Occurrence Of Defects Research Articles

Multiplexing of TMT labeling reveals folate-deficient diet-specific proteome changes in NTDs.

Introduction: In the early stage of embryonic development, the neural tube (NT) cannot be closed properly due to some complex factors, including environmental factors, genetic factors, and the relationship between various factors, leading to the occurrence of neural tube defects (NTDs). Methods: In this study, we induced a mouse model of NTDs by feeding mice with a low-folate diet and intraperitoneally injecting them with 1.5mg/kg methotrexate on E7.5. Fetal mice were achieved at E13.5, and we extracted proteins from brain tissues with trypsin digestion. After enzymatic digestion, peptides were labeled with TMT/iTRAQ and separated in high-performance liquid chromatography (HPLC) for subsequent liquid chromatography tandem mass spectroscopy (LC-MS/MS) analysis. We used gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation to analyze proteomic changes and analyze the functional enrichment of differentially expressed proteins (DEPs) in the NTD mice tissues. Results: A low-folate-induced mouse model was successfully constructed. Folate was used as a sensitizing agent, and the teratogenicity rate of the NTD fetal mice increased to 36.5% when the concentration of methotrexate was at 1.5mg/kg. Mass spectrometry was used to identify 6,614 proteins, and among them, 5,656 proteins were quantified. In the following proteomic analysis, GO classification and KEGG pathway enrichment analysis were conducted, and heatmaps were drawn for differentially expressed proteins (DEPs). The main pathways associated with NTDs, such as the Hedgehog, Wnt, p53, and Hippo signaling pathways and the one-carbon pool mediated by folate, can be identified through a protein-protein interaction (PPI) network. It was also found that the regulation of ribosomal proteins, such as RPL13 and RPL14, which are upregulated in NTDs, has a certain impact on neural tube development. Discussion: Our results revealed proteomic changes in the tissues of low-folate-induced NTD mice. Validation showed that ribosomal proteins play a regulatory role during the development of NTDs and provides new ideas for the pathogenesis and preventive measures of NTDs.

Enhancing additive manufacturing precision: Intelligent inspection and optimization for defect-free continuous carbon fiber-reinforced polymer

Artificial intelligence (AI) has emerged as a pivotal tool in managing extensive datasets, enabling pattern recognition, and deriving solutions, particularly revolutionizing additive manufacturing (AM). This study intends to develop AI deep machine learning image processing techniques for real-time defects detection in additively manufactured continuous carbon fiber-reinforced polymer(cCFRP) specimens. Leveraging YOLOv8- a state-of-the-art, single-stage object detection algorithm, this study focuses on the relationship between printing parameters and defect occurrences, specifically misalignment errors. The research delineates a methodological advancement by correlating detected defects with parameter optimization, leading to significant quality improvements in cCFRP specimens. An impressive 94 % accuracy in detecting misalignments was achieved through fine-tuning the nozzle temperature adjustment, resulting in significant reductions in misalignment errors, while minimal impact is observed from print bed temperature, feed amount, and feed rate/sec on refining the proposed model for identifying optimal parameters.

Towards understanding the influence of structured indenters geometry on material deformation behavior of indentation process

Nanoindentation has emerged as a pioneering technique for fabricating array structures at the nanoscale. This research incorporates molecular dynamics simulations to investigate the deformation mechanisms of materials influenced by structured indenters exhibiting diverse geometries. The primary objective of this study is to mitigate interference during the continuous indentation applied by the indenters, diminish the material's elastic recovery, and minimize the occurrence of defects. The simulation results unveil that plastic deformation of the workpiece induced by structured indenters manifests through the nucleation and slip of dislocations. The cylindrical indenter engenders the most pronounced residual indentation morphology, accompanied by substantial pile-up interference and an extensive range of dislocation expansion, surpassing that of other indenters. Conversely, pyramidal indenters exhibit minimal residual indentation morphology, albeit displaying considerable elastic recovery. A comprehensive evaluation that considers factors such as recovery after indentation, ease of processing, system stability, and material transfer against the indentation force is imperative for the determination of the ideally structured indenter. The meticulous analysis supports the efficacy of paraboloidal indenters possessing smaller taper angles in attaining consistent processing results, rendering them highly suitable for continuous indentation processing. The research content bears significant implications for advancing the application of structured indenters.

