Promising Manufacturing Technology Research Articles

Design and batch fabrication of anisotropic microparticles toward small-scale robots using microfluidics: recent advances.

Small-scale robots with shape anisotropy have garnered significant scientific interest due to their enhanced mobility and precise control in recent years. Traditionally, these miniature robots are manufactured using established techniques such as molding, 3D printing, and microfabrication. However, the advent of microfluidics in recent years has emerged as a promising manufacturing technology, capitalizing on the precise and dynamic manipulation of fluids at the microscale to fabricate various complex-shaped anisotropic particles. This offers a versatile and controlled platform, enabling the efficient fabrication of small-scale robots with tailored morphologies and advanced functionalities from the microfluidic-derived anisotropic microparticles at high throughput. This review highlights the recent advances in the microfluidic fabrication of anisotropic microparticles and their potential applications in small-scale robots. In this review, the term 'small-scale robots' broadly encompasses micromotors endowed with capabilities for locomotion and manipulation. Firstly, the fundamental strategies for liquid template formation and the methodologies for generating anisotropic microparticles within the microfluidic system are briefly introduced. Subsequently, the functionality of shape-anisotropic particles in forming components for small-scale robots and actuation mechanisms are emphasized. Attention is then directed towards the diverse applications of these microparticle-derived microrobots in a variety of fields, including pollution remediation, cell microcarriers, drug delivery, and biofilm eradication. Finally, we discuss future directions for the fabrication and development of miniature robots from microfluidics, shedding light on the evolving landscape of this field.

Ray tracing heat source model for polymer powder bed fusion considering laser energy absorption and multiple reflection

Powder bed fusion is considered one of the most promising additive manufacturing technologies, but the generation and evolution of volumetric heat source involved in the powder bed fusion process remain unclear as laser-powder bed interactions has not been fully developed. In this work, a ray tracing model is established to simulate the laser-powder bed interaction during the powder bed fusion process. Five types of powder beds with homogeneous and non-homogeneous particle radius distributions are generated using the discrete element method, and then the energy distributions in the ray tracing heat source are explored by considering laser energy absorption and multiple reflection. The results indicate that the energy distribution in the ray tracing heat source basically exhibits a Gaussian-like trend in the horizontal plane and an exponential decay in the vertical direction. The fitting coefficients Z0 (related to laser power) and η2 (related to laser radius) of the ray tracing heat source for different powder beds show maximum differences of 21.87 % and 72.22 %, respectively, compared to conventional Gaussian volumetric heat sources. The deviation between the fitting coefficient η3 (related to laser energy attenuation) and the corresponding fitted attenuation coefficient of the powder bed is related to the overall uniformity of the powder bed. Due to multiple reflections of the laser within the powder bed, the ray tracing heat source has larger width and depth as compared to Gaussian volumetric heat source. This study is significant because it provides a more accurate heat source model for the reliable prediction and safe operation of powder bed fusion process.

A Statistical Investigation and Prediction of the Effect of FDM Variables on Flexural Stress of PLA Prints

Due to its many engineering applications, low manufacturing costs, and environmental friendliness, 3D printing is considered one of the most promising manufacturing technologies. The quality of printed parts will inevitably be affected by the controllable variables used in the 3D printing process. The present study aims to investigate how different printing process parameters affect the bending strength of PLA prints. The ASTM D790 standard was used to fabricate the samples in this work, while the Taguchi principle was used to design the experiments. The following values were chosen: shell width (0.8, 1.2, 1.6, and 2 mm), layer thickness (0.15, 0.2, 0.25, and 0.3 mm), and infill density (40%, 60%, 80%, and 100%). The results showed that fill density is the most effective variable for improving bending strength. Measurements of infill density (100%), layer thickness (0.15 mm), and shell width (2 mm) gave the best results, which were calculated to be 83.1479 MPa in bending test. The mathematical model in this study was developed using linear regression analysis, and the residuals confirmed that the model fit the data well, with a maximum error of 6.1%.

Powder movement rules of laser powder bed fusion additive manufacturing aluminum alloy based on discrete element method

Laser Powder Bed Fusion (LPBF) is a promising metal additive manufacturing technology based on layer by layer powder spreading, and powder bed uniformity has a great influence on the forming quality. By Discrete Element Method and powder spreading experiment, the interaction and movement between powder were studied during powder spreading, including powder jamming, rebound, splash, eddy, and empty powder area. Additionally, five kinds of powder spreading schemes were explored, and the new process of one-way reciprocating with tri-splint blade was designed to change the motion state of powder spreading from “blade pushing powder” to “blade holding powder.” By increasing the distance between the blade and the working platform form 0 to 20 µm with the distance between the upper surface of the substrate and the working platform 50 µm, defects such as powder splash and empty powder decreased. And the uniform powder bed of aluminum alloy powder was achieved with the new process of one-way reciprocating with tri-splint blade structure.

