Project description:Powder bed additive manufacturing (AM) processes, including binder jetting (BJAM) and powder bed fusion (PBF), can manufacture complex three-dimensional components from a variety of materials. A fundamental understanding of the spreading of thin powder layers is essential to develop robust process parameters for powder bed AM and to assess the influence of powder feedstock characteristics on the subsequent process outcomes. Toward meeting these needs, this work presents the design, fabrication, and qualification of a testbed for modular, mechanized, multi-layer powder spreading. The testbed is designed to replicate the operating conditions of commercial AM equipment, yet features full control over motion parameters including the translation and rotation of a roller spreading tool and precision motion of a feed piston and the build platform. The powder spreading mechanism is interchangeable and therefore can be customized, including the capability for dispensing of fine, cohesive powders using a vibrating hopper. Validation of the resolution and accuracy of the machine and its subsystems, as well as the spreading of exemplary layers from a range of powder sizes typical of BJAM and PBF processes, are described. The precision engineered testbed can therefore enable the optimization of powder spreading parameters for AM and correlation to build process parameters in future work, as well as exploration of spreading of specialized powders for AM and other techniques.
Project description:In conventional processing, metals go through multiple manufacturing steps including casting, plastic deformation, and heat treatment to achieve the desired property. In additive manufacturing (AM) the same target must be reached in one fabrication process, involving solidification and cyclic remelting. The thermodynamic and kinetic differences between the solid and liquid phases lead to constitutional undercooling, local variations in the solidification interval, and unexpected precipitation of secondary phases. These features may cause many undesired defects, one of which is the so-called hot cracking. The response of the thermodynamic and kinetic nature of these phenomena to high cooling rates provides access to the knowledge-based and tailored design of alloys for AM. Here, we illustrate such an approach by solving the hot cracking problem, using the commercially important IN738LC superalloy as a model material. The same approach could also be applied to adapt other hot-cracking susceptible alloy systems for AM.
Project description:The process instabilities intrinsic to the localized laser-powder bed interaction cause the formation of various defects in laser powder bed fusion (LPBF) additive manufacturing process. Particularly, the stochastic formation of large spatters leads to unpredictable defects in the as-printed parts. Here we report the elimination of large spatters through controlling laser-powder bed interaction instabilities by using nanoparticles. The elimination of large spatters results in 3D printing of defect lean sample with good consistency and enhanced properties. We reveal that two mechanisms work synergistically to eliminate all types of large spatters: (1) nanoparticle-enabled control of molten pool fluctuation eliminates the liquid breakup induced large spatters; (2) nanoparticle-enabled control of the liquid droplet coalescence eliminates liquid droplet colliding induced large spatters. The nanoparticle-enabled simultaneous stabilization of molten pool fluctuation and prevention of liquid droplet coalescence discovered here provide a potential way to achieve defect lean metal additive manufacturing.
Project description:3D microfluidic devices have emerged as powerful platforms for analytical chemistry, biomedical sensors, and microscale fluid manipulation. 3D printing technology, owing to its structural fabrication flexibility, has drawn extensive attention in the field of 3D microfluidics fabrication. However, the collapse of suspended structures and residues of sacrificial materials greatly restrict the application of this technology, especially for extremely narrow channel fabrication. In this paper, a 3D printing strategy named nanofiber self-consistent additive manufacturing (NSCAM) is proposed for integrated 3D microfluidic chip fabrication with porous nanofibers as supporting structures, which avoids the sacrificial layer release process. In the NSCAM process, electrospinning and electrohydrodynamic jet (E-jet) writing are alternately employed. The porous polyimide nanofiber mats formed by electrospinning are ingeniously applied as both supporting structures for the suspended layer and percolating media for liquid flow, while the polydimethylsiloxane E-jet writing ink printed on the nanofiber mats (named construction fluid in this paper) controllably permeates through the porous mats. After curing, the resultant construction fluid-nanofiber composites are formed as 3D channel walls. As a proof of concept, a microfluidic pressure-gain valve, which contains typical features of narrow channels and movable membranes, was fabricated, and the printed valve was totally closed under a control pressure of 45 kPa with a fast dynamic response of 52.6 ms, indicating the feasibility of NSCAM. Therefore, we believe NSCAM is a promising technique for manufacturing microdevices that include movable membrane cavities, pillar cavities, and porous scaffolds, showing broad applications in 3D microfluidics, soft robot drivers or sensors, and organ-on-a-chip systems.
Project description:For many different types of businesses, additive manufacturing has great potential for new product and process development in many different types of businesses including automotive industry. On the other hand, there are a variety of additive manufacturing alternatives available today, each with its own unique characteristics, and selecting the most suitable one has become a necessity for relevant bodies. The evaluation of additive manufacturing alternatives can be viewed as an uncertain multi-criteria decision-making (MCDM) problem due to the potential number of criteria and candidates as well as the inherent subjectivity of various decision-experts engaging in the process. Pythagorean fuzzy sets are an extension of intuitionistic fuzzy sets that are effective in handling ambiguity and uncertainty in decision-making. This study offers an integrated fuzzy MCDM approach based on Pythagorean fuzzy sets for assessing additive manufacturing alternatives for the automotive industry. Objective significance levels of criteria are determined using the Criteria Importance Through Inter-criteria Correlation (CRITIC) technique, and additive manufacturing alternatives are prioritized using the Evaluation based on Distance from Average Solution (EDAS) method. A sensitivity analysis is performed to examine the variations against varying criterion and decision-maker weights. Moreover, a comparative analysis is conducted to validate the acquired findings.