Preimplantation genetic testing as a means of preventing hereditary congenital myasthenic syndrome caused by RAPSN.

Congenital myasthenic syndrome is a heterogeneous group of inherited neuromuscular transmission disorders. Variants in RAPSN are a common cause of CMS, accounting for approximately 14%-27% of all CMS cases. Whether preimplantation genetic testing for monogenic disease (PGT-M) could be used to prevent the potential birth of CMS-affected children is unclear. Application of WES (whole-exome sequencing) for carrier testing and guidance for the PGT-M in the absence of a genetically characterized index patient as well as assisted reproductive technology were employed to prevent the occurrence of birth defects in subsequent pregnancy. The clinical phenotypes of stillborn fetuses were also assessed. The family carried two likely pathogenic variants in RAPSN(NM_005055.5): c.133G>A (p.V45M) and c.280G>A (p.E94K). And the potential birth of CMS-affected child was successfully prevented, allowing the family to have offspring devoid of disease-associated variants and exhibiting a normal phenotype. This report constitutes the first documented case of achieving a CMS-free offspring through PGT-M in a CMS-affected family. By broadening the known variant spectrum of RAPSN in the Chinese population, our findings underscore the feasibility and effectiveness of PGT-M for preventing CMS, offering valuable insights for similarly affected families.

Effects of Contaminations on Electric Arc Behavior and Occurrence of Defects in Wire Arc Additive Manufacturing of 316L-Si Stainless Steel

Additive Manufacturing is a manufacturing process that consists of obtaining a three-dimensional object from the deposition of material layer by layer, unlike conventional subtractive manufacturing methods. Wire Arc Additive Manufacturing stands out for its high productivity among the Additive Manufacturing technologies for manufacturing metal parts. On the other hand, the excessive heat input promotes increased residual stress levels and the occurrence of defects, such as pores, voids, a lack of fusion, and delamination. These defects result in abnormalities during the process, such as disturbances in electrical responses. Therefore, process monitoring and the detection of defects and failures in manufactured items are of fundamental importance to ensure product quality and certify the high productivity characteristic of this process. Thus, this work aimed to characterize the effects of different contaminations on the electric arc behavior of the Wire Arc Additive Manufacturing process and the occurrence of microscopic defects in thin walls manufactured by this process. To investigate the presence of defects in the metal preforms, experimental conditions were used to promote the appearance of defects, such as the insertion of contaminants. To accomplish the electric arc behavior analysis, voltage and current temporal data were represented through histograms and cyclograms, and the arc stability was assessed based on the Vilarinho index for a short circuit. Effectively, the introduction of contaminants caused electric arc disturbances that led to the appearance of manufacturing defects, such as inclusions and porosities, observed through metallographic characterization. The results confirm that the introduction of contaminations could be identified early in the Wire Arc Additive Manufacturing process through electric arc data analysis.

Elimination of Shrinkage in Ductile Iron Castings Using Computer Simulation of Casting and Solidification

The production of high-quality castings without foundry defects at minimal production costs is a constant priority for foundries. Innovation and optimization of production processes are key to achieving this goal. Computer simulation of foundry processes offers a modern alternative to expensive and time-consuming experiments in real foundries and provides a reliable representation and analysis of casting and solidification processes. A detailed analysis of the casting and solidification simulation results allows the prediction of various risks that can cause defects in cast castings, thereby reducing their quality and, last but not least, the cost of their production. This paper deals with the analysis of a computer simulation of the casting of a brake disc in a Slovak foundry. This brake disc has had shrinkages and micro shrinkages that reduce the internal quality of the casting. These defects occurred in the ribs in the upper part of the casting under the feeders. A computer simulation of the casting and solidification of this casting was made according to the real conditions. It turned out that the designed gating system with a system of feeders was not sufficient to eliminate the emerging defects. A new layout for the feeders was proposed, which ultimately eliminated the occurrence of defects based on the results of the computer simulation. The input parameters were set to be as close as possible to the actual needs of the foundry. Moreover, 3D models of the assemblies were designed in SolidWorks Premium 2015 x64 Edition CAD software, and the filling and solidification simulations were performed using the NovaFlow & Solid CV 4.6r42 simulation program.