Enriching Laser Powder Bed Fusion Part Data Using Category Theory

Laser powder bed fusion (LPBF) is a promising metal additive manufacturing technology for producing functional components. However, there are still a lot of obstacles to overcome before this technology is considered mature and trustworthy for wider industrial applications. One of the biggest obstacles is the difficulty in ensuring the repeatability of process and the reproducibility of products. To tackle this challenge, a prerequisite is to represent and communicate the data from the part realisation process in an unambiguous and rigorous manner. In this paper, a semantically enriched LPBF part data model is developed using a category theory-based modelling approach. Firstly, a set of objects and morphisms are created to construct categories for design, process planning, part build, post-processing, and qualification. Twenty functors are then established to communicate these categories. Finally, an application of the developed model is illustrated via the realisation of an LPBF truncheon.

Solar-driven hydrogen production based on moisture adsorption-desorption cycle

Photocatalytic hydrogen production by water splitting is considered one of the most promising green hydrogen manufacturing technologies, but the actual outdoor deployment of this technology is limited by the availability of water feedstocks in geographical locations. An integrated device consisting of a photocatalytic layer and an atmospheric water harvesting material that can achieve in-situ photocatalytic water vapor splitting for hydrogen production by spontaneously capturing moisture from the air as a water feedstock is proposed herein. The integrated system has excellent photocatalytic and photothermal synergistic ability, low hydrogen diffusion resistance by interfacial photocatalysis, and high stability by isolating salts contamination. The system can achieve a hydrogen production rate of 425.4 μmol g−1 h−1 and a water vapor supply rate of 0.121 kg m−2 h−1 under one sun illumination, and has a water vapor adsorption rate of 0.03 kg m−2 h−1 under dark conditions in the laboratory. Furthermore, this hybrid device obtains a total hydrogen production of up to 3963.1 μmol g−1 in the real outdoor daytime and accomplishes water regeneration within 10 hours by capturing environmental moisture at night. This work provides a suitable solution for hydrogen production in areas with water scarcity or drought.

Microstructure and mechanical properties of IN718 on 42CrMo for repair applications by laser directed energy deposition

Laser Directed Energy Deposition (LDED) has gained significant attention as a promising additive manufacturing technology for the repair and restoration of worn parts. The objective of this study is to assess the feasibility and performance of repairing 42CrMo substrates using Inconel 718(IN718) powder. In this study, the LDED process was employed to investigate the influence of varying laser power, scanning speed, and feed rate on a single track study. The optimal process parameters for trapezoidal groove repair were determined by comparing and evaluating factors such as dilution, aspect ratio, track continuity, and molten pool shape. Furthermore, the effects of different repair strategies (X-, Y-, Cross-paths) on the microstructure and mechanical properties were investigated. The results indicate that by using suitable process parameters, successful deposition of IN718 on 42CrMo is achieved. The investigation reveals elemental diffusion between the two materials, quenching phase transition on the substrate surface, and the formation of intermetallic compounds in the fusion zone. The fusion zone(FZ) and heat-affected zone(HAZ) exhibited the highest microhardness compared to the substrate. Significant changes in molten pool shape and arrangement were observed using different repair strategies, while the effect on grain size was not significant. Due to the high directionality of the samples fabricated using the LDED process, the samples of the X-path demonstrated the highest ultimate tensile strength and elongation compared to the other two repair strategies. The ultimate tensile strength after repair is comparable to that of the substrate, while the elongation is reduced.

Evaluation of tensile properties of ABS material specimen using commercial and manufactured filaments in FDM process

Acrylonitrile butadiene styrene (ABS) is the most common of engineering plastic materials; it is copolymer of Styrene-Acrylonitrile and butadiene styrene in special form. It is widely used in substantive and Additive Manufacturing (AM) applications. AM is a promising manufacturing technology that can fulfill future needs as products and their configurations are changing day by day. Fused Deposition Modeling FDM is an AM technique that fabricates parts layer upon layer. This paper analyses the effect of different process variables and orientations on the tensile properties of the FDM manufactured specimens. Standard specimens are printed using commercial ABS and manufactured ABS filaments for analysis of tensile properties. The ABS filament is fabricated using a mini filament extruder. After modeling, the specimens are printed using an FDM printer. The tensile testing of specimens is carried out on the Instron 3360 table frame UTM machine. Tensile properties obtained for different specimens are presented.

