Intricate Shapes Research Articles

High precision detection of artificial defects in additively manufactured Ti6Al4V alloy via laser ultrasonic testing

Laser powder bed fusion (LPBF) is a cutting-edge metal additive manufacturing technology with promising applications in aerospace, biomedicine, and industrial automation. Its ability to fabricate intricate geometric shapes, achieve high surface precision, and deliver comprehensive results renders it highly advantageous. In LPBF process, several factors such as the interference of laser processing fluctuations, rapid cooling rate, and environmental gas, contribute to the initiation and expansion of defects including pores and cracks, limiting the application of LPBF components. Laser ultrasonic testing (LUT) is a promising non-destructive evaluation method that can accurately detected defects in LPBF manufactured components by non-contact generation and detection. In this paper, a multi-physical field LUT model for the pores and cracks in LPBF additively manufactured Ti6Al4V alloy is proposed. The interaction of ultrasound with defects produces defect reflection echo wave, diffracted wave, and transmission wave. The transmission and diffraction phenomena occur in the longitudinal sound waves at defects. Furthermore, the echo wave reflected at crack exhibited enhanced prominence and an irregular shape. Clear differences were observed in the effects of laser ultrasonic detection on different defect types, pore depths and sizes. Finally, the LUT technology achieved the detection of 90 μm pore defects in LPBF manufactured Ti6Al4V alloy.

Experimental and Numerical Stress-Strain Analyses of Conventional and Intricate Shapes of LCS (1008-AISI) Sheet Metal Under Deep Drawing Operation

Experimental and Numerical Stress-Strain Analyses of Conventional and Intricate Shapes of LCS (1008-AISI) Sheet Metal Under Deep Drawing Operation

Enhancing Turbine Blade Manufacturing through MEMS-Based Milling Monitoring

Manufacturing steam turbine blades with intricate shapes poses a substantial challenge. These blades significantly impact turbine efficiency, reliability, and productivity. Their precise formation through milling processes demands vigilant monitoring, especially concerning the cutting tool’s condition, which is responsible for shaping the blade profiles and ensuring high-quality outcomes. Direct tool monitoring, however, disrupts productivity, prompting the need for an indirect Tool Condition Monitoring (TCM) system. Such a system becomes essential for detecting tool wear and damage early, ensuring dimensional accuracy and surface smoothness. This experiment monitors tool conditions by analyzing vibrations generated by an endmill cutter while machining martensitic stainless steel (MSS) AISI 420. The setup employs a cost-effective MEMS vibration sensor, the ADXL 345, and Raspberry Pi for real-time online TCM functionality as the signal processor. This study delves into the sensor’s ability to capture vibrations representing actual tool conditions, focusing on the affordability and effectiveness of MEMS-based monitoring. The successful real-time implementation of MEMS-based monitoring could be an easily accessible gateway for manufacturing industries to embrace cloud-based monitoring systems, aligning with current technological trends. This research aims to underscore the viability and potential adoption of affordable MEMS sensors for comprehensive tool condition monitoring, offering a seamless transition toward cloud-integrated monitoring systems for manufacturing industries.

Building a New Platform for Significantly Improving Performance of Hartree-Fock and CCSD(T) Correlation Energy Based on Two-Point Complete Basis Set Extrapolation Schemes.

The leading cause of high expense in gold standard coupled cluster theory is that calculations of electronic energies converge exceedingly slowly with an increased basis set size. Extrapolation principally allows for achieving higher-quality outcomes at reduced costs. Numerous extrapolation formulas have been developed, with attempts to predict energies up to the complete basis set limit. Unfortunately, since the intricate shape of the function hinges on the molecular properties with the highest angular momentum of the basis set, the accuracy of the extrapolated energies highly depends on the fitted empirical parameters, which rely on the quality of the data sets for fitting. In this work, to overcome the extrapolation deficiency caused by the very limited data sets and smaller basis sets in the early stages, we constructed a new benchmark platform that includes a broader data set of 183 species (containing open-shell, closed-shell, ionic, and neutral species) and a larger basis set up to aug-cc-pV6Z. The newly optimized parameters can significantly improve the energy-predictive abilities of ten published formulas. Notably, all ten formulas perform quite similarly under the new platform with the reoptimized parameters. Finally, we built an online calculator for researchers to use for these extrapolation schemes. Our work would reignite the interest and applications of the underestimated formulas.

