polymeric material melt has too high viscosity than the continuous flow is not possible and so the overlapped melt fronts are created which generate the voids formation, sometimes joined by small nano and/or micrometric channels. The rheological properties of the melts depend both on the material formulation and the seleted melt processing conditions.

Back-trap mottle: A review of mechanisms and possible solutions

Print mottle is a serious and common uneven printing defect that occurs when printing coated paper by the offset multicolor printing process. It is characterized by a non-uniform appearance in terms of brightness, gloss, or color density that appears mostly in solid printed areas. Back-trap print mottle and water-interference print mottle occur when the quality of the paper substrate, especially the coating layer quality, is not satisfactory. To cope with this quality problem of coated papers, the understanding of the offset printing process, the requirements of coated-paper quality, and the reasons for this problem should be addressed. In this review, the basic process of offset printing and the mechanisms of print mottles were explored, the importance of coating uniformity in both the coating structure and process was reviewed, and the approaches to cope with print mottle were introduced.

Automatic defect detection for fabric printing using a deep convolutional neural network

ABSTRACT Defect detection is a crucial step in textile and apparel quality control. An efficient defect detection system can ensure the overall quality of the processes and products that are acceptable to consumers. Existing techniques for real-time defect detection tend to vary according to unique manufacturing processes, focal defects and computational algorithms. Although the need is high, research related to automatic printed fabric defect detection processes is not prevalent in academic literatures. This research proposes a novel methodology that demonstrates the application of convolutional neural network (CNN) to classify printing defects based on the fabric images collected from industries. The research also integrated visual geometric group (VGG), DenseNet, Inception and Xception deep learning networks to compare model performance. The results exhibit that the VGG-based models perform better compared to a simple CNN model, suggesting promise for automatic defect detection (ADD) of printed fabrics that can improve profitability in fashion supply chains.

Unsupervised defect detection algorithm for printed fabrics using content-based image retrieval techniques

Automatic detection of printing defect technology is significant for improving printing fabrics’ appearance and quality. In this research, we proposed an unsupervised printing defect detection method by processing the difference map between the test image and the reference image. Aimed at this, we adopted a content-based image retrieval (CBIR) method to retrieve the reference image, which includes an image database, a convolutional denoising auto-encoder (CDAE) and a hash encoder (HE): the elements of image database are extracted from only one defect-free sample image of the test fabric; the CDAE prevents the system being affected by the texture of the fabric and provides a reliable feature description of the patterns; the HE indexes the feature vectors to binary code while maintaining their similarity; both CDAE and HE are trained in an unsupervised manner. With the retrieved reference image, the defect is determined by applying the Tsallis entropy thresholding and opening operation on the difference map. The method can be implemented without labeled and defective samples, and without consideration of the periodical primitive of patterns. Experimental results demonstrate the effectiveness and efficiency of the proposed method in defect detection for printed fabrics with complex patterns.

Effects of embedded spherical pore on the tensile properties of a selective laser melted Ti6Al4V alloy

Selective laser melting (SLM) is one of the most promising additive manufacturing (AM) technologies in recent years. However, some sub-millimeter defects often appear accidently in SLM specimens, resulting in deterioration of mechanical properties. Therefore, the effects of these defects on the mechanical properties of the SLM materials are significantly important. In this study, the effect of a single spherical defect on the tensile properties was investigated by embedding a spherical pore inside the SLM Ti6Al4V specimens. Different combinations of pore diameters (Ф100 μm, Ф300 μm, Ф500 μm, Ф1000 μm) and locations (center, sub-surface, near-surface) were designed to mimic the sub-millimeter printing defects. The results demonstrated that these embedded pores had negligible influence on the elastic modulus and tensile strength but had a noticeable influence on the tensile elongation. With the increase of pore size, the elongation to failure of the specimens tended to be stochastic, stably high and stably low in sequence, which could be related to different fracture modes. The tensile specimens fractured at the embedded pore when the embedded pore size was increased to a critical diameter (approximately Ф1000 μm). Concurrently, the location of the embedded pore started to have an obvious influence on ductility, namely, the elongation to failure decreased as the embedded pore became closer to free surface.