Inference of highly time-resolved melt pool visual characteristics and spatially-dependent lack-of-fusion defects in laser powder bed fusion using acoustic and thermal emission data

With a growing demand for high-quality fabrication, the interest in real-time process and defect monitoring of laser powder bed fusion (LPBF) has increased, leading manufacturers to incorporate a variety of online sensing methods including acoustic sensing, photodiode sensing, and high-speed imaging. However, real-time acquisition of high-resolution melt pool images in particular remains computationally demanding in practice due to the high variability of melt pool morphologies and the limitation of data caching and transfer, making it challenging to detect the local lack-of-fusion (LOF) defect occurrences. In this work, we propose a new acoustic and thermal information-based monitoring method that can robustly infer critical LPBF melt pool morphological features in image forms and detect spatially-dependent LOF defects within a short time period. We utilize wavelet scalogram matrices of acoustic and photodiode clip data to identify and predict highly time-resolved (within a 1.0 ms window) visual melt pool characteristics via a well-trained data-driven pipeline. With merely the acoustic and photodiode-collected thermal emission data as the input, the proposed pipeline enables data-driven inference and tracking of highly variable melt pool visual characteristics with R2≥0.8. We subsequently validate our proposed approach to infer local LOF defects between two adjacent scanlines, showing that our proposed approach can outperform our selected baseline theoretical model based on previous literature. Revealing the physical correlation between airborne acoustic emission, thermal emission, and melt pool morphology, our work demonstrates the feasibility of creating an efficient and cost-effective acoustic- and thermal-based approach to facilitate online visual melt pool characterization and LOF defect detection. We believe that our work can further contribute to the advances in quality control for LPBF.

Defect Prediction for Capacitive Equipment in Power System

As a core component of the smart grid, capacitive equipment plays a critical role in modern power systems. When defects occur, they pose a significant threat to the safety of both other equipment and personnel. Hence, it is of great significance to predict whether defects occur in capacitive equipment in advance. To achieve this goal, we propose a novel method that integrates the weight of evidence (WOE) feature encoding with machine learning (ML). Five models, including support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), multi-layer perceptron (MLP), and linear classification, are employed with WOE features for defect prediction. Furthermore, based on the prediction of equipment with defects, an additional prediction is conducted to determine the potential defect level of the equipment. Experimental results demonstrate that the performance of each algorithm significantly improves with WOE encoding features. Particularly, the RF model with WOE encoding features exhibits optimal performance. In conclusion, the proposed method offers a promising solution for predicting the occurrence of defects and the corresponding defect levels of capacitive equipment. It enables relevant personnel to focus on and inspect equipment predicted to be at risk of defects, thereby preventing major malfunctions.

Noncanonical function of folate through folate receptor 1 during neural tube formation

Folate supplementation reduces the occurrence of neural tube defects (NTDs), birth defects consisting in the failure of the neural tube to form and close. The mechanisms underlying NTDs and their prevention by folate remain unclear. Here we show that folate receptor 1 (FOLR1) is necessary for the formation of neural tube-like structures in human-cell derived neural organoids. FOLR1 knockdown in neural organoids and in Xenopus laevis embryos leads to NTDs that are rescued by pteroate, a folate precursor that is unable to participate in metabolism. We demonstrate that FOLR1 interacts with and opposes the function of CD2-associated protein, molecule essential for apical endocytosis and turnover of C-cadherin in neural plate cells. In addition, folates increase Ca2+ transient frequency, suggesting that folate and FOLR1 signal intracellularly to regulate neural plate folding. This study identifies a mechanism of action of folate distinct from its vitamin function during neural tube formation.

Gaussian–Poisson Mixture Regression model for defects prediction in steelmaking

In the steelmaking process, real-time prediction of the occurrence of defects is crucial. To this end, a novel Gaussian–Poisson Mixture Regression (GPMR) model is proposed in this work. GPMR utilizes Poisson and Gaussian distributions to describe input process measurements and the count-type output quality variable, respectively. At the same time, inspired by the idea of finite mixture models, the two distributions are further extended into a mixture form to improve the predictive performance of the model, which also fully considers the characteristics of multi-mode and process variation in the steelmaking process. In addition, a parameter learning strategy based on variational inference is designed for the training of GPMR. The application results of a numerical example and an actual steelmaking process demonstrate the effectiveness and superiority of the proposed GPMR method compared to other traditional methods.