Fast generation of parametric neutronic models for stellarators. Coupling HeliasGeom and GEOUNED

The operation of the Wendelstein 7-X has highlighted stellarators as potential fusion power reactors. As a consequence, the pre-conceptual design of the next HELIAS stellarator has started in the framework of EUROfusion. During these first stages, it is essential to assess the feasibility of various stellarator concepts. It is important to emphasize that these concepts can vary significantly in terms of their overall shape and allocation of space for each component, including the vacuum vessel, breeding blankets, among others.Although pre-conceptual neutronic analysis can be conducted using straightforward yet representative neutronic models, such as simplified multilayer models with minimal geometric details, the intricate shape of stellarators presents challenges to produce them quickly. This issue prevents the effective parametric optimization of the concepts, creating a bottleneck in the design of HELIAS.In order to overcome this problem, we are developing a computational workflow for the fast production of parametric stellarator neutronic MCNP models. This work presents the current status of the scheme composed of two steps: HeliasGeom for the production of parametric multilayer CAD models, and GEOUNED for the translation of these models to MCNP CSG neutronic models.

Resolving the long-standing discrepancy in Fe3Al ordering mobilities: A synergistic experimental and phase-field study

This study addressed the long-standing challenge of determining the growth kinetic of Antiphase Domains (APDs) with D03-ordered structure in Fe3Al by the combination of phase-field (PF) simulations and transmission electron microscopy (TEM) observation. The “shape coefficient” which correlates the growth rate of APD with 3-dimensional intricate shape and the shrinking rate of 2-dimensional circular antiphase boundaries (APBs) in thin film was assessed through a comparative analysis of PF simulations involving the shrinking of 2D circular APBs and the growth of 3D APDs emerged from a disordered state. Simultaneously, the increase rate in APD size in bulk samples after heat treatment was measured using TEM. By incorporating the calculated shape coefficient and experimental data on APD growth rates, we successfully derived accurate values of mobility for forming D03 type ordered structure. The ordering mobilities evaluated by this approach align more closely with those obtained through traditional X-ray powder diffraction of order-order relaxation rather than those obtained through in-situ TEM observation. This finding lays the foundation for optimizing heat treatment conditions to regulate APD structure and enhance the superelasticity of Fe3Al primarily due to the interaction between dislocation and APBs. This methodology can be extended to estimate the ordering mobility of other intermetallic.

Suitability of novel 3D-printed and coated vessels for water calorimetry.

