Printing Defects Research Articles

Additively Manufactured Lattice Materials with a Double Level of Gradation: A Comparison of Their Compressive Properties when Fabricated with Material Extrusion and Vat Photopolymerization Processes.

Natural porous materials adjust their resulting mechanical properties by the optimal use of matter and space. When these are produced synthetically, they are known as mechanical metamaterials. This paper adds degrees of tailoring of mechanical properties by producing double levels of gradation in lattice structures via cross-section variation in struts in uniformly periodic lattice structures (UPLS) and layered lattice structures (LLS). These were then additively manufactured via material extrusion (ME) and vat photopolymerization (VP). Their effective mechanical properties under compressive loads were characterized, and their stiffness contrasted with finite element models (FEM). According to the simulation and experimental results, a better correlation was obtained in the structures manufactured via VP than by ME, denoting that printing defects affect the correlation results. The brittle natural behavior of the resin caused a lack of a plateau region in the stress-strain curves for the UPLS structures, as opposed to those fabricated with ME. The LLS increased energy absorption up to 244% and increased the plateau stress up to 100% compared to the UPLS. The results presented in this paper demonstrate that the mechanical properties of lattice structures with the same base topology could be modified by incorporating variations in the strut diameter and then arranging these differently.

Formation and Control of Pore Defects in Metal 3D Printing

Formation and Control of Pore Defects in Metal 3D Printing

Effect of printing parameters on the tensile properties of improperly stored chopped carbon fibres reinforced nylon

In the case of 3D printing of polymer matrix composites, the most widespread material is nylon. Improper storage of nylon results in moisture absorption, which in the case of Fused Filament Fabrication affects the print quality due to uncontrolled deposition of the material into the structure. The uncontrolled deposition results in printing imperfections which the authors faced during the preparation process of specimens for the articles. The imperfections negatively affect the resulting mechanical properties. It has been shown that modification of the nozzle temperature is one of the options which could solve the issue of improperly stored materials printing. The specimens did not report any apparent printing defects. However, the modification of the nozzle temperature affects the joint quality between the individual laminas. The results showed that the quality improved heating of the printing bed. To identify the effect of the printing parameters modifications, the authors carried out a tensile test to compare the tensile properties of a) specimens printed from properly stored material at default printing parameters and b) specimens printed from improperly stored material with modified printing parameters. According to the results, these series reported different fracture patterns. On the other hand, the tensile strength values were comparable.

A lightweight model for digital printing fabric defect detection based on YOLOX

Online detection of digital printing defects is a necessary but challenging topic. The performance of the current detection methods is still not ideal for the diversified patterns of digital printing fabric defects and the realtime requirements of online detection. In this paper, we proposed a lightweight model of digital printing fabric defect detection based on YOLOX. Firstly, according to the characteristics of many types of defects and complex background of digitally printed fabrics, a defect detection network structure based on YOLOX is constructed. Then, the SE attention module is introduced to enhance important features and weaken unimportant features, which make the extracted features more directional. And it can further solve the influence of small feature size on the detection accuracy of small targets. The experimental results show that the proposed model has a detection accuracy of 66.2 mAP on our self-built dataset, which is 2.7 percentage points higher than YOLOX. This method can effectively solve the problem that low detection accuracy of small defects. The proposed model can meet the real-time requirements and improve the detection accuracy of small target defects.

Analysis of Cup Printing Quality Control Using Statistical Process Control Methods and Human Reliability Assessment (Case Study: PT Indo Ceria Plastic Printing)

This research was conducted at PT Indo Ceria Plastic Printing aims to determine the main cause of defective cup printing and to reduce product defects in the production process. Based on the problems faced by the company, namely the high percentage of cup printing defects which eventually led to more and more reject cup printing rates, even the problem of passing the defective cup being sent to the customer’s hands, which made the customer complain about the problem of the number of bad product quality being sent so that they finally submitted a returned product. Therefore, quality control efforts are carried out on cup printing products to find the cause of the defect and find a solution to repair it. The research methods used in solving the problems that occur at PT Indo Ceria Plastic Printing is to use Statistical Process Control methods and Human Reliability Assessment. From the result of research conducted, it is known that the biggest cause of defect in cup printing products is “defect awal start”. From the results of meaning employee performance, the largest HEP value was found, namely, the type of possible error is “pengisian tinta pada mesin” which was 0,6324. So, this factor was the dominant one that resulted in defects and must be repaired immediately.

