Final Object Research Articles

Phenomenology of photons-enriched semi-visible jets

This Letter proposes a new signature for confining dark sectors at the LHC. Under the assumption of a QCD-like hidden sector, hadronic jets containing stable dark bound states originating from hidden strong dynamics, known as semi-visible jets, could manifest in proton-proton collisions. In the proposed simplified model, a heavy Z′\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Z'$$\\end{document} mediator coupling to SM quarks allows the resonant production of dark quarks, subsequently hadronizing in stable and unstable dark bound states. The unstable dark bound states can then decay back to SM quarks via the same Z′\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Z'$$\\end{document} portal or photons via a lighter pseudo-scalar portal (such as an axion-like particle). This mechanism creates a new signature where semi-visible jets are enriched in non-isolated photons. We show that these exotic jets evade the phase space probed by current LHC searches exploiting jets or photons due to the expected high jet neutral electromagnetic fraction and photons candidates non-isolation, respectively. In the proposed analysis strategy to tackle such signature, we exploit jets as final state objects to represent the underlying QCD-like hidden sector. We show that, by removing any selection on the neutral electromagnetic fraction from the jet identification criteria, higher signal efficiency can be reached. To enhance the signal-to-background discrimination, we train a deep neural network as a jet tagger that exploits differences in the substructure of signal and background jets. We estimate that with the available triggers for Run 2 and this new strategy, a high mass search can claim a 5σ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$5 \\sigma $$\\end{document} discovery (exclusion) of the Z′\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Z'$$\\end{document} boson with a mass up to 5 TeV (5 TeV) with the full Run 2 data of the LHC when the fraction of unstable dark hadrons decaying to photons pairs is around 30%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${30}{\\%}$$\\end{document}, and with a coupling of the Z′\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Z'$$\\end{document} to SM quarks of 0.25.

4D Printing of Liquid Crystal Emulsions for Smart Structures with Multiple Functionalities.

3D printing, and more recently 4D printing, has emerged as a transformative technology for fabricating structures with complex geometries and responsive properties. However, employing functional colloidal solutions as inks for printing remains unexplored. In this work, we present a novel and versatile 4D printing approach for fabricating functional and complex-shaped objects using polymerizable liquid crystal (LC) emulsion droplets. Leveraging a digital light processing (DLP) 3D printing technique, we achieve rapid production of intricate 3D geometries with high resolution. The printed structures retain the LC ordering from the precursor droplets, imparting the final objects with shape memory properties, including shape fixation and recovery upon heating or light exposure. Light-responsive behavior is introduced post-printing by embedding an azo dye into the 3D structures. Additionally, we explore the potential to create intrinsically porous 3D structures by selectively removing non-reactive components from the printed geometries, adding an extra level of functionality to the printed objects. Furthermore, we incorporate chiral nematic LCs into the emulsion droplets, producing 3D objects with tunable reflective properties. To our knowledge, this is the first example of DLP 3D printing with emulsions, offering an effective and versatile pathway for developing 4D-printed materials with potential applications in optics, robotics, and biomedicine.

Integrated Anti-Aliasing and Fully Shared Convolution for Small-Ship Detection in Synthetic Aperture Radar (SAR) Images

Synthetic Aperture Radar (SAR) imaging plays a vital role in maritime surveillance, yet the detection of small vessels poses a significant challenge when employing conventional Constant False Alarm Rate (CFAR) techniques, primarily due to the limitations in resolution and the presence of clutter. Deep learning (DL) offers a promising alternative, yet it still struggles with identifying small targets in complex SAR backgrounds because of feature ambiguity and noise. To address these challenges, our team has developed the AFSC network, which combines anti-aliasing techniques with fully shared convolutional layers to improve the detection of small targets in SAR imagery. The network is composed of three key components: the Backbone Feature Extraction Module (BFEM) for initial feature extraction, the Neck Feature Fusion Module (NFFM) for consolidating features, and the Head Detection Module (HDM) for final object detection. The BFEM serves as the principal feature extraction technique, with a primary emphasis on extracting features of small targets, The NFFM integrates an anti-aliasing element and is designed to accentuate the feature details of diminutive objects throughout the fusion procedure, HDM is the detection head module and adopts a new fully shared convolution strategy to make the model more lightweight. Our approach has shown better performance in terms of speed and accuracy for detecting small targets in SAR imagery, surpassing other leading methods on the SSDD dataset. It attained a mean Average Precision (AP) of 69.3% and a specific AP for small targets (APS) of 66.5%. Furthermore, the network’s robustness was confirmed using the HRSID dataset.