In water calorimetry, absolute dose to water is determined by measuring radiation-induced temperature rises. In conventional water calorimeters, temperature detectors are housed in handmade glass vessels that are filled with high-purity water, thus mitigating radiation-induced exo/endothermic chemical reactions of impurities that would otherwise introduce additional heat gain/loss, known as heat defect. Being hand-crafted, these glass vessels may suffer from imperfections, have shape and design constraints, are often backordered, and can be prohibitively expensive. The purpose of this work is to determine suitability of 3D-printed plastic vessels that are further coated for use in water calorimetry applications, and to study their stability and characterize their associated heat defect correction factor ( . This novel vessel production technique would allow for cost-effective rapid construction of vessels that can be produced with high accuracy and designs that are simply not practical with current glass vessel construction techniques. This in turn enables water calorimetry applications in many novel radiation delivery modalities, which may include spherical vessels in GammaKnife ICON water calorimetry as an example. Eight vessels were 3D-printed using Accura ClearVue in an SLA 3D-printer. Two vessels were coated with Parylene C and four were coated with Parylene N. The water calorimetry preparation procedures followed for these vessels was identical to that of our traditional glass-vessels (i.e., same cleaning procedures, same high purity water, and same saturation procedures with high purity hydrogen gas). The performance of each vessel was characterized using our in-house built water calorimeter in an Elekta Versa using both 6MV flattening filter-free (FFF) and 18MV beams. The stability of the coating as function of time and accumulated dose was evaluated through repeated measurements. of each vessel was determined through cross-comparisons against an Exradin A1SL ionization chamber with direction calibration link to Canada's primary standard laboratory. of the two uncoated vessels differed by 2.8% under a 6MV FFF beam. Vessels coated with Parylenes resulted in a stable and reproducible heat defect for both energies. An overall of 1.001±0.010 and 1.005±0.010 were obtained for Parylene N and Parylene C coated vessels respectively. All Parylene coated vessels showed agreement, within the established uncertainties, to the zero-heat defect observed in a hydrogen-saturated glass vessel system. An additional long-term study (17 days) of a Parylene N vessel showed no change in response with accumulated dose and time. Electron microscopy images of a Parylene N coated vessel showed a uniform intact coating after repeated irradiations. An uncoated 3D-printed vessel is not viable for water calorimetry because it exhibits an unstable vessel-dependent heat defect. However, applying a Parylene coating stabilizes the heat defect, suggesting that coated 3D-printed vessels may be suitable for use in water calorimetry. This method facilitates the creation of intricate vessel shapes, which can be efficiently manufactured using 3D printing.

Aerodynamic Analysis of Deorbit Drag Sail for CubeSat Using DSMC Method

Reducing space debris is a critical challenge in current space exploration. This study focuses on designing a drag sail for CubeSat models and examining their aerodynamic properties using the direct simulation Monte Carlo (DSMC) method. The analysis encompasses the aerodynamic performance of intricate three-dimensional shapes with varying sail dimensions at orbital altitudes of 125 km, 185 km, 300 km, and 450 km. Additionally, free molecular flow (FMF) theory is applied and compared with the DSMC findings for both a flat-plate model and the CubeSat. The results reveal that FMF accurately predicts the drag coefficient at altitudes of 185 km and above, while significant discrepancies occur at lower altitudes due to increased inter-molecular collisions. This study also suggests that the drag sail substantially enhances the CubeSat’s drag force, which effectively reduces its deorbiting time.

Experimental Investigations of a Springback in Hydromechanical Deep Drawing of Low Carbon Steel 1008 AISI

Hydro mechanical deep drawing (HMDD) is an emerging technology that reduces the number of sheet-forming stages and increases the production of advanced lightweight materials with intricate shapes. This study aims to verify the springback in HMDD and the effect of holding time and fluid pressure on this phenomenon (springback). The springback was measured using a new method by matching the projector p-300 axes with the cylindrical cup wall. The difference between the wall and the axis represents the springback value. Using a blank of low carbon steel (1008-AISI) with 80 mm of diameter and a thickness of 0.7 mm. The effect of holding time (1, 2, and 3 minutes) and the fluid pressure (0.6, 0.8, and 1 N/mm2) was studied. The used hydraulic oil was (code number 3803). The springback value of a cylindrical cup was calculated using the profile projector P-300. The result showed that the springback phenomenon was presented in the hydromechanical deep-drawing process but at a lower rate than the traditional deep-drawing process. The holding time and fluid pressure significantly affected the springback value. The springback decreased with increasing the fluid pressure and holding time.

