Manufacturing Of Metal Parts Research Articles

Assessing the Effect of Toothpastes on Enamel Surface Roughness Using a Custom-Designed and Fabricated Toothbrush Simulator Device for Evaluation.

The primary objective of this study is to emphasize the importance of maintaining optimal oral health through regular toothbrushing practices. To achieve this objective, a custom-designed electromechanical toothbrush simulator device was developed. This innovative tool enables researchers to investigate the impact of abrasive-based whitening toothpastes on enamel surface roughness compared to brushing without toothpaste. The device design is composed of multiple systems, including mechanical, motorization, and toothpaste irrigation components. The device incorporates various components, including mechanical, motorization, and toothpaste irrigation systems. Specifically, the mechanical aspect comprises fabricated metal parts, 3D printed elements, and a load cell for measuring brushing force. The motorization section integrates a microcontroller and a stepper motor, allowing for the adjustment of brushing cycles and speed. Furthermore, the toothpaste irrigation system employs a pump with adjustable speed, along with a toothpaste canister and a waste receptacle. By providing a controlled environment for evaluating the effects of different toothpaste formulations on enamel integrity, this simulator device contributes significantly to advancements in oral care research and product development.

3D printing of metal parts using a highly-filled thermoplastic filament

Purpose This study aims to develop a highly loaded filament with spherical metallic particles for fused filament fabrication (FFF) technology. The research focuses on optimizing powder loading, printing parameters and final processes, including debinding and sintering, to produce successful metal parts. Design/methodology/approach The optimal powder loading was identified by measuring mixing torque and viscosity at various temperatures. The filament was extruded, and printing parameters − particularly printing speed to ensure proper material flow − were optimized. Different filling patterns were also examined. After printing, the polymeric binder was removed and the parts were sintered to form the final metal components. Findings The optimal powder loading was determined to be 55 vol.%. The best surface quality was achieved with an optimized printing speed of 5 mm/s. Parts printed with various infill patterns were studied for differences in open, closed and total porosity, showing a strong link between porosity and infill pattern. Originality/value This comprehensive study provides new insights into manufacturing metal parts using FFF technology. It fills a gap in the literature regarding feedstock viscosity and shear rate in highly loaded metal filaments during FFF. Additionally, it uniquely examines the open, closed and total porosity of metal parts printed with different infill patterns.

Acoustic emission monitoring of a laser powder bed fusion process

Additive manufacturing (AM) metal parts offers opportunities for various industrial applications. From individual production of spare parts for unique mechanical components to prototyping of complex structures, the possibilities of production using the additive manufacturing process are manifold. One common AM technique is the Laser Powder Bed Fusion (PBF-LB/M) process, where a laser is used to selectively melt metal powder and create the parts layer wise as designed in a model. During manufacturing certain defects like pores, cracks and lack of fusion may be created in the built parts. As AM parts often have complex geometries, a postprocess non-destructive testing is difficult or even not possible. Thus, different optical monitoring techniques are applied to detect flaws during the build process with the aim to detect and repair defects right away during the manufacturing process. However, optical monitoring system require a clear view of the melt pool and quite expensive equipment. Acoustic monitoring by AE sensors attached to the built plate would be a cheaper alternative which also can be used without visual contact to the building chamber. This paper shows a first approach of an AE based process monitoring. The build plate of a PBF-LB/M machine therefore was adapted to hold four AE sensors that are then used to monitor the process. The build process itself creates AE signals caused by the melting and cooling and the mechanical application of powder. The formation of pores and cracks is expected to create additional acoustic emissions that will be distinct from the AE pattern from the printing process. This AE signals can be linked to the different layers of the printed part or in the long run to the laser position. Results from the first tests in a customized LPBF machine will be shown as well as the implementation of AE into the PBF-LB/M machine.

Directed energy deposition of Ti6Al4V wire with continuous layer varied laser power

The laser-based directed energy deposition (DED) additive manufacturing enables the efficient fabrication of metallic parts. The layer-wise performance in DED-built metallic parts is challenging to remain consistent due to the intricate heat accumulation. As such, it is of significance to control the printing quality by adjusting the process settings with layers. In this work, we used DED to achieve Ti6Al4V wire deposition with continuously layer-varied laser power. Three laser power settings were selected to investigate the variation in defects, microstructure, and mechanical properties with layers. Our findings suggested that optimizing real-time laser power is critical for achieving desirable manufacturing efficiency and resultant performance in the DED of metallic materials. This research provides valuable guidelines for parameter optimization in DED processes, aiming to enhance the production quality and performance of high-strength metallic components, particularly in sectors demanding high-performance materials.