Effect of solid-solution treatment on high-temperature properties and creep fracture mechanism of laser powder bed fused Inconel 625 alloy

Laser powder bed fusion (LPBF) has been widely used for fabricating Inconel 625 (IN625) alloy, and it is widely regarded as a promising manufacturing technology. However, during high-temperature service, the γ″ strengthening phase of LPBFed IN625 gradually transforms into the detrimental δ phase. Controlling the morphology and distribution of precipitates is necessary for optimizing high-temperature performance. This study compared the as-built and 1090 °C solution-treated LPBFed IN625 alloy and investigated the microstructure evolution, tensile properties, and creep fracture behavior at a high temperature of 815 °C. The results show that the solid-solution treatment improves the creep ductility of LPBFed IN625 alloys at 815 °C by forming a high percentage of twinned boundaries and eliminating dislocations. The formation of coarsened needle-like δ phases and carbides during creep reduces the strength of the grain boundaries and is responsible for creep fracture failure. During creep of the as-built samples, the irregular Laves phase between the columnar dendrites gradually transforms into plate-like δ phases and M6C carbides uniformly and densely distributed within the columnar grains. The precipitation-strengthening effect of these fine precipitates, combined with the initial high density of dislocations and strong texturing effect, brings favorable high-temperature strength and creep fracture life but consequently leads to low creep ductility.

3D bioprinting in otorhinolaryngology: from bench to clinical application

Three-dimensional (3D) bioprinting is a promising additive manufacturing technology that uses imaging data and computer-assisted deposition of biological materials or cells to reconstruct complex 3D structures accurately. This technology has progressed rapidly, in part because of its integration across multiple disciplines and combination with other technologies for clinical applications. Advances in experimental research and clinical applications related to otorhinolaryngology have led to the development of diagnostic and treatment methods based on 3D bioprinting, including the development of tissue engineering scaffolds, biosensors, organ chips, and organoids, surgical planning, graft construction, and medical education. Additionally, otorhinolaryngologists will be better equipped to treat tissue function defects with personalized printed graft implants. It is also expected that 3D printing can be used to build ideal in vitro models in the future to help solve existing research challenges. This article briefly introduces the relevant 3D bioprinting technologies and bioinks that can be used by otorhinolaryngologists and discusses their potential applications in otorhinolaryngology.

A Real-Time Monitoring Method for Selective Laser Melting of TA1 Materials Based on Radiation Detection of a Molten Pool.

Selective laser melting (SLM) technology is a promising additive manufacturing technology. However, due to the numerous influencing factors in this complex process, a reliable real-time method is needed to monitor the forming process of SLM. The molten pool is the smallest forming unit in the SLM process, the consistency of which can effectively reflect the quality of the printing process. By using a coaxial optical path structure and a compound amplifier circuit, high-speed acquisition of molten pool radiation can be realized. Next, single factor analysis and orthogonal experimentation were used to investigate the influence levels of key process parameters on the radiation of molten pool. In addition, numerical simulation was carried out with the same parameter setting schemes, the results of which are consistent with those in radiation detection experiments. It is shown that the laser power has the greatest effect on the radiation of the molten pool, while the scanning speed and the hatch spacing have little effect on the radiation. Finally, the positioning experiment involving the small hole structure was carried out, and the experimental results showed that the device could accurately locate the position coordinates of the given hole structure.

Modeling of the shape of Taylor cone in EHD printing based on backpropagation neural network and genetic algorithm

Electrohydrodynamic (EHD) printing has been recognized as a promising additive manufacturing technology with superior pattern resolution and economic viability. The shape of Taylor cone is deemed a pivotal element for the amplification of deposition efficiency, and the maintenance of consistent operational steadiness in EHD printing. The correlations between diverse operating parameters and the shape of Taylor cone are presently not well investigated. In this paper, modeling of relationships between operating parameters and the shape of Taylor cone was conducted with a backpropagation neural network (BPNN) and a genetic algorithm optimized backpropagation (GA-BP) neural network. Taylor cone semi-vertical angle and the meniscus height were employed as two indexes to characterize the shape of Taylor cone. The prediction accuracies of BPNN model and GA-BP model were 92.79 % and 95.46 %, respectively. The GA-BP model demonstrated higher precision in forecasting the shape of Taylor cone. A predictive framework for the shape of Taylor cone was proposed, which provided a practical tool for process optimization in EHD printing.