Rock Layer Classification and Identification in Ground-Penetrating Radar via Machine Learning

Ground-penetrating radar (GPR) faces complex challenges in identifying underground rock formations and lithological structures. The diversity, intricate shapes, and electromagnetic properties of subsurface rock formations make their accurate detection difficult. Additionally, the heterogeneity of subsurface media, signal scattering, and non-linear propagation effects contribute to the complexity of signal interpretation. To address these challenges, this study fully considers the unique advantages of convolutional neural networks (CNNs) in accurately identifying underground rock formations and lithological structures, particularly their powerful feature extraction capabilities. Deep learning models possess the ability to automatically extract complex signal features from radar data, while also demonstrating excellent generalization performance, enabling them to handle data from various geological conditions. Moreover, deep learning can efficiently process large-scale data, thereby improving the accuracy and efficiency of identification. In our research, we utilized deep neural networks to process GPR signals, using radar images as inputs and generating structure-related information associated with rock formations and lithological structures as outputs. Through training and learning, we successfully established an effective mapping relationship between radar images and lithological label signals. The results from synthetic data indicate a rock block identification success rate exceeding 88%, with a satisfactory continuity identification of lithological structures. Transferring the network to measured data, the trained model exhibits excellent performance in predicting data collected from the field, further enhancing the geological interpretation and analysis. Therefore, through the results obtained from synthetic and measured data, we can demonstrate the effectiveness and feasibility of this research method.

Metallic surface defect recognition network based on global feature aggregation and dual context decoupled head

Surface defect detection is a crucial inspection phase in ensuring industrial product quality and reliability, particularly in the context of metallic manufacturing components. While existing deep learning-based approaches have demonstrated some effectiveness, the design of certain network structures still lacks consideration for industrial scenarios. This paper proposed a complex metallic surface defect detection neural network which incorporates two innovatively designed modules along with other targeted enhancements. Firstly, the spatial pyramid pooling (SPP) structure is redesigned for richer global-local information fusion. A global feature fusion and redistribution module (GFAR) is designed to fully leverage feature information across different scales. With the combination of a novel global positional attention mechanism (GPAM), GFAR is capable of distinguishing defects from complex backgrounds. Moreover, the asymmetric convolution blocks (ACB) are employed to process the final features, enhancing the representational capacity for intricate defect shapes. Subsequently, the detection head is decoupled in both spatial and task domains to addresses the problems posed by inconsistent focus between classification and regression tasks. Finally, the reassignment of loss functions is undertaken for each respective task to enhance their alignment with the specific requirements of the defect detection task. Extensive ablation experiments are conducted on two steel surface defect datasets, NEU-DET and GC10-DET, to demonstrate the effectiveness of each module. The defects detection accuracy of our method respectively reaches 80.9 % and 84.8 % for the two datasets, which outperforms the majority of state-of-the-art detection neural networks.

Twisting: An Innovative Combination of Coil and Slab Building Techniques in Ceramic Art

This work seeks to create decorative ceramics wares which would be self-decorating to reduce the time of attention given to the surface of ceramics wares by incorporating twisting and weaving techniques into the production of the wares by drawing my inspiration from the climbing plant. Two hand forming techniques namely slab making and coiling were successfully combined to create twisted and woven wares. It also seeks to demonstrate how clay can be twisted and woven into intricate shapes for outdoor decorations. These twisted and woven wares reduced the tendency of cutting and incising as an afterthought finishing but forms an integral part of the work. Production of the wares were based on the basic units’ formation as the building blocks which were pre-formed and allowed to get leather hard and joined together in the building process. It looked at the possibility of providing an alternative to the conventional way of building ceramic wares, particularly the solidness in their form; so as to break the monotony of sold form production and present forms with surfaces which are with self-decorative appeal. There are about seven forming techniques I have experienced in ceramic art, and still counting, namely Pinching, Slab making, Coiling, Wheel Throwing, Slip Casting, Jiggering, Jollying, Ram Press, Modeling and Sculpting. The above basic techniques allows both learners and master ceramic artist to explore hand building creativity and come out with fascinating ceramics or pottery wares. My technique of production is what I term as <i>twisting </i>and my style as<i> twistism,</i> which I developed from the combination of two hand formations methods, namely, slab forming and coil making. As happened in painting, where a group of painters lead by Pablo Picasso in 1907, pronounced monotony on the line and style of painting at that time, in the late twentieth century and brought about “<i>cubism</i>”, so do I feel and seeks to digress from tradition to bring about forming variation, which I termed as “<i>twistism</i>” in ceramics, whereas the attention will be on how to manipulate the clay by twisting strips of slabs and creating non solid forms out of that as well as weaving coils to produce ceramic wares. I therefore put forward for the acceptance and inclusion of this technique, twisting as a basic forming technique in addition to pinching, slab making and coiling.