Dissolution dynamics of poly(4-hydroxystyrene-co-methacrylic acid) in tetraalkylammonium hydroxide aqueous solutions

Resist materials and their lithography processes are essential for achieving further miniaturization in semiconductor microfabrication. In the sub-20 nm half-pitch region, the development process becomes particularly important due to the occurrence of stochastic defects (pinching and bridging). However, the details of dissolution dynamics are still unclear. In this study, we investigated the dissolution dynamics of poly(4-hydroxystyrene-co-methacrylic acid) (PHSMA) films in tetraalkylammonium hydroxide (TAAH) aqueous solutions using a quartz crystal microbalance method. PHSMA is a typical backbone polymer used in extreme UV lithography. For poly(4-hydroxystyrene) films, it has been reported that the dissolution mode changed when the alkyl chain length of TAAH increased. However, the mode change was not observed for PHSMA films. This difference is considered to be caused by the formation of thick transient swelling layers of PHSMA films owing to the fast water intake and the molecular interaction of the hydroxyl groups of hydroxystyrene units.

Identifying optical microscope images of CVD-grown two-dimensional MoS2 by convolutional neural networks and transfer learning.

In Complementary Metal-Oxide Semiconductor (CMOS) technology, scaling down has been a key strategy to improve chip performance and reduce power losses. However, challenges such as sub-threshold leakage and gate leakage, resulting from short-channel effects, contribute to an increase in distributed static power. Two-dimensional transition metal dichalcogenides (2D TMDs) emerge as potential solutions, serving as channel materials with steep sub-threshold swings and lower power consumption. However, the production and development of these 2-dimensional materials require some time-consuming tasks. In order to employ them in different fields, including chip technology, it is crucial to ensure that their production meets the required standards of quality and uniformity; in this context, deep learning techniques show significant potential. This research introduces a transfer learning-based deep convolutional neural network (CNN) to classify chemical vapor deposition (CVD) grown molybdenum disulfide (MoS2) flakes based on their uniformity or the occurrence of defects affecting electronic properties. Acquiring and labeling a sufficient number of microscope images for CNN training may not be realistic. To address this challenge, artificial images were generated using Fresnel equations to pre-train the CNN. Subsequently, accuracy was improved through fine-tuning with a limited set of real images. The proposed transfer learning-based CNN method significantly improved all measurement metrics with respect to the ordinary CNNs. The initial CNN, trained with limited data and without transfer learning, achieved 68% average accuracy for binary classification. Through transfer learning and artificial images, the same CNN achieved 85% average accuracy, demonstrating an average increase of approximately 17%. While this study specifically focuses on MoS2 structures, the same methodology can be extended to other 2-dimensional materials by simply incorporating their specific parameters when generating artificial images.

DEVELOPMENT OF LOADING MODELS AND ANALYSIS OF THE STRESS-STRAIN STATE OF THE MAIN GAS PIPELINE WITH COMPOSITE LININGS

The operation of sections of main gas pipelines that are on the verge of developing a design resource is associated with a number of problems in ensuring industrial safety. One of the most acute problems is the occurrence of local defects. The main material for the manufacture of main gas pipelines is steel. During the operation of the pipeline, local defects occur due to metal corrosion or mechanical damage to the gas pipe. Such concentrators with high internal pressure in the gas pipeline can lead to the destruction of the entire structure. In this case, the situation of destruction is especially critical, since it can cause a man-made disaster. To increase the shelf life of damaged sections of main pipelines in places where defects are located, banding with composite linings is used. The article contains part of a comprehensive study, which consisted in the development of methods for modeling the main gas pipeline for oil and petroleum products under seismic loads, dynamic stress-strain state under the influence of seismic loads and stress-strain state of a damaged gas pipeline reinforced with composite linings under the action of internal pressure. The technology of manufacturing linings from composite material is also described in detail. At the same time, a geometric model of the section of the main gas pipeline with composite linings was developed and an analysis of the loading of the section of the main gas pipeline in ANSYS was carried out, provided that the lining is mounted without tension on the pipeline and provided that the lining is mounted with tension, where a comparative analysis showed that the installation condition of composite linings does not significantly affect the static stress state.