Toward Industry 4.0 in Surface Mount Technology: Smart Manufacturing in Stencil Printing Operations

As the foremost point of contact in surface mount technology (SMT) operations, the stencil/solder paste printer (SPP) is a major contributor to assembly defects in the printed circuit board (PCB) fabrication process. Initial printing defects lead to reductions in throughput, quality and reliability of the entire downstream system. Because of the high variation in SMT processes, a predictive, real-time machine learning (ML) algorithm to optimize printer settings based on the estimation of printing volumes is a promising solution to improving production performance and process capability. Building upon existing literature, this research discusses the implementation of a lab-trained ML model to predict optimal printing parameters and reduce changeover time within SPP processes through the training and testing of large solder paste inspection (SPI) datasets. To sustain growing market demand for smart electronics manufacturing (SEM), successful development and validation of a dynamic prediction system for optimizing printing parameters provides significant potential for improvements in first pass yield rates and system throughput.

Formation of printing defects and their effects on mechanical properties of additively manufactured metal alloys

Printing defects are known to degrade the performance of additively manufactured (AM) alloys. Thus, a thorough understanding of their formation mechanisms and effects on the mechanical properties of AM materials is critically needed. Here, we take CoCrFeNi high-entropy alloy as a model material and print this alloy by laser powder bed fusion over a wide range of printing conditions. We reveal the processing windows for the formation of different printing defects including lack of fusion (LOF), keyhole, and solidification cracking. LOF and keyholes can be well correlated with insufficient and excessive laser energy density inputs, respectively. Of particular interest, we observe that solidification cracks only emerge at the medium laser energy density region, where the porosity is minimal yet the grain size and misorientation are relatively large. Such observation is rationalized within the framework of Rappaz–Drezet–Gremaud solidification theory. Among the above printing defects, solidification cracks in AM CoCrFeNi result in less degradation of mechanical properties compared with LOF and keyholes due to their different defect densities and resultant capabilities of coalescence. Our work provides fundamental insight into understanding the physical origins underlying the formation of printing defects and their impacts on the mechanical properties of AM metals and alloys.

Sim-YOLOv5s: A method for detecting defects on the end face of lithium battery steel shells

The detection of lithium battery shell defects is an important aspect of lithium battery production. The presence of pits, R-angle injuries, hard printing, and other defects on the end face of lithium battery shells severely affects the production safety and usage safety of lithium battery products. In this study, we propose an effective defect-detection model, called Sim-YOLOv5s, for lithium battery steel shells. In this model, we propose a fast spatial pooling pyramid structure, SimSPPF, to speed up the model and embed the attention mechanism convolutional block attention module in the backbone. Contextual information can be aggregated over a large perceptual field by using the new upsampling operator, which has a larger field of perception. A cross-layer connection operation is performed to fuse shallow feature information with deep feature information. The experimental results show that the proposed Sim-YOLOv5s model has a better overall performance with a mean average precision of 88.3%, which is 6.9% better than that of YOLOv5s. Therefore, the proposed Sim-YOLOv5s can lay the foundation for the industrial implementation of real-time inspection of lithium battery products.

Battery screen print defect detection based on stationary velocity fields neural network matching and optical flow rectification.

In this study, an automatic defect detection method is proposed for screen printing in battery manufacturing. It is based on stationary velocity field (SVF) neural network template matching and the Lucas-Kanade (L-K) optical flow algorithm. The new method can recognize and classify different defects, such as lacking, skew, and blur, under the condition of irregular shape distortion. Three critical processing stages are performed during detection: (1) Image preprocessing was performed to acquire the printed region of interest and then image blocking was carried out for template creation. (2) The SVF network for image registration was constructed and the corresponding dataset was built based on oriented fast and rotated brief feature matching. (3) Irregular print distortion was rectified and defects were extracted using L-K optical flow and image subtraction. Software and hardware systems have been developed to support this method in industrial applications. To improve environment adaptation, we proposed a dynamic template updating mechanism to optimize the detection template. From the experiments, it can be concluded that the method has desirable performance in terms of accuracy (97%), time efficiency (485ms), and resolution (0.039mm). The proposed method possesses the advantages of image registration, defect extraction, and industrial efficiency compared to conventional methods. Although they suffer from irregular print distortions in batteries, the proposed method still ensures a higher detection accuracy.