Machinability of different cutting tool materials for electric discharge machining: A review and future prospects

Electric Discharge Machining (EDM) is essential for shaping and cutting tool steel. EDM’s precision in machining difficult materials and tool steel characteristics are well known. EDM efficiency requires reliable performance measurement parameters. The physical shape and mobility of the electrode tool are critical in EDM research. Layer machining is an advanced method that removes material in a sequential manner to produce intricate 3D shapes in tool steel and several other materials. The improvement in layer machining methods with precise toolpath algorithms, adaptive layer thickness management, and real-time monitoring systems is required to maximize precision and efficiency. Response surface methodology, the artificial neural network, and other techniques are necessary to optimize EDM operations and maximize performance. Many researchers experimented with electrode shapes and movement patterns to enhance the removal of material and the quality of surfaces. Investigation of complex electrode structures and innovative tool path strategies has been performed in previous studies. It was very difficult to consider various factors during the EDM operation; hence, the present review summarizes the positive outcomes of previous research. The review emphasizes optimizing pulse duration and discharge current to improve EDM efficiency. The present comprehensive review discusses research on EDM in three main areas: electrode tool geometry and motion, tool steel layer processing, and factors for measuring EDM performance. The objective of the present review is to focus on measuring material removal rates, surface roughness, tool wear, and energy usage. The present review concludes that EDM is crucial to machining tool steel and cutting tool materials. Integrating and hybrid machining technologies can improve performance, and improved optimization techniques are crucial. It also recognizes knowledge gaps and explores new frontiers in this dynamic field.

Multimodal machine learning for predicting heat transfer characteristics in micro-pin fin heat sinks

As three-dimensional integrated circuit (3D-IC) chip technology advances, thermal management has become increasingly important because of increasing heat flux from thermal stacking. Micro-pin fin-embedded cooling has emerged as a promising solution for 3D-ICs, offering better thermal and hydraulic performance than conventional microchannel heat sinks. It is also easy to integrate into existing 3D-IC structures, such as through-silicon vias between stacks. The utilization of two-phase flow in micro-pin fins further enhances temperature uniformity and improves the heat transfer coefficient by leveraging latent heat. Nevertheless, predicting thermal performance in micro-pin fin heat sinks under boiling conditions remains challenging owing to intricate geometric shapes and diverse operating conditions. The present lack of correlation or theoretical models poses a significant obstacle. To address this problem, our study employed a Multimodal machine-learning (ML) approach, combining image data capturing boiling patterns of two-phase flow and information about geometric shape and operating conditions, to predict heat transfer characteristics in micro-pin fin heat sinks. We utilized experimental data comprising 155 types of boiling heat transfer data with the dielectric fluid FC-72 in two micro-fin shapes directly etched on Si. Four ML algorithms (XGBoost, LightGBM, Multilayer perceptron (MLP), and Multimodal ML) were employed to predict thermal performance. The correlation coefficient analysis before learning revealed the influence of each type of measurement data on the heater surface temperature during two-phase flow. Prediction accuracy was measured using mean absolute percent error (MAPE), and the results were compared in terms of maximum and average temperature depending on the characteristics of each ML algorithm. Overall, the Multimodal approach demonstrated superior capability in predicting temperature distributions with spatial details, surpassing conventional decision-tree algorithms and MLP in performance. When trained with boiling images, the Multimodal ML model achieved remarkable precision, evidenced by a MAPE of 1.81% for the maximum temperature and 0.84% for the average temperature, highlighting its exceptional accuracy in mapping the heated surface temperature profile. By contrast, the traditional MLP model, which lacked training on boiling images, showed diminished accuracy, with a MAPE of 2.54% for the maximum temperature and 1.77% for the average temperature, indicating a comparative shortfall against the Multimodal model results.