Modified Multiresolution Convolutional Neural Network for Quasi-Periodic Noise Reduction in Phase Shifting Profilometry for 3D Reconstruction

Fringe profilometry is a method that obtains the 3D information of objects by projecting a pattern of fringes. The three-step technique uses only three images to acquire the 3D information from an object, and many studies have been conducted to improve this technique. However, there is a problem that is inherent to this technique, and that is the quasi-periodic noise that appears due to this technique and considerably affects the final 3D object reconstructed. Many studies have been carried out to tackle this problem to obtain a 3D object close to the original one. The application of deep learning in many areas of research presents a great opportunity to to reduce or eliminate the quasi-periodic noise that affects images. Therefore, a model of convolutional neural network along with four different patterns of frequencies projected in the three-step technique is researched in this work. The inferences produced by models trained with different frequencies are compared with the original ones both qualitatively and quantitatively.

Salient object detection with bayesian inference based on radar and camera fusion used in UAV obstacle avoidance

Most existing salient object detection methods are sensitive to background noise and rely on prior information in UAV obstacle avoidance applications despite detection methods witnessing rapid progress. In this paper, we propose an efficient framework for salient object detection based on radar-camera fusion and iterative Bayesian optimization. A rough salient object (RSO) image is first built through radar and camera calibration. Next, the RSO image is used to calculate the edge response based on the receptive field mechanism of the primary visual cortex to construct the contour image. Finally, the above two images and the 2D Gaussian distribution are jointly integrated into an iterative Bayesian optimization scheme to get the final salient object image. Different from typical detection methods, this method suppresses background noise by filtering out irrelevant pixels using fusion information. The Bayesian inference framework’s detection performance is improved by precise spatial prior, consisting of optimized contour and RSO images. Experimental results indicate that the presented algorithm performs well against state-of-the-art salient object detection methods on several reference datasets in different evaluation metrics.

Best practices for machine learning strategies aimed at process parameter development in powder bed fusion additive manufacturing

AbstractThe process parameters used for building a part utilizing the powder-bed fusion (PBF) additive manufacturing (AM) system have a direct influence on the quality—and therefore performance—of the final object. These parameters are commonly chosen based on experience or, in many cases, iteratively through experimentation. Discovering the optimal set of parameters via trial and error can be time-consuming and costly, as it often requires examining numerous permutations and combinations of parameters which commonly have complex interactions. However, machine learning (ML) methods can recommend suitable processing windows using models trained on data. They achieve this by efficiently identifying the optimal parameters through analyzing and recognizing patterns in data described by a multi-dimensional parameter space. We reviewed ML-based forward and inverse models that have been proposed to unlock the process–structure–property–performance relationships in both directions and assessed them in relation to data (quality, quantity, and diversity), ML method (mismatches and neglect of history), and model evaluation. To address the common shortcomings inherent in the published works, we propose strategies that embrace best practices. We point out the need for consistency in the reporting of details relevant to ML models and advocate for the development of relevant international standards. Significantly, our recommendations can be adopted for ML applications outside of AM where an optimum combination of process parameters (or other inputs) must be found with only a limited amount of training data.