A study on effects of curing and machining conditions in post-processing of SLA additive manufactured polymer

Stereolithography (SLA) is a promising additive manufacturing technology in terms of versatility and functionality. However, SLA-printed products lack mechanical properties and accuracy, so post-processing is essential. This study comprehensively contributes to a deeper understanding of influences of two types of post-processing [ultraviolet (UV) treatment and machining] and interactions between them. The effect of UV treatment was first investigated by evaluating properties of SLA specimens before and after UV curing. The UV curing greatly increased hardness by rearranging the molecular chains. The higher curing temperature, the denser and more uniform network was created; the surface hardness of the specimen cured at 80 °C increased by 208.30 % compared to the uncured specimen. Then, the interactions were analyzed by machining SLA specimens cured under different conditions. The pre-curing increased the hardness, molecular bonding, and brittleness. Thus, small surface roughness on the machined surface and small exit edge chipping were observed under the non-pre-curing conditions; the pre-curing increased the exit edge chipping size up to 48.21 %.

A novel model considering combined effects of as-built roughness and notch for multiaxial fatigue life prediction of L-PBF Ti6Al4V

Additive Manufacturing (AM) has emerged as a promising manufacturing technology. However, there is still a lack of effective methods for evaluating the influence of as-built roughness, especially for notched components under complex fatigue loading. In this paper, a method based on damage mechanics for multiaxial fatigue life prediction is proposed for addressing the individual and combined effect of as-built surfaces and macro notches. Chaboche material model is coupled with a damage variable to describe the elastic–plastic deformation around the notch. The effect of as-built roughness is integrated into the damage evolution law developed by Chaudonneret, taking into account the degree of roughness, the orientation-dependent effect, and the variation of roughness effect with stress levels. The proposed model is numerically implemented by UMAT and validated through experimental data from literature under four types of fatigue loading including axial, torsion, in-phase axial-torsion and 90° out-of-phase axial-torsion. The predicted results agree well with the experimental values and the combined effect of the as-built roughness and notch is well analyzed.

Generation of neutral droplets via waveform optimization for stable electrohydrodynamic drop-on-demand printing

Electrohydrodynamic (EHD) drop-on-demand (DOD) printing is a highly promising additive manufacturing technology, particularly for micro- and nanoscale applications, owing to its exceptional printing resolution. However, a significant challenge for EHD DOD to achieve stable printing is the accumulation of residual charge, particularly on highly insulated substrates where the charge cannot be discharged. A novel voltage-driven waveform is proposed to generate neutral droplets in EHD DOD printing, aiming to completely solve the problem of residual charge accumulation. The proposed voltage-driven waveform consists of two waveforms with opposite polarity and equal amplitude. The waveforms of opposite polarity generate two droplets with opposite charges. Overlapping these two droplets causes the charges to be neutralized, therefore generating neutral droplets. The correctness of the proposed method is verified through simulations and experiments, and the printing parameters were optimized accordingly. Notably, silver nanowires with a line width of 5.3 μm were successfully printed on insulating substrates using this technique, and large-area on-demand patterns were stably printed. This outcome underscores the capability of the proposed high-voltage driving waveform to generate charge-neutral droplets, thereby significantly enhancing the stability of EHD DOD printing and facilitating its implementation in high-resolution manufacturing processes.

DEPOSITION QUALITY OPTIMIZATION OF ADDITIVE FRICTION STIR DEPOSITED ALUMINIUM ALLOY USING UNSUPERVISED MACHINE LEARNING

Additive friction stir deposition (AFSD) is a promising solid-state additive manufacturing technology, but achieving continuous high deposition quality remains challenging due to complex process-structure connections. This study investigates unsupervised machine learning algorithms for mapping process parameters to deposition outcomes without requiring extensive labelled data. On experimental deposition data, algorithms including hierarchical clustering, k-means, spectral clustering, Gaussian mixtures, autoencoders, and self-organizing maps are used. The algorithms find intrinsic patterns and groupings in the multi- factor process data in an unbiased manner. With a silhouette score of 0.7618, k-means clustering performed the best, showing cohesive data clustering. Visualizations like dendrograms and trained maps shed light on the links between process parameters and deposition quality. The cluster analysis identifies process conditions that result in poor deposition quality. This highlights the ability of unsupervised approaches to capture deposition process patterns based solely on data with no prior system knowledge. This data-driven strategy has the potential to significantly improve AFSD process optimization and control, with implications for boosting industrial adoption. The unsupervised learning framework lays the groundwork for using process data to improve the quality and productivity of advanced manufacturing procedures.