Effect of micron-scale manufacturing flaws on the tensile response of centimeter sized two-photon polymerization microlattices

Two-photon polymerization (TPP) is a unique fabrication technique that enables sub-micron scale resolution. Herein, we report on uniaxial tensile tests on millimeter-sized TPP log-pile structures that were fabricated using a recently developed interdigitated stitching strategy. As expected, the Young’s modulus increased with laser intensity, however elastic modulus, ultimate strength and strain-at-failure varied widely, even for nominally identical samples. Post-mortem analysis revealed a series of print defects resulting from various sources including misalignments, resin shrinkage, laser beam shadowing, and local depletion of the oxygen inhibitor. While some of the defects can be eliminated, such as misalignments due to insufficient precision of the mechanical stage, others are more intrinsic to TPP and thus more difficult to address.

Connecting structural defects to tensile failure in a 3D-printed fully-amorphous bulk metallic glass

Abstract Selective laser melting (SLM) is a promising powder bed based additive manufacturing technique for the near-net-shape production of large-scale bulk metallic glasses (i.e. 3D-printed BMGs). Initial defects such as pores inherent to the SLM process are regarded as key factors for the mechanical integrity of BMGs. Here the structural defects in the form of pores are controlled in the SLM-processing of large-scale and fully-amorphous Zr-based BMGs. Their effects on the tensile fracture behaviors are revealed by experimental and computational studies. Under uniaxial tension, 3D-printed BMGs fracture in a brittle mode with almost zero macroscopic plasticity. The ultimate tensile strength (UTS) exhibits a rapid linear decrease with increasing porosity, while the Young's modulus is found to be insensitive in the low-porosity ( 2 % ) regime but decreases quickly in the high-porosity regime. Finite element (FE) simulations show different deformation mechanisms for pore-free and pore-containing BMGs. The former fractures with abundant homogeneously distributed shear bands initiated from material inherent weak points, while the latter experiences firstly plastic deformation around some pores with shear transformation zones (STZs). These STZs connect with each other as the load increases, forming a small number of highly localized shear bands, which develop into the final fracture surface. The orientation of the fracture surface in 3D-printed BMGs tends to show a larger angle than that of most as-cast Zr-based BMGs under tension (e.g. 50 ∘ ) and is predominantly affected by pore structures. The experimental and computational results imply that the printing defects play a dominant role in shear band formation and corresponding mechanical properties for 3D-printed BMGs.

Generation mechanism and suppression method of landing error of two successively deposited metal droplets caused by coalescence and solidification

Accurate deposition of metal droplets is crucial to limit geometrical defects in metal droplet-based 3D printing. However, unpredictable movements of the droplets displaced from the original landing location due to surface tension-driven flow before it is hindered by solidification, named landing error, lead to broken traces, irregular waved surfaces, or collapsed structures. In the present work, the effect of solidification at the early-time coalescence of two deposited metal droplets on the landing error was revealed. High-speed images were presented to observe coalescence dynamics. A volume of fluid (VOF) based numerical model was developed to analyze the generation mechanism of the landing error. An interesting phenomenon is observed that the second droplet absorbs more mass from the first droplet to form a bigger spherical crown with the increase of substrate temperature, resulting in the distinct final shapes. Inspired by this, a mass conservation model was proposed to predict the dependence of the landing error on substrate temperature. Metal droplets overlapping experiments were further conducted to verify the model and establish the relationship between the landing error and the substrate temperature. Predictions agreed well with experimental results. Finally, a strategy of increasing the thermal conductivity of the substrate to suppress the landing error at high substrate temperatures was proposed, which significantly expanded the processing window of the accurate deposition. This work can provide useful theoretical and experimental guidance for accurate printing of high-quality lines or patterns by utilizing metal droplets.