Fabrication and investigation of graphene‐loaded triiron tetraoxide/silicone‐rubber electromagnetic shielding composites based on static magnetic‐field–induced orientation

AbstractGraphene‐loaded triiron tetraoxide/silicone‐rubber composites containing highly loaded graphene‐loaded Fe3O4 (Fe3O4@RGO) particles were fabricated using static magnetic field‐induced alignment. The Fe3O4@RGO particles were characterized through Fourier transform infrared, x‐ray diffraction, SEM, and XPS analyses. Subsequently, the influence of added material and the strength of the magnetic field on the alignment distribution of the filler were examined through a synergistic combination of SEM and Raman spectroscopy. The electrical conductivity and electromagnetic shielding properties of the composites were assessed. The electrical conductivity of the composites increased with increasing filler addition, the 25 wt% filler mixture had the fastest curing rate and the maximum torque, and the continued increase in filler addition led to the occurrence of many defects. Concurrently, the alignment distribution of the filler progressively enhances with the augmentation of magnetic field strength. The composites synthesized at 180 mT exhibit the most substantial alignment, facilitating the establishment of effective conductive pathways. This results in a notable enhancement in electromagnetic shielding effectiveness, approximately 400% greater than that of pure silicone rubber and around 40% higher than that of non‐aligned composites. This approach introduces a novel concept for shaping the structural design of flexible electromagnetic shielding materials.Highlights Highly loaded Fe3O4@RGO was prepared by self‐assembly. The continuous phase structure of Fe3O4@RGO oriented was assessed. Fully oriented Fe3O4@RGO promotes the formation of conductive pathways. Fully oriented Fe3O4@RGO reduced the SER by 13% and increased the SEA by 67%.

Analisis Defect Rajungan (Portunus pelagicus) Kaleng Menggunakan Metode Failure Mode And Effect Analysis (FMEA) Di PT. XYZ

PT XYZ is an industry that operates in the fisheries sector with superior products, pasteurized crab meat in cans. Increasing market demand, including demand for canned crab exports, requires companies to produce products quickly and correctly. However, in the production of canned crab, defective products are often found or do not meet quality standards. The aim of this research is to identify defect problems in canned crab products and provide suggestions for improvements to overcome the problem in order to minimize the occurrence of defects that are detrimental to the company. Research data collection methods are through observation, interviews and questionary. The Failure Mode and Effect Analysis (FMEA) method is used to identify and analyze defects that occur. FMEA is used to determine the severity level, occurrence level and detection level, so that a Risk Priority Number (RPN) value can be obtained which can determine the priority of defects that must be prioritized for repair. The identification results at PT XYZ found 7 types of defects. The highest RPN defect value that occurs is a rust defect with an RPN value of 324. The defects that occur in this problem are caused by 4 factors, namely humans, methods, materials and the environment. Proposed improvements to reduce the chance of defects occurring in cans include material inspection, human/operator error evaluation, maintaining process environmental conditions, and optimizing the implementation of standard operating procedures (SOP)

Investigation on micro-milling of cemented carbide with ball nose and corner radius diamond-coated end mills

Micro-milling of cemented carbides is a challenging task due to their high hardness, low toughness and high wear resistance. Ensuring good surface quality and dimensional accuracy is crucial for extending parts service life, which in turn enhances economical and environmental sustainability. This paper is mainly focused on evaluating surface formation mechanisms, scale effects, fracture behaviour and chip formation using distinct cemented carbide micro-milling tools with multi-layer diamond HF-CVD. In order to achieve higher precision and more efficient micro-milling operations on WC-15Co and WC-10Co, a systematic experimental approach has been carried out. The influence of cutting parameters, achievable surface quality and defects occurrence were thoroughly examined. Experimental results evidence the influence of operational conditions on the chip formation of cemented carbides as well as an important impact of the utilized cutting tool. Micro-pits, cracks, thin ploughing layer and fractured workpiece edges are amongst the observed surface damage mechanisms. A ductile cutting regime of the high-hardness composite material is confirmed, exhibited by the plastic deformation even when small depths of cut are considered.