Molten pool behaviors and printability of tungsten anti-scattering grids with extremely thin wall thickness fabricated via laser powder bed fusion

It is challenging for laser powder bed fusion (LPBF) technique to fabricate metal parts with a wall thickness below 100 μm. This work investigated the critical conditions for achieving extremely thin wall thickness in tungsten grids fabricated via LPBF. Specifically, the impact of low energy density on the printability of tungsten single tracks and grids via LPBF was comprehensively examined. A computational fluid dynamics approach was employed to develop a thermal fluid flow model for single tracks with multilayers in LPBF. The findings demonstrate that at low energy densities, single tracks exhibit four different morphologies, i.e., balling, discontinuity and winding, discontinuity but straightness, as well as continuity and straightness. The simulation model effectively elucidates the continuity of single tracks and provides insights into the governing mechanism of molten pool defects. Due to high thermal diffusion properties of tungsten, the continuity of its track relies on the connection of neighboring molten pools and is sensitive to scanning speed. The tungsten molten pools with low energy density can be categorized into shallow flows affected by surface morphology and deep flows influenced by internal voids of the powder bed. After multi-layer stacking, the track fluctuations and defects in single tracks accumulated into greater surface roughness and deteriorate thin-walled morphology. The critical conditions required for printing extremely thin walls were achieved, ensuring minimal merging of tracks between two layers by maintaining the energy density of 57J/mm3. Based on these findings, an ultra-thin-walled anti-scattering tungsten grid with a wall thickness of 86 μm and a wall roughness below 3.3 μm (Ra) was fabricated by LPBF. This work provides valuable theoretical insights and presents a viable methodology for determining the minimum energy density threshold and wall thickness essential for LPBF processing of thin-walled components.

Development of Robust Steel Alloys for Laser-Directed Energy Deposition via Analysis of Mechanical Property Sensitivities

To ensure consistent performance of additively manufactured metal parts, it is advantageous to identify alloys that are robust to process variations. This paper investigates the effect of steel alloy composition on mechanical property robustness in laser-directed energy deposition (L-DED). In situ blending of ultra-high-strength low-alloy steel (UHSLA) and pure iron powders produced 10 compositions containing 10–100 wt% UHSLA. Samples were deposited using a novel configuration that enabled rapid collection of hardness data. The Vickers hardness sensitivity of each alloy was evaluated with respect to laser power and interlayer delay time. Yield strength (YS) and ultimate tensile strength (UTS) sensitivities of five select alloys were investigated in a subsequent experiment. Microstructure analysis revealed that cooling rate-driven phase fluctuations between lath martensite and upper bainite were a key factor leading to high hardness sensitivity. By keeping the UHSLA content ≤20% or ≥70%, the microstructure transformed primarily to ferrite or martensite, respectively, which generally corresponded to improved robustness. Above 70% UHSLA, the YS sensitivity remained low while the UTS sensitivity increased. This finding, coupled with the observation of auto-tempered martensite at lower cooling rates, may suggest a strong response of the work hardening capability to auto-tempering at higher alloy contents. This work demonstrates a methodology for incorporating robust design into the development of alloys for additive manufacturing.

Nondestructive fatigue life prediction for additively manufactured metal parts through a multimodal transfer learning framework

Understanding the fatigue behavior and accurately predicting the fatigue life of laser powder bed fusion (L-PBF) parts remain a pressing challenge due to complex failure mechanisms, time-consuming tests, and limited fatigue data. This study proposes a physics-informed data-driven framework, a multimodal transfer learning (MMTL) framework, to understand process-defect-fatigue relationships in L-PBF by integrating various modalities of fatigue performance, including process parameters, XCT-inspected defects, and fatigue test conditions. It aims to leverage a pre-trained model with abundant process and defect data in the source task to predict fatigue life nondestructively with limited fatigue test data in the target task. MMTL employs a hierarchical graph convolutional network (HGCN) to classify defects in the source task by representing process parameters and defect features in graphs, thereby enhancing its interpretability. The feature embedding learned from HGCN is then transferred to fatigue life modeling in neural network layers, enabling fatigue life prediction for L-PBF parts with limited data. MMTL validation through a numerical simulation and real-case study demonstrates its effectiveness, achieving an F1-score of 0.9593 in defect classification and a mean absolute percentage log error of 0.0425 in fatigue life prediction. MMTL can be extended to other applications with multiple modalities and limited data.