A wavelet-based multi-dimensional temporal recurrent neural network for stencil printing performance prediction

The solder paste printing (SPP) is a critical procedure in a surface mount technology (SMT) based assembly line, which is one of the major attributes to the defect of the printed circuit boards (PCBs). The quality of SPP is influenced by multiple factors, such as the squeegee speed, pressure, the stencil separation speed, cleaning frequency, and cleaning profile. During printing, the printer environment is dynamically varying due to the physical change of solder paste, which can result in a dynamic variation of the relationships between the printing results and the influential factors. To reduce the printing defects, it is critical to understand such dynamic relationships. This research focuses on determining the printing performance during printing by implementing a wavelet filtering-based temporal recurrent neural network. To reduce the noise factor in the solder paste inspection (SPI) data, this research applies a three-dimensional dual-tree complex wavelet transformation for low-pass noise filtering and signal reconstruction. A recurrent neural network is utilized to model the performance prediction with low noise interference. Both printing sequence and process setting information are considered in the proposed recurrent network model. The proposed approach is validated using practical dataset and compared with other commonly used data mining approaches. The results show that the proposed wavelet-based multi-dimensional temporal recurrent neural network can effectively predict the printing process performance and can be a high potential approach in reducing the defects and controlling cleaning frequency. The proposed model is expected to advance the current research in the application of smart manufacturing in surface mount technology.

Manufacturing of complex diamond-based composite structures via laser powder-bed fusion

Manufacturing complex metal matrix composite (MMC) structures by laser powder-bed fusion (LPBF) could unleash their full potential but is difficult due to the presence of reinforcement. Unmelted particles negatively affect the pool dynamics, cause critical spatter ejections, and form printing defects. In this work, by taking copper (Cu) / diamond (D) composite as an example for its prospective thermal management applications and machinability limitations; we discovered that adding steps to LPBF enables the fabrication of high-quality materials and structures. We demonstrated that adding a recoating step improves the composite quality compared to structures manufactured by conventional LPBF. Adding a remelting step enabled further improvement by limiting the generation of spatter and printing defects, leading to 3D laser print dense (97%), highly thermally conductive (349 W/m K) and complex Cu/5 vol% D structures. Therefore, pursuing research into nonconventional LPBF could open new avenues for manufacturing MMCs.

Streak Detection in Color Printed Customer Content

Streaks are one of the most common print defects in electrophotographic printers that influence print quality. Streak defects are dark or light lines with the major axis along the printing process direction and are usually caused by a defective Intermediate Transfer Belt (ITB) or Organic Photo Conductor (OPC) component in the printer. Previously, we designed an algorithm to detect the streak defects only on standard printed test pages, which have uniform color [1]. In this paper, we design an algorithm to detect the streak defects on the customer content area, which we call a raster ROI. It is more complicated than the uniform color printed page because the customer content influences our streak detection result. Sometimes, the customer content has some dark or light straight lines along the printing process direction, and they are similar to streak defects. To detect the streak defects on the customer pages, we must separate the straight lines of customer content and the streak defects. To detect the streak in a raster ROI, we apply the Sobel edge detection algorithm and morphological operations to the master image, which includes the customer content without defects, to remove the straight customer content lines on the scanned test pages. The remaining dark or light straight line along the printing process direction may be streak defects. For the detected streak defects, we use the DAG-SVM multi-classification method to classify the rank of streak defects in the raster ROI.

Numerical study of shear bands formation in a 3D‐printed Zr‐based bulk metallic glass under uniaxial loading

AbstractProcessing metallic materials via selective laser melting (SLM) enables the production of bulk metallic glasses (BMGs) with large size and complex shape, which is difficult or even impossible to achieve using traditional manufacturing techniques. In this study, the initiation and propagation of shear bands as well as the tensile strength in a 3D‐printed BMG with printing defects by means of inherent porosities are studied. A multi‐slips system model based on Mohr‐Coulomb theory is applied.

In-Situ Print Characterization and Defect Monitoring of 3D Printing via Conductive Filament and Ohm’s Law

Additive manufacturing (AM), as a means for rapid product development, has seen continued growth in adoption to facilitate complex low volume part production. Fused deposition modelling (FDM), the AM fabrication of parts with thermoplastic and thermosetting materials, biomaterial, and metal, is popular due to its high level of precision and low cost. However, one of the downsides of AM, and FDM, is that any variance in extrusion consistency during the printing process can potentially alter a part in such a manner that any confidence in the parts durability and structural soundness becomes unsure. Not to mention, even the simplest of defects in the middle of a print quite often propagates and ruins the entire part. To combat these issues, printing methods are introduced so that an electrical signal can be obtained over the course of a print by using a sourcemeter and printing with conductive filament. In the following publication, this technique is shown to not only allow a manufacturer to characterize an AM part as it is being printed, but also monitor and alert the production team if any serious defects occur during the print.This concept in and of itself can surge the achievable resolution in AM to new heights.