3D printing of a photo-curable hydrogel to engineer mechanically robust porous structure for ion capture or sustained potassium ferrate(VI) release for water treatment

Contaminated water and scarcity of clean water are becoming progressively serious environmental concerns. Removal of hazardous pollutants from wastewater is vital and urgently needed attention for the protection of pure water resources. To tackle the aforementioned challenges, macroporous structures have great potential to be used in the treatment of heavy metal ions to improve the adsorption efficiency and sustainability of wastewater treatment methodologies. In this context, additive manufacturing technologies have gained considerable attention because of their ability to construct intricate macroporous shapes with multifunctionalities using diverse materials. Here, we used a photocrosslinking graft copolymerization of polyvinyl alcohol/acrylic acid-based ink using UV irradiation in the presence of Norrish type II photosensitizer through a hot-melt extrusion-type 3D printer to improve the printing quality of crosslinked ink. The photocured inks offered strong shear-thinning behavior that allowed layer-by-layer deposition to generate well-defined 3D structures, while having sufficiently high viscoelasticity to retain the shape after printing process. With an adequate viscosity at the higher extrusion shearing forces, the 3D printed structures could create multifaceted self-supporting scaffolds with an internal lattice structure that possesses high level of porosity. The hierarchically porous structure of 3D objects showed a recoverable structure yet robust matrix, offering more specific surface area, capable of effectively removing heavy metal ions from water with fast-responsiveness and a high capacity. The ferrate(VI)-contained 3D capsule-like object was also detected to be efficient regarding chemical oxygen demand reduction and decolorization of real wastewater. Such 3D-printed hierarchical macroporous objects can offer great prospects in the treatment of water and wastewater purification applications.

A Three-branch Jointed Feature and Topology Decoder guided by game-theoretic interactions for temporomandibular joint segmentation

Segmentation of the temporomandibular joint (TMJ) disc and condyle from magnetic resonance imaging (MRI) is a crucial task in TMJ internal derangement research. The automatic segmentation of the disc structure presents challenges due to its intricate and variable shapes, low contrast, and unclear boundaries. Existing TMJ segmentation methods often overlook spatial and channel information in features and neglect overall topological considerations, with few studies exploring the interaction between segmentation and topology preservation. To address these challenges, we propose a Three-Branch Jointed Feature and Topology Decoder (TFTD) for the segmentation of TMJ disc and condyle in MRI. This structure effectively preserves the topological information of the disc structure and enhances features. We introduce a cross-dimensional spatial and channel attention mechanism (SCIA) to enhance features. This mechanism captures spatial, channel, and cross-dimensional information of the decoded features, leading to improved segmentation performance. Moreover, we explore the interaction between topology preservation and segmentation from the perspective of game theory. Based on this interaction, we design the Joint Loss Function (JLF) to fully leverage the features of segmentation, topology preservation, and joint interaction branches. Results on the TMJ MRI dataset demonstrate the superior performance of our TFTD compared to existing methods.

Design and manufacturing process of pneumatic soft gripper for additive manufacturing

The soft gripper, constructed from elastic materials, serves as a crucial automated device component, aiming to minimize object damage. The central focus of utilizing soft grippers lies in their shape design, enabling effective handling of objects with low rigidity and diverse forms. This study delves into shape design research, employing a pneumatic pressure-responsive, tube-like soft gripper fabricated through injection molding. During the design phase, a comprehensive survey of soft gripper research trends was conducted, leading to two distinct configurations: Type1, drawing from established shapes, and Type2, a novel design ensuring bending angle stability. The primary dimensions of Soft Gripper Type 1 and Type 2 are as follows: lengths of 78.11 mm and 74.96 mm, heights of 18.69 mm and 20.0 mm, respectively, with a uniform thickness of 0.6 mm. The prediction of bending angles was accomplished through employment of the Abaqus finite element analysis program. In both simulation and experimentation, pneumatic pressures of 100, 200, and 300 kPa were applied to compare the bending angles of two types of soft grippers. While conventional injection molding remains a prevalent manufacturing technique, it confronts challenges like demolding and undercuts. Thus, for intricate shapes and monolithic soft grippers, layer-by-layer additive manufacturing emerges as a superior choice over injection molding. The implementation of this approach holds the potential for streamlined soft gripper design processes, potentially yielding reductions in both time and cost. The significance of these findings lies in advancing the capabilities of soft grippers, propelling them toward efficient and cost-effective applications in various fields.