The Investigation of Dimensional, Form and Orientation Accuracy of Polymer Gears Prepared by Additive Manufacturing

Among the additive manufacturing techniques Fused Deposition Modeling (FDM) is a widely used process, due to its rapid development in the recent years, and its consequent drop in price. 3D printing has multiple process parameters, which affect the dimensional, form, and orientation accuracy, and thus the usability of the final product. In this work the effects of layer thickness, printing speed, and infill on the dimensional and geometric deviations were studied with a 3 factor Box-Behnken experimental design. The measured deviations were within the tolerance range specified by the standards. Based on this, it can be stated that 3D printing is a promising gear manufacturing technology for light and medium load applications.

Direct laser writing-enabled 3D printing strategies for microfluidic applications.

Over the past decade, additive manufacturing-or "three-dimensional (3D) printing"-has attracted increasing attention in the Lab on a Chip community as a pathway to achieve sophisticated system architectures that are difficult or infeasible to fabricate via conventional means. One particularly promising 3D manufacturing technology is "direct laser writing (DLW)", which leverages two-photon (or multi-photon) polymerization (2PP) phenomena to enable high geometric versatility, print speeds, and precision at length scales down to the 100 nm range. Although researchers have demonstrated the potential of using DLW for microfluidic applications ranging from organ on a chip and drug delivery to micro/nanoparticle processing and soft microrobotics, such scenarios present unique challenges for DLW. Specifically, microfluidic systems typically require macro-to-micro fluidic interfaces (e.g., inlet and outlet ports) to facilitate fluidic loading, control, and retrieval operations; however, DLW-based 3D printing relies on a micron-to-submicron-sized 2PP volume element (i.e., "voxel") that is poorly suited for manufacturing these larger-scale fluidic interfaces. In this Tutorial Review, we highlight and discuss the four most prominent strategies that researchers have developed to circumvent this trade-off and realize macro-to-micro interfaces for DLW-enabled microfluidic components and systems. In addition, we consider the possibility that-with the advent of next-generation commercial DLW printers equipped with new dynamic voxel tuning, print field, and laser power capabilities-the overall utility of DLW strategies for Lab on a Chip fields may soon expand dramatically.

Development of an intuitive visualization system for measuring the melt electrowritten (MEW) jet diameter along the spinline

AbstractMelt electrowriting (MEW) is a young and promising additive manufacturing technology for producing high‐precision scaffolds. In this study, we develop an intuitive visualization system enabling users to measure MEW jet diameters along the spinline without programming knowledge. The digital measurement adopts the same theory as our reported manual measurement. The maximum error between the digitally and manually measured data is smaller than 9%, suggesting that the open‐source system developed here is reliable in measuring jet diameters.

Uncertainty-driven trustworthy defect detection for high-resolution powder bed images in selective laser melting

Selective laser melting (SLM) is known as one of the most promising metal additive manufacturing technologies, and how to ensure its consistent quality is still a main challenge, which urgently needs to be addressed. The metal powder spreading quality, as the first stage of SLM, has a direct impact on the subsequent forming and it is necessary to be monitored. However, existing deep learning-based methods for monitoring powder spreading quality often suffer from the problem of unreliability. The high-resolution property of powder bed images is often ignored, and the predicted results are not well evaluated. To address the above issues and achieve trustworthy defect detection, this paper proposes an uncertainty-driven trustworthy defect detection method for high-resolution powder bed images in SLM. A super resolution module based on ISDNet is first proposed to refine the upsampling process. Checkpoint ensemble is adopted for better achieving model uncertainty estimation only requiring a single training process, feature reuse helps reduce the computational load, and temperature scaling is used to further calibrate each ensemble member to get a more precise uncertainty. Besides, we design an uncertainty-driven model improvement method to further improve defect detection performance for regions with high uncertainty. Experiments demonstrate the effectiveness of our proposed method, and our work achieves a trustworthy defect detection for high-resolution powder bed images in SLM.