Unlocking the potential of low-melting-point alloys integrated extrusion additive manufacturing: insights into mechanical behavior, energy absorption, and electrical conductivity

AbstractAdditive manufacturing is a commonly used manufacturing method in complex part fabrication, instant assemblies, part consolidation, mass customization and personalization, on-demand manufacturing, lightweight, and topological optimization due to its advantage of lower costs, flexibility to learn and use, reduced raw material wastage, digital design integration, high efficiency, environmental-friendliness. However, the current metal 3D printing, which is mainly fabricated layer by layer using laser, is expensive to manufacture metal parts. Therefore, in this paper, a low-cost high-quality metal manufacturing process called low-melting-point alloys (LMPAs) integrated extrusion additive manufacturing will be examined. This manufacturing process can fabricate complex metal structures and integrated circuits with simple fused deposition modeling, which is a cost-effective method for producing these objects. LMPAs with different melting points are used for performance comparison to find out the optimized mechanical behavior, energy absorption properties, and electrical conductivity. Our investigation into LMPAs integrated extrusion additive manufacturing has revealed significant findings. Tensile tests conducted on LMPAs with varying melting points have illuminated distinct mechanical behaviors. Notably, lower melting points contribute to increased ductility but reduced stiffness, while higher melting points result in greater stiffness but diminished ductility. These results emphasize the importance of tailoring LMPA selection based on specific application requirements. Furthermore, our examination of lattice and triply periodic minimal surface structures has demonstrated consistent energy absorption properties across different manufacturing temperatures, highlighting the adaptability and versatility of LMPAs for energy absorption applications. Additionally, our electrical conductivity assessments have shown that LMPAs with melting points of $${{47}^{\circ }C}$$ 47 ∘ C and $${{120}^{\circ }}\hbox {C}$$ 120 ∘ C exhibit higher electrical conductivity, making them suitable for applications requiring good electrical conduction properties. These findings collectively underscore the significance of LMPAs in additive manufacturing, offering valuable insights for material selection and applications in various domains.

Sub-surface thermal measurement in additive manufacturing via machine learning-enabled high-resolution fiber optic sensing

Microstructures of additively manufactured metal parts are crucial since they determine the mechanical properties. The evolution of the microstructures during layer-wise printing is complex due to continuous re-melting and reheating effects. The current approach to studying this phenomenon relies on time-consuming numerical models such as finite element analysis due to the lack of effective sub-surface temperature measurement techniques. Attributed to the miniature footprint, chirped-fiber Bragg grating, a unique type of fiber optical sensor, has great potential to achieve this goal. However, using the traditional demodulation methods, its spatial resolution is limited to the millimeter level. In addition, embedding it during laser additive manufacturing is challenging since the sensor is fragile. This paper implements a machine learning-assisted approach to demodulate the optical signal to thermal distribution and significantly improve spatial resolution to 28.8 µm from the original millimeter level. A sensor embedding technique is also developed to minimize damage to the sensor and part while ensuring close contact. The case study demonstrates the excellent performance of the proposed sensor in measuring sharp thermal gradients and fast cooling rates during the laser powder bed fusion. The developed sensor has a promising potential to study the fundamental physics of metal additive manufacturing processes.

An experimental process parameter study on the identification of defects in additively fabricated Al6061 with laser powder bed fusion

Additively fabricated metal parts using laser powder bed fusion (L-PBF) possess sophisticated morphology due to the recurrent use of laser-induced metal powder melting and solidification. The surface and 3D morphology of these parts often include defects in the form of protrusions, depressions, pores, voids, keyholes, or cracks that are known to be influenced by laser scanning paths and layer-to-layer processing. Such inconsistent part quality hampers the extensive adoption of L-PBF. Pores and cracks are detrimental to the fatigue life of the parts and components. Quantifying and controlling part defects and optimizing processing and scanning strategy parameters adaptively in real-time through in situ monitoring systems are highly desired. This study investigates the optimization of experimental process parameters (power, scan velocity, and hatch spacing) and their effects on the cracking and porosity of Al6061 alloy using machine learning techniques. Multi-objective optimization is formulated and conducted to determine the L-PBF parameters that minimize both porosity and crack densities.