A superplastic micro-extrusion technology to develop engineered magnesium micro-components

Superplastic micro-extrusion (SME) is a state-of-the-art micro-manufacturing technology that produces intricate shape miniaturized components. Magnesium and its alloys (Mg alloys) are potential contenders for the miniaturization industries due to their lightweight and biocompatibility properties. SME is a promising solution to overcome the room temperature ductility/formability concerns in Mg alloys. Therefore, for the first time, the current work proposes superplastic micro-extrusion technology to manufacture high-performance Mg-RE alloy (QE22) based micro-components. The desirable ultrafine grain (UFG) microstructure suitable for superplasticity is synthesized/engineered and further subjected to two modes of superplastic micro-deformation: (1) micro-forward extrusion (MFE) and (2) micro-backward extrusion (MBE) in pre-optimized extrusion conditions. The test results revealed a uniform hardness profile throughout the developed miniaturized micro-pin and micro-cup. Such homogeneous mechanical properties of the developed micro-components can be attributed to the microstructural engineering and high thermal stability of the developed material especially engineered for the current superplastic micro-extrusion processes. Microstructural analysis in all the conditions linked this distinctive characteristic to the activation of grain boundary sliding (GBS) and its accommodative micro-mechanisms. The physics and fundamental science of superplastic microforming in both micro-forward and micro-backward extrusion domain is established for the engineered Mg-RE alloy. Such investigations on understanding the fundamental mechanisms can address the existing challenges in micro-manufacturing industries.

Influence of Motion Mechanism Change in 3D Printers on the Quality of Printed Models

The objective of this study is to compare the quality of 3D printed models manufactured using the Fused Filament Fabrication (FFF) method. The samples were fabricated using a robotic arm with 4 axes and conventional 3D printers. The focus of this experiment lies in assessing the influence of the devices’ structural design on the resulting quality of the 3D printed models. Additionally, the study aims to identify the strengths and limitations of each device and define their respective applicability. The 3D model designed for this investigation comprises intricate geometrical shapes specifically chosen to evaluate the precision and repeatability of layer deposition while establishing geometric tolerances and determining shape deviations. The samples were 3D printed under identical printing conditions and parameters, and subsequently, these produced samples will undergo 3D digitization through an optical scanner, namely ATOS II Triple Scan. The obtained data will then be subjected to a comparative analysis utilizing GOM Inspect software to determine the geometric tolerances. The findings from this analysis will be critically evaluated and serve as a basis for informing and guiding future research endeavors.

Fabrication of High Entropy Alloy by Powder Metallurgy Process

Recently, High entropy alloys have attracted a lot of interest because of their unique properties. which include outstanding strength, remarkable corrosion resistance, and consistent thermal stability. Powder metallurgy is one of the production procedures commonly used to manufacture these alloys. Metal powders are compacted and sintered to create a cohesive material in the production process known as powder metallurgy. The capacity to produce a fine-grained microstructure and a homogeneous distribution of various elements are two benefits of this technology for the synthesis of high entropy alloys. Moreover, powder metallurgy makes it possible to precisely combine various alloying constituents, which can improve the qualities of high entropy alloys even more. Powder metallurgy allows for the manufacture of intricate shapes and dimensions, rendering it well-suited to a range of uses. With the use of powder metallurgy, high entropy alloys can be fabricated with improved mechanical properties and enhanced performance.