To Study the Need of Systemic Formwork for Waste Water Treatment at Fish Processing Plant' (A Review)

Waste water from angle preparation enterprises originates from several sources, including emptying angles, dressing, liquefying water from ice dissolution, washing equipment and utensils, treating with additional substances, and cleaning and disinfecting offices and spaces. Water is used not only to clean angles but also to wash blood, sludge, and offal from the surfaces of various handling equipment, utensils, floors, and sump pumps. The majority of computerized handling supplies come with a water shower framework that may clean hardware and remove offal. The emergence of these forms in tall water use also leads to the mixing of blood and offal in the wash water. Fish handling waste water is full of significant pollutants that are colloidal, soluble, and particulate. Features of waste water produced depend on the type of angle used for preparation, the preparation method, and the final object placed. When handling operations such as minor (angle washing or cleaning), mild (angle filet preparation), and severe (surimi planning), the degree of defilement can change. Fish waste water must have exceptionally high levels of nitrogen, fat, oil, and oil (Mist), as well as biochemical oxygen demand (BOD). In order to reduce shameful waste transfer in the environment, this extended show introduces angle waste management processors to the concepts of fishery waste categorization and the many types of treatment used. This topic includes many aspects of angle waste, separate management of fluid angle waste and strong waste using chemicals, Indore composting technique, and suitable transfer.

On-line and in-line quality assessment across all scale levels of 3D concrete printing

3D Concrete printing requires much more elaborate quality control procedures compared to conventional concrete processing. Due to the various process steps, and the corresponding variation in material behaviour, time-, and length-scales, a single quality indicator and measurement technique (similar to the ‘slump test’ for traditional construction) cannot be selected. Instead, three families of quality indicators have been established: homogeneity during material production and deposition (quality variations), material evolution during printing (transient material behaviour), and macroscopic features and geometric conformity during printing and of the final object (geometry). For each family, quality assessment techniques which have been proven in other fields or for different applications, have been successfully transferred and adapted to the 3DCP process. In some cases, completely new methods have been developed. This paper aims to provide the state-of-the-art in such quality assessment methods, indicating high potential methods and research gaps across all scale levels of 3D concrete printing processes.

Statistical Analysis of Features for Detecting Leukemia

In this age of digital microscopy, image processing, statistical analysis, categorization, and systems for decision-making have become essential tools for medical diagnostics research. By visualizing and analyzing images, clinicians can identify anomalies in intracellular structure. Leukemia is a cancerous condition marked by an unregulated increase in aberrant white blood cells (WBCs). Recognizing acute leukemia tumor cells in blood smear images (BSI) is a challenging assignment. Image segmentation is regarded as the most significant step in the automated identification of this disease. The innovative concavity-based segmentation algorithm is employed in this study to segment WBC in sub-images from the ALLIDB2 database. The concave endpoints and elliptical features are used in the segmentation step of convex-shaped cell images. The procedure involves the extraction of contour evidence, which detects the visible section of each object, and contour estimation, which corresponds to the final object’s contours. Following the identification of the cells and their internal structure by concavity-based segmentation, the cells are categorized based on their morphological and statistical features. The method was evaluated using a public dataset meant to test classification and segmentation approaches. The statistical tool SPSS is used to independently check the significance of derived features. For classification, significant features are passed into machine learning techniques such as support vector machines (SVM), k-nearest neighbor (KNN), neural networks (NN), decision trees (DT), and Nave Bayes (NB). With an AUC of 98.9% and a total accuracy of 95%, the neural network model performed better. We advocate using the neural network model to identify acute leukemia cells based on its accuracy.

Experimental high-energy physics without computer simulations

This paper argues against the claim that high-energy physics experiments done so far could not be carried out without computer simulations. We show that it would be possible to completely dispense with computer simulations for experiments conducted to date, and that computer simulations up to now are mostly used for practical reasons. Our investigation covers all elements of experimental research in which computer simulations have been used. Dispensing with simulations would yield an advantage with regard to the complex theory dependence of experiments. We also point out that computer simulations may play a more essential role for the complex measurements foreseen at the Large Hadron Collider, where subtle dependencies between final state objects in high-energy physics experiments must be accurately described. Therefore, the conceivable complete replacement of computer simulations may have come to an end, and the theory dependence of high-energy physics experiments through computer simulations may be entering a new phase.