Monitoring process stability in robotic wire-laser directed energy deposition based on multi-modal deep learning

Robotized wire-laser directed energy deposition (DED) is a highly anticipated technology with excellent material utilization and deposition efficiency for fabricating complex-structure metal parts. Nevertheless, the immaturity of the monitoring and control techniques for the deposition process stability are the main challenges in achieving automatic and repeatable manufacturing of metal parts. This study proposes a novel approach to monitor the process stability, i.e., the intersection point between the laser beam and the wire to the top layer distance (IPTD), in robotic wire-laser DED based on a designed multi-modal IPTD estimation model and coaxial visual sensing. The novelty is that directly extracting deposition height features from coaxial molten pool images is attempted based on deep learning. In particular, the designed multi-modal IPTD estimation model, consisting of a convolutional neural network (CNN) part and a fully connected network, combines the features of molten pool images and process parameters to estimate the IPTD state. Six model architectures and three image pixel sizes are used in the IPTD classification tasks to determine the CNN part architecture and the input image pixel size of the multi-modal deep learning model based on classification results. The regression performances of established single-modal and multi-modal IPTD estimation models are studied and discussed. Compared to other model architectures, the ResNet-18 model possesses the highest convergence rate during training and the best classification accuracy of 0.9975 on the testing dataset with the image pixel size of 180 × 105. The fitting accuracy and generalization performance of the multi-modal IPTD estimation model are marked superior to the single-modal IPTD estimation model. Validation experiments reveal the effectiveness of the proposed monitoring approach of process stability. This study will lay a solid foundation for the future control of process stability in robotic wire-laser DED.

Fatigue life prediction of rough Hastelloy X specimens fabricated using laser powder bed fusion

Additive manufacturing, especially laser powder bed fusion (L-PBF), is extensively used for fabricating metal parts with intricate geometries. However, parts produced via L-PBF suffer from varied surface roughness, which affects the fatigue properties. Accurate prediction of fatigue properties as a function of surface roughness is a critical requirement for qualifying L-PBF parts. In this work, an analytical methodology was put forth to predict the fatigue life of L-PBF components having heterogeneous surface roughness. Thirty-six Hastelloy X specimens were printed using L-PBF followed by industry-standard heat treatment procedures. Half of these specimens had as-printed gauge sections and the other half were printed as cylinders from which fatigue specimens were extracted via machining. Specimens were printed in a vertical orientation and an orientation of 30° from the vertical axis. The surface roughness of the specimens was measured using computed tomography and parameters such as the maximum valley depth were used to build an extreme value distribution. Fatigue testing was conducted at an isothermal condition of 500 °F. It was observed that the rough specimens failed much earlier than the machined specimens due to the deep valleys present on the surfaces of the former ones. The valleys behaved as notches leading to high strain localization. Based on this observation, an analytical functional relationship was formulated that treated surface valleys as notches and correlated the strain localization around these notches with fatigue life, using the Coffin-Manson-Basquin and Ramberg-Osgood equations. The functional relationship was generated with the average of the extreme value distribution. The mean life curve from the functional relationship showed a maximum difference of 2 % from the experimental mean fatigue life observations for vertically built rough specimens and 10 % for 30⁰-built rough specimens. In conclusion, the proposed analytical model successfully predicted the fatigue life of L-PBF specimens at an elevated temperature undergoing different strain loadings.