Quenching and Hot Isostatic Pressing of Additively Manufactured Tool Steel

A maraging grade and a modified H13 type tool steel were used as testing materials to study the possibility of healing printing defects while combining a solutionizing/hardening treatment to the components. Extensive mechanical testing and microstructural analysis showed that the high-pressure heat treatment can stabilize retained austenite in maraging grades, but has no effects on the aging kinetics. A HIP temperature of 850°C is not sufficient to heal printing defects in highly defective maraging components, while 1020°C were sufficient to fully heal a modified H13 component. Increasing HIP temperature over 1140°C causes lower impact toughness, due to grain growth and/or precipitation and growth of MnS.

Fibre misalignment and breakage in 3D printing of continuous carbon fibre reinforced thermoplastic composites

This paper investigates the formation of manufacturing induced fibre misalignment and breakage during fused filament fabrication (FFF) 3D printing of 1 K continuous carbon fibre filament. Single stripes at various turning angles and curvatures are printed by a desktop printer Prusa i3 using a specific brass nozzle and characterised using X-ray computed micro-tomography (µCT) and optical microscopy. A finite element (FE) model of the printing process is also established to support the experimental measurement. It has been found that high porosity and fibre misalignment in the printed straight stripe result from the weak fibre/matrix interface and the uneven pressure executed by the nozzle. Increase of turning angle and/or reducing of curvature radius leads to more aggravated printing defects, including shape inaccuracy, fibre twisting, folding and misalignment, due to the excessive force from the nozzle, debonding with the print bed and the unmatched geometry of nozzle outlet and fibre filament. Severe fibre breakage and significant change of thickness can be seen in the samples with turning angles larger than 120° or curvature radius smaller than 5 mm, while the wrinkles of the stripe in the inner periphery appear more frequently as the curvature radius decreases.

Real-Time 3D Printing Remote Defect Detection (Stringing) with Computer Vision and Artificial Intelligence

This work describes a novel methodology for the quality assessment of a Fused Filament Fabrication (FFF) 3D printing object during the printing process through AI-based Computer Vision. Specifically, Neural Networks are developed for identifying 3D printing defects during the printing process by analyzing video captured from the process. Defects are likely to occur in 3D printed objects during the printing process, with one of them being stringing; they are mostly correlated to one of the printing parameters or the object’s geometries. The defect stringing can be on a large scale and is usually located in visible parts of the object by a capturing camera. In this case, an AI model (Deep Convolutional Neural Network) was trained on images where the stringing issue is clearly displayed and deployed in a live environment to make detections and predictions on a video camera feed. In this work, we present a methodology for developing and deploying deep neural networks for the recognition of stringing. The trained model can be successfully deployed (with appropriate assembly of required hardware such as microprocessors and a camera) on a live environment. Stringing can be then recognized in line with fast speed and classification accuracy. Furthermore, this approach can be further developed in order to make adjustments to the printing process. Via this, the proposed approach can either terminate the printing process or correct parameters which are related to the identified defect.

Accurate Prediction of Melt Electrowritten Laydown Patterns from Simple Geometrical Considerations

AbstractMelt electrowriting (MEW), an electrohydrodynamic additive manufacturing technology, can produce polymer structures with micrometer resolution. Unlike conventional direct writing of fluids, the laydown pattern often deviates from the programmed one, due to a lag in the electrified jet. MEW of increasingly complex patterns currently entails a cumbersome trial‐and‐error procedure of adjusting printing parameters to ensure good accuracy. This issue is addressed with a geometrical model that predicts the expected printed fiber path, based on the coded trajectory, critical translation speed, and the collector speed. The influence of the process parameters on the jet lag is investigated and a general equation for the lag computation is implemented into the model. The presented model can evaluate the printing result up to ten times more accurate than the designed collector movement path and thus assist in identifying printing defects and adjustment of the printing G‐code in advance.