Defects in layered boron nitride grown by Metal Organic Vapor Phase Epitaxy: luminescence and positron annihilation studies

Defects in two-dimensional boron nitride (BN) are candidates for a manifold of applications, for example, as single-photon emitters or optically addressable spin defects. However, the topic has proven to be complex and despite extensive research efforts, knowledge on midgap defects and their signature in photoluminescence (PL) remains fragmentary. Mass production of high-quality layered BN by Metal Organic Vapor Phase Epitaxy (MOVPE) is possible and effective, thus understanding the conditions of formation and identifying defects that arise in the material grown by this method is extremely important. Here, we present an analysis of the influence of the MOVPE growth conditions of layered BN on the occurrence of specific structural defects. PL spectra in the visible range were analysed involving the Huang-Rhys theory implemented in the fitting procedure for polycrystalline samples grown under various conditions. Such an approach allowed us to separate bands from individual entities. Two bands at 1.57 eV and 2.6 eV with phonon replicas were observed. The three additional bands are characterized by a broad emission centered at 1.38 eV, 1.9 eV and 2.24 eV. Further properties, including an analysis of the surface morphology, the impact of annealing and laser bleaching on the PL spectra and Positron Annihilation Spectroscopy (PAS) were considered to draw conclusions about the nature of the examined defects. The PAS analysis correlated with the PL of the BN layers allows us to classify the studied samples in terms of the concentrations of boron vacancies and their complexes. Based on the obtained results, we propose that the rich family of PL bands observed in various sp2-BN layers is associated with different defect complexes build of boron vacancies (VB), boron antisite defects (BN), substitutional carbon defects (CB, CN) and their donor-acceptor pairs. The main value of this paper is the transfer of knowledge gained about defects and the corresponding luminescent bands to characterize high-quality continuous epitaxial sp2-BN layers. The presented results constitute an important step toward the deterministic manipulation of defect concentrations in two-dimensional boron nitride, which opens up new pathways for future optoelectronic device application.

Occurrence and distribution of lost molars and furcation defects in a Bulgarian population: A retrospective three-dimensional study.

To identify the ocuurrence, distribution, and factors associated with lost molars and furcation defects in a sample of the Bulgarian population. The full mouth cone-beam computed tomography of 56 male and 57 female patients, aged between 19 and 84 years, were examined. A comprehensive evaluation was performed on a total of 675 molars-339 in maxilla and 336 in mandible. Associations with variables such as age, gender, and periodontal disease were analyzed. With aging the number of missing teeth and interradicular lesions increased. No significant links between gender and molar loss (p = .64) or gender and furcation involvement (p = .25) was found. Periodontitis was strongly associated with both studied dental problems (p < .001). The occurrence of furcation defects was more frequently observed in the maxilla than in the mandible. The occurrence and distribution of lost molars and furcation defects is substantial within the studied sample. Further investigation with a larger patient cohort is needed. Pertinent healthcare strategies to address the observed dental health issues also have to be developed.

Analisis Pengendalian Kualitas pada Proses Produksi dengan Metode Six Sigma (Studi Kasus PT. Busana Indah Global)

To maintain consistent quality of production results and as an effort to identify unexpected factors that cause the production process to not function smoothly, quality control is very important. The research questions answered are how much production output and level of defects in products at PT. Busana Indah Global for the 2022 period?; what is the Six Sigma level in the products produced at PT. Busana Indah Global?; What factors cause defects in products?; What are the suggestions for improving the causes of product defects to improve product quality? The data analysis method used in this research is the Six Sigma method. This method is used to anticipate the occurrence of errors or defects by using measurable and structured steps. From the results of quality control research using the Six Sigma method, it is hoped that companies will carry out quality control continuously and consistently to reduce the level of defect values in production and to increase the Six Sigma value.

Influence of the LPBF Process Parameters on the Porosity of the AlSi10Mg Alloy

Abstract: Laser powder bed fusion (LPBF) process has a great ability to produce complex AlSi10Mg 3D components with uncommon degrees of freedom for broad span of applications in different industries. Presence of the microstructural imperfections such as porosity, dependent on the parameters of the process can be detrimental to the printed products for different engineering applications. Parameters of the process and different post-processing heat and surface treatments are recognized for decrease of the occurrence of microstructural defects and for the improvement of the mechanical properties. The influence of laser power by applying four different laser speeds at one layer thickness with a constant hatch distance, on the microstructure and microhardness of the AlSi10Mg alloy was examined in this paper. The goal of the research was to determine whether increasing the laser speed will have a significant impact on the change in microstructure and the appearance of porosity in the tested samples.