Manufacture of tunnel-shaped sheet metal parts with improved accuracy using novel toolpath strategies for single point incremental forming

Manufacture of tunnel-shaped sheet metal parts with improved accuracy using novel toolpath strategies for single point incremental forming

Enhancement of Scan Strategy for Improving Surface Characteristics of the SLMed Inconel 718 Alloy Component

The technique of selective laser melting has garnered significant interest due to its capacity to fabricate metal parts of any shape using a single printing process. In this study, a scan strategy was proposed for printing the inner structure part based on the varying performance of the Inconel 718 alloy part produced using different scan techniques. The test findings indicated that the surface quality of the printed material along the scan line exhibited significantly superior performance in comparison to that produced in a vertical direction to the scan line. Regarding the scan strategy, it is evident that the zigzag shape scan strategy exhibited superior performance in terms of the microstructure of the printed part. This is attributed to the more consistent cooling rate on each scan track. On the other hand, the square-framed scan strategy demonstrated a relatively optimal condition for the bending deformation of the printed part compared to other scan strategies. This is due to the more appropriate behavior of the molten pool during the outer edge printing process. After considering all of these criteria, a novel scan approach was created that included various scan strategies. The test findings indicated that the component produced using this novel scanning technique exhibited the combined benefits of both the square-shaped and zigzag-shaped scanning strategies.

Interpenetrating microstructure in laser powder-bed fusion parts using selective rescanning

In-situ microstructural control is desirable in additively manufactured metal parts due to limited post-processing options for net-shaped components. Here, we introduce a novel selective rescanning approach to control the local solidification conditions and the microstructure in metal parts produced by laser powder-bed fusion (LPBF). We show that the melt pool dimensions, grain size, and sub-grain cell structure can be selectively varied in three dimensions to engineer the mechanical response of LPBF parts. The lattice-based rescanning strategy enables the formation of an interpenetrating microstructure comprised of fine and coarse grains. The localized heating and cooling-induced thermal stresses increase the hardness and tensile strength of rescanned specimens. The study shows the potential of selective rescanning strategy as a promising avenue for achieving precise control of microstructure and properties in as-printed LPBF parts without subsequent processing.

Efficient solid dielectric electrochemical polishing with minimal electrolyte consumption and reusable conductive media

Electrochemical polishing finds wide application across various industries, including manufacturing, biomedicine, and semiconductors. However, this technology requires substantial water usage and results in environmental pollution due to the generation of waste electrolytes containing highly corrosive acids and heavy metal ions. Herein, we propose an electrochemical polishing method (SDEP) that employs solid dielectrics consisting of macroreticular ion exchange resin (MIER) solid particles and phosphoric acid electrolyte as an alternative to traditional liquid conductive media. Maintaining a polishing current of 0.4–0.5 A requires only an electrolyte consumption of 10–20 mL/h, leading to a more than 65% reduction in electrolyte usage compared to liquid electrochemical polishing. Meanwhile, this method can be applied to polish additively manufactured metal parts with an initial roughness Ra>10 μm. The achievable surface finish features Ra = 0.778 ± 0.078 μm, Rq = 0.960 ± 0.121 μm, and Rz = 4.175 ± 1.111 μm after 1-h polishing, with a material removal rate of 0.15–0.19 mm3/min. The improvement of Ra, Rq, and Rz are 92.3%, 92.1%, and 92.0%, respectively. With the EDS and XPS analysis, rod-shaped by-products adhering to the surface of MIER particles generated during the SDEP process are confirmed as Fe3+, PO43−, and PO3−. Furthermore, the MIER particles can be reused after cleaning, exhibiting polishing performance comparable to fresh MIER. This method reduces the use of electrolytes at the source of electropolishing, and MIER particles can be recycled, pointing towards the development of eco-friendly electrochemical polishing.

Effect of heat treatments on inhomogeneous deformation of the melt-pool structure of Al–Si alloy manufactured via laser powder bed fusion