Dual-Pass Feature-Fused SSD Model for Detecting Multi-Scale Vehicles on the Construction Site

When detecting equipment on a construction site the objects of detection could have very different scale relative to the image on which they are located. For better detection and bounding box visualization of small objects, a Feature-Fused modification of the SSD detector can be used. Together with the use of overlapping image slicing on the inference, this model copes well with the detection of small objects. However, excessive manual adjustment of the slicing parameters for better detection of small objects can both generally worsen detection on scenes different from those on which the model was adjusted, and lead to significant losses in the detection of large objects and problems with their bound-ing box visualization. Therefore, to achieve the best quality, the image slicing parameters should be automatically selected by the model depending on the characteristic scales of objects in the image. The article presents a dual-pass version of Feature-Fused SSD for automatic determination of image slicing parameters. To determine the characteristic sizes of detected objects on the first pass, a fast truncated version of the detector is used. On the second pass the final object detection is carried out with slicing parameters selected after the first one. Depending on the complexity of the task being solved, the detector demonstrates a quality of 0.82 - 0.92 according to the mAP (mean Average Precision) metric.

Mechanics of polymers obtained by layered photopolymerization

The photopolymerization process is a high-precision and efficient technology to obtain solid polymers starting from a photosensible liquid resin made of monomer units. This technology is widely used in Additive Manufacturing (AM) to produce objects whose size can fall within the range 10−5−100 m. The outstanding potentialities of this technology can be harnessed to tailor the structure of a polymeric material by controlling how the light-induced polymerization process is performed, typically by operating on subsequent layers forming the final object. The present research illustrates the chemical-physics phenomena involved in the photopolymerization and presents a multi-physics framework and the related governing equations. Its implementation within a computational framework is developed and several simulations demonstrating the influence of the AM printing setup on the final microstructure of the obtained polymer are presented. It is shown that photopolymerization-based AM technology allows us to finely tune the mechanical properties distribution within the material domain, enabling the material's architecture to be tailored to the application of interest.

Making meaning material: A textile politics of autistic interests

Disability offers important considerations for participatory crafting projects that seek to foreground diverse forms of knowledge-making. As a case study in ‘disability craft’, this ethnographic knitting project uses collaborative knitting pattern design to explore autistic adults’ interests in the context of the autistic self-advocacy movement in the United States. Colloquially known as ‘special interests’, these interests challenge ableist stereotypes about what it means to be autistic. Crucially, this project was informed by the autistic author’s own special interest of knitting, making special interests both the object and method of research. Knitting is used to explore what counts as meaningful knowledge and who can create it. Through themes of identity, well-being and communication, special interests are shown to be intensely meaningful. The final knitted objects from this project materialize knowledge about how autistic people navigate the world and show how craft can support autistic activism against epistemic injustice.

Quasar Island – three new z ∼ 6 quasars, including a lensed candidate, identified with contrastive learning

ABSTRACT Of the hundreds of z ≳ 6 quasars discovered to date, only one is known to be gravitationally lensed, despite the high lensing optical depth expected at z ≳ 6. High-redshift quasars are typically identified in large-scale surveys by applying strict photometric selection criteria, in particular by imposing non-detections in bands blueward of the Lyman-α line. Such procedures by design prohibit the discovery of lensed quasars, as the lensing foreground galaxy would contaminate the photometry of the quasar. We present a novel quasar selection methodology, applying contrastive learning (an unsupervised machine learning technique) to Dark Energy Survey imaging data. We describe the use of this technique to train a neural network which isolates an ‘island’ of 11 sources, of which seven are known z ∼ 6 quasars. Of the remaining four, three are newly discovered quasars (J0109−5424, z = 6.07; J0122−4609, z = 5.99; J0603−3923, z = 5.94), as confirmed by follow-up and archival spectroscopy, implying a 91 per cent efficiency for our novel selection method; the final object on the island is a brown dwarf. In one case (J0109−5424), emission below the Lyman limit unambiguously indicates the presence of a foreground source, though high-resolution optical/near-infrared imaging is still needed to confirm the quasar’s lensed (multiply imaged) nature. Detection in the g band has led this quasar to escape selection by traditional colour cuts. Our findings demonstrate that machine learning techniques can thus play a key role in unveiling populations of quasars missed by traditional methods.