Project description:The combination of Atomic Diffusion Additive Manufacturing (ADAM) and traditional CNC machining allows manufacturers to leverage the advantages of both technologies in the production of functional metal parts. This study presents the methodological development of hybrid manufacturing for solid copper parts, initially produced using ADAM technology and subsequently machined using a 5-axis CNC system. The ADAM technology was dimensionally characterized by adapting and manufacturing the seven types of test artifacts standardized by ISO/ASTM 52902:2019. The results showed that slender geometries suffered warpage and detachment during sintering despite complying with the design guidelines. ADAM technology undersizes cylinders and oversizes circular holes and linear lengths. In terms of roughness, the lowest results were obtained for horizontal flat surfaces, while 15° inclined surfaces exhibited the highest roughness due to the stair-stepping effect. The dimensional deviation results for each type of geometry were used to determine the specific and global oversize factors necessary to compensate for major dimensional defects. This also involved generating appropriate over-thicknesses for subsequent CNC machining. The experimental validation of this process, conducted on a validation part, demonstrated final deviations lower than 0.5% with respect to the desired final part, affirming the feasibility of achieving copper parts with a high degree of dimensional accuracy through the hybridization of ADAM and CNC machining technologies.
Project description:Rapid fabricating and harnessing stimuli-responsive behaviors of microscale bio-compatible hydrogels are of great interest to the emerging micro-mechanics, drug delivery, artificial scaffolds, nano-robotics, and lab chips. Herein, we demonstrate a novel femtosecond laser additive manufacturing process with smart materials for soft interactive hydrogel micro-machines. Bio-compatible hyaluronic acid methacryloyl was polymerized with hydrophilic diacrylate into an absorbent hydrogel matrix under a tight topological control through a 532 nm green femtosecond laser beam. The proposed hetero-scanning strategy modifies the hierarchical polymeric degrees inside the hydrogel matrix, leading to a controllable surface tension mismatch. Strikingly, these programmable stimuli-responsive matrices mechanized hydrogels into robotic applications at the micro/nanoscale (<300 × 300 × 100 μm3). Reverse high-freedom shape mutations of diversified microstructures were created from simple initial shapes and identified without evident fatigue. We further confirmed the biocompatibility, cell adhesion, and tunable mechanics of the as-prepared hydrogels. Benefiting from the high-efficiency two-photon polymerization (TPP), nanometer feature size (<200 nm), and flexible digitalized modeling technique, many more micro/nanoscale hydrogel robots or machines have become obtainable in respect of future interdisciplinary applications.
Project description:A novel data science workflow is developed and demonstrated to extract process-structure linkages (i.e., reduced-order model) for microstructure evolution problems when the final microstructure depends on (simulation or experimental) processing parameters. This workflow consists of four main steps: data pre-processing, microstructure quantification, dimensionality reduction, and extraction/validation of process-structure linkages. Methods that can be employed within each step vary based on the type and amount of available data. In this paper, this data-driven workflow is applied to a set of synthetic additive manufacturing microstructures obtained using the Potts-kinetic Monte Carlo (kMC) approach. Additive manufacturing techniques inherently produce complex microstructures that can vary significantly with processing conditions. Using the developed workflow, a low-dimensional data-driven model was established to correlate process parameters with the predicted final microstructure. Additionally, the modular workflows developed and presented in this work facilitate easy dissemination and curation by the broader community.
Project description:Fused Deposition Modeling (FDM), also known as Fused Filament Fabrication (FFF), is the most widely used type of Additive Manufacturing (AM) technology at the consumer level. This technology severely suffers from a lack of online quality assessment and process adjustment. To fill up this gap, a high-speed 2D Laser Profiler KEYENCE LJ-V7000 series is equipped above an FDM machine and performs a scan after each print layer. The point cloud of the upper surface will be processed and transformed into a 2D depth map to analyze the in-plane anomalies during the FDM fabrication process. The author used the above data to categorize the surface quality into four categories: under printing, over printing, normal, and empty regions. The author showed the effectiveness of data in detecting print anomalies, and further work can be done to show the application of more advanced algorithms towards a better detection accuracy or to present a novel way to approach the data and detect a broader range of anomalies.
Project description:The propensity to manufacture functional and geometrically sophisticated parts from a wide range of metals provides the metal additive manufacturing (AM) processes superior advantages over traditional methods. The field of metal AM is currently dominated by beam-based technologies such as selective laser sintering (SLM) or electron beam melting (EBM) which have some limitations such as high production cost, residual stress and anisotropic mechanical properties induced by melting of metal powders followed by rapid solidification. So, there exist a significant gap between industrial production requirements and the qualities offered by well-established beam-based AM technologies. Therefore, beamless metal AM techniques (known as non-beam metal AM) have gained increasing attention in recent years as they have been found to be able to fill the gap and bring new possibilities. There exist a number of beamless processes with distinctively various characteristics that are either under development or already available on the market. Since this is a very promising field and there is currently no high-quality review on this topic yet, this paper aims to review the key beamless processes and their latest developments.