Laser powder bed fusion (L-PBF) is an additive manufacturing technology widely applied to the manufacture of metallic parts. The L-PBF processed aluminum-silicon (Al–Si) cast-type alloy components exhibit an anisotropic tensile ductility. In this study, we investigated the influence of heat treatments on the microstructural features of an Al–12%Si binary alloy fabricated via L-PBF and the associated inhomogeneous deformation of its melt-pool structure, which contributes to the resulting anisotropic tensile ductility. The mechanical inhomogeneity of the melt-pool structure and the change induced by annealing at various temperatures (300 and 530 °C) were characterized using microscale digital-image correlation analyses of scanning electron microscopy images obtained in situ during tensile deformation and nanoindentation hardness mapping. In the specimen fabricated via L-PBF, the high strain was concentrated at the locally coarsened microstructure (the soft region) along the boundaries of the melt pools rather than at the refined solidification microstructure (the hard region) within the melt pools. The localized strain varied depending on the geometrical relation between the orientation of the melt-pool boundary and the tensile direction, thus contributing to the anisotropic tensile ductility. This tendency appeared less pronounced in the specimen annealed at 300 °C, which exhibited a slightly homogenized melt-pool structure. The reduced strain localization is associated with a reduced difference in local strength between the melt-pool boundary (soft region) and the melt-pool interior (hard region). The slight homogenization of the melt-pool structure emphasized the effect of grain morphologies in the α-Al matrix on the inhomogeneous deformation within melt pools. In the specimen annealed at 530 °C, which exhibited a homogenized α-Al/Si two-phase microstructure, the uniformly distributed Si phase would be responsible for the homogenous deformation, resulting in an isotropic tensile ductility. This study advances our understanding of the correlation between the melt-pool structure and the deformation behavior of Al–Si alloys processed using L-PBF, which provides new insights for controlling the ductility by post-processing.

Review on machine learning techniques for the assessment of the fatigue response of additively manufactured metal parts

AbstractThe present review paper addresses the increasing interest in the application of machine learning (ML) algorithms in the assessment of the fatigue response of additively manufactured (AM) metal alloys. This review aims to systematically collect, categorize, and analyze relevant research papers in this domain. The most commonly used ML algorithms are presented, discussing their specific relevance to the fatigue modeling of AM metal alloys. A detailed analysis of the most relevant input features used in the literature to predict the main parameters related to the fatigue response is provided. Each work has been analyzed to highlight its strengths and peculiarities, thereby offering insights into novel methodologies and approaches for addressing critical challenges within this field. Particular attention is dedicated to the role of defects and the related size‐effect, as they strongly influence the fatigue response. In conclusion, this review not only synthesizes existing knowledge but also offers forward‐looking recommendations for future research directions, providing a valuable resource for researchers in the domain of ML‐assisted fatigue assessment for AM metal alloys.

Directional solidification of Cu with dispersed W nanoparticles: A molecular dynamics study in the context of additive manufacturing

Directional solidification, resulting in the development of columnar grains, is prevalent in laser-additive manufacturing of metal parts. The addition of grain refiners or inoculants enables the control of the nucleation process and columnar-to-equiaxed transition. In this study, we applied molecular dynamics simulations to investigate the directional solidification in liquid copper inoculated with tungsten nanoparticles. The interaction of the solidification front with inert nanoparticles resulted in either their engulfment in the Cu matrix or their pinning by grain boundaries. Analysis of the forces acting on the nanoparticles and the temperature field provides conditions for engulfment, determining the feasibility of copper matrix composites. By global cooling reflecting the thermal dynamics characteristics of additive manufacturing, a columnar-to-equiaxed transition can be captured at the nanoscale, with heterogeneous nucleation occurring at the nanoparticle surfaces. The conditions for the onset of heterogeneous nucleation were assessed on the basis of nanoparticle size and interpreted in terms of classical nucleation theory. Our results highlight the critical role of W-nanoparticle wetting by Cu solid grains in increasing grain refinement efficiency and suppression of columnar growth in additive manufacturing.

Complete physico-chemical characterisation of foundry waste

ABSTRACT The manufacture of foundry metal parts generates various types of mineral wastes. Studies mentioned in the literature are mainly interested in the characterisation of foundry sands and their recycling way. The other wastes (finer than sand) are not dealt and are currently stored in landfills without any recycling solution. This paper aims to fill this gap and reports the complete characterisation of foundry wastes (FW) we carried out to find a way of recycling these materials. FWs were characterised by X-ray fluorescence, X-ray diffraction, scanning electron microscopy and thermogravimetry analyses (TGAs). Leaching tests complying with the NF EN 12457-2 standard were also carried out to evaluate the pollution degree of the different waste products. The results of this work that foundries do not produce just one type of waste, but several. Five types of waste were thus analysed and the results indicated in the first step that each sand was unique and in a second one that the two foundries present a certain similarity with regard to their materials. This complete characterisation study will provide a better understanding of their chemical composition and degree of pollution, so that they can be used more effectively in cement blends, which will be the subject of the rest of this study. The reuse of FW in concrete and mortars is possible and can reduce the environmental impact caused by their storage in landfills.