Global vision object detection using an improved Gaussian Mixture model based on contour.

Object detection plays an important role in the field of computer vision. The purpose of object detection is to identify the objects of interest in the image and determine their categories and positions. Object detection has many important applications in various fields. This article addresses the problems of unclear foreground contour in moving object detection and excessive noise points in the global vision, proposing an improved Gaussian mixture model for feature fusion. First, the RGB image was converted into the HSV space, and a mixed Gaussian background model was established. Next, the object area was obtained through background subtraction, residual interference in the foreground was removed using the median filtering method, and morphological processing was performed. Then, an improved Canny algorithm using an automatic threshold from the Otsu method was used to extract the overall object contour. Finally, feature fusion of edge contours and the foreground area was performed to obtain the final object contour. The experimental results show that this method improves the accuracy of the object contour and reduces noise in the object.

Threshold segmentation based on information fusion for object shadow detection in remote sensing images

In the shadow detection task, the shadow model is usually consistent with the approximate contour of ontology semantics, it is difficult to extract the features of land covered objects or ground pixels, and easy to be confused into foreground objects in gray scale. Therefore, we present to formulate and apply one new threshold segmentation method based on information fusion for object shadow detection in remote sensing images. Firstly, object shadow pixels are screened using intensity and chromaticity information in HSI color space. Secondly, the remote sensing image is carried out by principal component analysis (PCA) to obtain the first principal component. A new shadow index is constructed using the results obtained from HSI and the first principal component. Thirdly, based on the results of the above two information fusion, a threshold segmentation model is established using the improved threshold segmentation algorithm between the maximum and the minimum threshold segmentation algorithm, so as to obtain the final object shadow detection results. Finally, affluent experiments are conducted on the datasets collected from Google Earth. The results show that the proposed object shadow detection algorithm in remote sensing images can achieve better segmentation and detection (more than 95%) effect compared with state-of-the-art methods.

Boosting or inhibiting - how semantic-pragmatic and syntactic cues affect prosodic prominence relations in German.

In this exploratory study, we investigate the influence of several semantic-pragmatic and syntactic factors on prosodic prominence production in German, namely referential and lexical newness/givenness, grammatical role, and position of a referential target word within a sentence. Especially in terms of the probabilistic distribution of accent status (nuclear, prenuclear, deaccentuation) we find evidence for an additive influence of the discourse-related and syntactic cues, with lexical newness and initial sentence position showing the strongest boosting effects on a target word's prosodic prominence. The relative strength of the initial position is found in nearly all prosodic factors investigated, both discrete (such as the choice of accent type) and gradient (e.g., scaling of the Tonal Center of Gravity and intensity). Nevertheless, the differentiation of prominence relations is information-structurally less important in the beginning of an utterance than near the end: The prominence of the final object relative to the surrounding elements, especially the verbal component, is decisive for the interpretation of the sentence. Thus, it seems that a speaker adjusts locally important prominence relations (object vs. verb in sentence-final position) in addition to a more global, rhythmically determined distribution of prosodic prominences across an utterance.

Integrated CAD/CAM Approach for Experimental Stands in Hydrogen Explosion Analysis