In situ volumetric imaging and analysis of FRESH 3D bioprinted constructs using optical coherence tomography

As 3D bioprinting has grown as a fabrication technology, so too has the need for improved analytical methods to characterize engineered constructs. This is especially challenging for engineered tissues composed of hydrogels and cells, as these materials readily deform when trying to assess print fidelity and other properties non-destructively. Establishing that the 3D architecture of the bioprinted construct matches its intended anatomic design is critical given the importance of structure-function relationships in most tissue types. Here we report development of a multimaterial bioprinting platform with integrated optical coherence tomography for in situ volumetric imaging, error detection, and 3D reconstruction. We also report improvements to the freeform reversible embedding of suspended hydrogels bioprinting process through new collagen bioink compositions, gelatin microparticle support bath optical clearing, and optimized machine pathing. This enables quantitative 3D volumetric imaging with micron resolution over centimeter length scales, the ability to detect a range of print defect types within a 3D volume, and real-time imaging of the printing process at each print layer. These advances provide a comprehensive methodology for print quality assessment, paving the way toward the production and process control required for achieving regulatory approval and ultimately clinical translation of engineered tissues.

Artificial Defects in 316L Stainless Steel Produced by Laser Powder Bed Fusion: Printability, Microstructure, and Effects on the Very‐High‐Cycle Fatigue Behavior

The printability of artificial defects inside the additively manufactured laser powder bed fusion (LPBF) 316L stainless steel is investigated. The printing parameters of the LPBF process are optimized to produce artificial defects with reproducible sizes at desired positions while minimizing redundant porosity. The smallest obtained artificial defect is 90 μm in diameter. The accuracy of the geometry of the printed defect depends on both the height and the diameter in the input model. The effect of artificial defects on the very‐high‐cycle fatigue (VHCF) behavior of LPBF 316L stainless steel is also studied. The specimens printed with artificial defects in the center are tested under VHCF using an ultrasonic machine. Crack initiation is accompanied by the formation of a fine granular area (FGA), typical of VHCF. Despite the presence of relatively large artificial defects, FGA formation is observed around accidental natural printing defects closer to the surface, which can still be considered as internal. The causes for this occurrence are discussed.

A framework for graph-base neural network using numerical simulation of metal powder bed fusion for correlating process parameters and defect generation

Powder bed fusion (PBF) is the most common technique used for metal additive manufacturing. This process involves consolidation of metal powder using a heat source such as laser or electron beam. During the formation of three-dimensional(3D) objects by sintering metal powders layer by layer, many different thermal phenomena occur that can create defects or anomalies on the final printed part. Similar to other additive manufacturing techniques, PBF has been in practice for decades, yet it is still going through research and development endeavors which is required to understand the physics behind this process. Defects and deformations highly impact the product quality and reliability of the overall manufacturing process; hence, it is essential that we understand the reason and mechanism of defect generation in PBF process and take appropriate measures to rectify them. In this paper, we have attempted to study the effect of processing parameters (scanning speed, laser power) on the generation of defects in PBF process using a graph-based artificial neural network that uses numerical simulation results as input or training data. Use of graph-based machine learning is novel in the area of manufacturing let alone additive manufacturing or powder bed fusion. The outcome of this study provides an opportunity to design a feedback controlled in-situ online monitoring system in powder bed fusion to reduce printing defects and optimize the manufacturing process.

AI-Aided Printed Line Smearing Analysis of the Roll-to-Roll Screen Printing Process for Printed Electronics

Screen printing has been adopted for fabricating a wide variety of electronic devices. However, the printing defects and reliability have been an obstacle for industrialization of printed electronics. In this research, the artificial intelligence (AI) model was developed and integrated with the in-house roll-to-roll screen printing system to detect smearing defect, which is one of the main defects of screen printing. The U-Net architecture was adopted, and a total of 19 models were designed with model sizes ranging from 8E + 3 to 3E + 7 number of parameters. Their performances as validation mean Intersection over Union (IoU) were analyzed, and the optimal model was chosen with a validation mean IoU of 95.1% and a number of parameters of 8E + 6. The printed line images were evaluated by the AI model for various printing conditions, such as printed line widths, printing paste premixing, printing speeds, and printed line directions, which showed that the model could effectively detect the smearing defects. Also, the AI model capabilities were investigated for repeated printing, which demonstrated that it can be used for the reliability assessment of the screen printing process.

Study of the Printability, Microstructures, and Mechanical Performances of Laser Powder Bed Fusion Built Haynes 230

The nickel-based superalloy, Haynes 230 (H230), is widely used in high-temperature applications, e.g., heat exchangers, because of its excellent high-temperature mechanical properties and corrosion resistance. As of today, H230 is not yet in common use for 3D printing, i.e., metal additive manufacturing (AM), primarily because of its hot cracking tendency under fast solidification. The ability to additively fabricate components in H230 attracts many applications that require the additional advantages leveraged by adopting AM, e.g., higher design complexity and faster prototyping. In this study, we fabricated nearly fully dense H230 in a laser powder bed fusion (L-PBF) process through parameter optimization. The efforts revealed the optimal process space which could guide future fabrication of H230 in various metal powder bed fusion processes. The metallurgical analysis identified the cracking problem, which was resolved by increasing the pre-heat temperature from 80 °C to 200 °C. A finite element simulation suggested that the pre-heat temperature has limited impacts on the maximum stress experienced by each location during solidification. Additionally, the crack morphology and the microstructural features imply that solidification and liquation cracking are the more probable mechanisms. Both the room temperature tensile test and the creep tests under two conditions, (a) 760 °C and 100 MPa and (b) 816 °C and 121 MPa, confirmed that the AM H230 has properties comparable to its wrought counterpart. The fractography showed that the heat treatment (anneal at 1200 °C for 2 h, followed by water quench) balances the strength and the ductility, while the printing defects did not appreciably accelerate part failure.

Recent advances in melt electro writing for tissue engineering for 3D printing of microporous scaffolds for tissue engineering

Melt electro writing (MEW) is a high-resolution 3D printing technique that combines elements of electro-hydrodynamic fiber attraction and melts extrusion. The ability to precisely deposit micro- to nanometer strands of biocompatible polymers in a layer-by-layer fashion makes MEW a promising scaffold fabrication method for all kinds of tissue engineering applications. This review describes possibilities to optimize multi-parametric MEW processes for precise fiber deposition over multiple layers and prevent printing defects. Printing protocols for nonlinear scaffolds structures, concrete MEW scaffold pore geometries and printable biocompatible materials for MEW are introduced. The review discusses approaches to combining MEW with other fabrication techniques with the purpose to generate advanced scaffolds structures. The outlined MEW printer modifications enable customizable collector shapes or sacrificial materials for non-planar fiber deposition and nozzle adjustments allow redesigned fiber properties for specific applications. Altogether, MEW opens a new chapter of scaffold design by 3D printing.

The Influence of Intralayer Porosity and Phase Transition on Corrosion Fatigue of Additively Manufactured 316L Stainless Steel Obtained by Direct Energy Deposition Process.

A direct energy deposition (DED) process using wires is considered an additive manufacturing technology that can produce large components at an affordable cost. However, the high deposition rate of the DED process is usually accompanied by poor surface quality and inherent printing defects. These imperfections can have a detrimental effect on fatigue endurance and corrosion fatigue resistance. The aim of this study was to evaluate the critical effect of phase transition and printing defects on the corrosion fatigue behavior of 316L stainless steel produced by a wire laser additive manufacturing (WLAM) process. For comparison, a standard AISI 316L stainless steel with a regular austenitic microstructure was studied as a counterpart alloy. The structural assessment of printing defects was performed using a three-dimensional non-destructive method in the form of X-ray microtomography (CT) analysis. The microstructure was evaluated by optical and scanning electron microscopy, while general electrochemical characteristics and corrosion performance were assessed by cyclic potentiodynamic polarization (CCP) analysis and immersion tests. The fatigue endurance in air and in a simulated corrosive environment was examined using a rotating fatigue setup. The obtained results clearly demonstrate the inferior corrosion fatigue endurance of the 316L alloy produced by the WLAM process compared to its AISI counterpart alloy. This was mainly related to the drawbacks of WLAM alloys in terms of having a duplex microstructure (austenitic matrix and secondary delta-ferrite phase), reduced passivity, and a significantly increased amount of intralayer porosity that acts as a stress intensifier of fatigue cracking.

Heat treatment for selective laser melting of Inconel 718 alloy with simultaneously enhanced tensile strength and fatigue properties