Hydrogen is considered a promising clean energy carrier gas for the future and is used in fuel cell systems to generate electricity without producing greenhouse gas emissions. However, the safe storage and handling of hydrogen are essential for the widespread adoption of hydrogen as an alternative energy source. Research into hydrogen explosions is aimed at developing safe storage methods, safety guidelines, and accident prevention measures - crucial objectives for the sustainable growth of the hydrogen economy. Fundamental research into fast hydrogen combustion requires specially designed and built experimental stands to ensure both safe operation and recording of explosion parameters. More than a simple combustion chamber, often the geometry of the stands must contain elements to increase the complexity of the explosion process. In this respect, this paper deals with the possibilities of designing and 3D printing obstacles that can be placed in the path of hydrogen explosions on research stands to modify the cross-section, direct the pressure waves, turbulence the fluid movements, or even suppress the flame front. By combining the computer-aided modeling and design facilities offered by the Autodesk Fusion 360 software, digitized models are converted into 3D objects by the process of dividing them into thin horizontal layers (slices) and transmitting the information thus generated to the 3D printer, using Ultimaker Cura. Here, through the process of material deposition and fusion (FDM), the thermoplastic material is extruded, deposited layer by layer, rapidly cooling, and solidifying to form the final object.

Reducing Uncertainty Using Placement and Regrasp Planning on a Triangular Corner Fixture

This paper presented a regrasp planning method to eliminate grasp uncertainty while considering the geometric constraints of a fixture. The method automatically finds the Stable Placement Poses (SPPs) of an object on a Triangular Corner Fixture (TCF), elevates the object from its SPPs to dropping poses and finds the Deterministic Dropping Poses (DDPs), builds regrasp graphs by using the SPP-DDP pairs and their associated grasp configurations, and searches the graph to find regrasp motion sequences for precise assembly. Since the SPPs and their associated regrasps are constrained by the TCF’s geometry and have high precision, the final object poses regrasped via it has low uncertainty and can be directly used for assembly by position control. In the experimental section, we study the performance of analytical and learning-based methods for estimating the DDPs of different objects and quantitatively examine the proposed method’s ability to suppress uncertainty using assembly tasks like peg-in-hole insertion and sheathing tubes, aligning holes, mounting bearing housings, etc. The results demonstrate the method’s robustness and efficacy. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In production lines, robots interact with peripheral devices to improve efficiency and reduce uncertainty. In this work, we focus on a particular peripheral device – a Triangular Corner Fixture (TCF) made by three inclined and mutually perpendicular plates. We study using the TCF to improve manipulation precision. The inclined plates of the TCF form a gravity bucket that holds dropped objects in stable states under gravity. In a real scenario, a robot picks up an object and releases it above the TCF. The released object will reach a stable state on the TCF. Then, the robot regrasps and moves the stabilized object to the target pose with reduced uncertainty. Using the method proposed in this paper, a robot can automatically finish the above procedure by finding all the object’s stable states in the TCF, planning grasp configurations, invalidating infeasible states and grasps, building regrasp graphs and searching the graph to find a regrasp motion sequence that moves the object to a goal pose with high precision for assembly. In industrial applications, the proposed method has the potential to improve the flexibility of robotic systems for high-precision tasks. In the research fields, it may promote the research on sensorless manipulation and extrinsic manipulation, and push forward the studies in robotic regrasp.

Cerrahi Alet İmalatının 3 Boyutlu Yazıcı ile Üretim Özelliklerinin Araştırılması

The process of 3D printing begins with creating a digital 3D model of the object to be printed using CAD software. This model is then sliced into thin cross-sectional layers, which are used as a guide for the 3D printer to deposit material layer by layer. The printer follows the instructions from the digital model and adds material, typically plastic, metal, or composite, to build up the final 3D object. One of the key advantages of 3D printing is its ability to produce complex geometries that are difficult or impossible to achieve with traditional manufacturing methods. This allows for the creation of lightweight and optimized designs, reducing material waste and improving the overall performance of the finished product.&#x0D; This study investigates the feasibility of SLA technology (Stereolithography) as a production method as a practical alternative for surgical instrument manufacturing. Although there are many studies on obtaining accurate prints with SLA 3D device, research on the production of surgical instruments is insufficient. For this purpose, an experimental study was conducted using an SLA type 3D printer, examining the hardware and software components in terms of print quality. First , the same size prints were taken of the tools whose geometries were scanned with a 3D scanner.&#x0D; The purpose of this article is to conduct detailed research on the feasibility of lower-cost, 3D printing technology in surgical instrument manufacturing. An application was made and shared regarding the use of printed tools in artificial leather working sets.