Inconel 718 alloy fabricated by selective laser melting (SLM) was used to investigate the evolution of precipitated phase from as-SLM to heat-treated (HT) samples. A heat treatment, including homogenization, solution and aging heat treatments, was proposed to obtain SLMed Inconel 718 alloy components for desirable mechanical performance. Post heat treatment process can reduce the number of original print defects due to grain boundary migration and grain growth. The solid solution plus double aging treatment (HT-1) results in the precipitation of large amounts of δ phase within the grain and at grain boundaries, as well as a small amount of massive MC. Homogenization, solid solution plus double aging (HT-2) eliminated a large amount of internal δ phases in the grain as well as needle-like δ phases at grain boundaries. However, the single aging (HT-3) still retains some of the needle-like δ phase. Heat-treated specimens increased tensile strength by 20–24% at room temperature and maintained high strength at 650 °C compared to the as-SLM. Further fatigue results also confirmed that the HT-2 treatment increases the fatigue life by an order of magnitude compared to the as-SLM to 10 8 cycles at a stress amplitude of 300 MPa. This work is anticipated to provide a potential route to obtaining attractive mechanical properties for SLM superalloy components. • The evolution of precipitated phase in SLMed Inconel 718 alloy under different heat treatment systems was studied. • Post heat treatment significantly improves strength of SLMed Inconel 718 alloy and maintains the high strength at 650 °C. • HT-2 heat treatment significantly increases the ultra-high cycle symmetric bending fatigue strength and life of the specimens .

Online Monitoring of Surface Quality for Diagnostic Features in 3D Printing

Investigation into non-destructive testing and evaluation of 3D printing quality is relevant due to the lack of reliable methods for non-destructive testing of 3D printing defects, including testing of the surface quality of 3D printed parts. The article shows how it is possible to increase the efficiency of online monitoring of the quality of the 3D printing technological process through the use of an optical contactless high-performance measuring instrument. A comparative study of contact (R130 roughness tester) and non-contact (LJ-8020 laser profiler) methods for determining the height of irregularities on the surface of a steel reference specimen was performed. It was found that, in the range of operation of the contact method (Ra 0.03–6.3 µm and Rz 0.2–18.5 µm), the errors of the contactless method in determining the standard surface roughness indicators Ra and Rz were 23.7% and 1.6%, respectively. Similar comparative studies of contact and non-contact methods were performed with three defect-free samples made of plastic polylactic acid (PLA), with surface irregularities within the specified range of operation of the contact method. The corresponding errors increased and amounted to 65.96% and 76.32%. Finally, investigations were carried out using only the non-contact method for samples with different types of 3D printing defects. It was found that the following power spectral density (PSD) estimates can be used as diagnostic features for determining 3D printing defects: Variance and Median. These generalized estimates are the most sensitive to 3D printing defects and can be used as diagnostic features in online monitoring of object surface quality in 3D printing.

Defects in 3D/4D food printing and their possible solutions: A comprehensive review.

3D food printinghas recently attracted significant attention, both from academic and industrial researchers, due to its ability to manufacture customized products in such terms as size, shape, texture, color,and nutrition to meet demands of individual consumers. 4D printing, which is a technique that allows evolution of various characteristics/properties of 3D printed objects over time through external stimulation, has also been gaining more attention.In order to produce defect-free printed objects via both 3D and 4D printing, it is necessary to first identify the causes of defects and then their mitigation strategies. Comprehensive review on these important issues is nevertheless missing. The purpose of this review is to investigate causes and characteristics of defects occurring during and/or after 3D food printing, with a focus on how different factors affect the printing accuracy. Various techniques that can potentially minimize or eliminate printing defects and produce high-quality 3D/4D printed food products without the need for time-consuming trial and error printing experiments are critically discussed. Guidelines to avoid defects to improve the efficiency of future 3D/4D printed food production are given.

Determining of process parameters of the PLA/titanium dioxide/hydroxyapatite filament

ABSTRACT In this study, filaments for FDM 3D printers were produced by extrusion method by mixing 95% polylactic acid (PLA), 1% hydroxyapatite (HA) and 4% titanium dioxide (TiO2). The extruder temperature, raster angle and layer thickness parameters, and printing defects of the prints made with the FDM 3D printer were examined. According to the dimensional analysis, 0.5–2% dimensional deviations occurred in 3D prints. The least average roughness value was 12.1 µm on the Z-axis surface, the highest average roughness value was 14.6 µm on the Y-axis surface. The highest ultimate tensile strength was 57 MPa at the layer thickness of 0.15 mm, extruder temperature of 205°C and raster orientation of 45°. The highest modulus of elasticity was 2020 MPa at the layer thickness of 0.15 mm, extruder temperature of 220°C and raster orientation of 45°. The highest elongation at break was 2.0 mm at the layer thickness of 0.20 mm, extruder temperature of 220°C and raster orientation of 45°. The dimensional deviations in the X, Y and Z axes plane were approximately 1%. X parallelism, Y parallelism and Z profile surface values were approximately 0.6%.