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Geometrical-based Simulation of Drop-on-Demand 3D Inkjet Printing
Abstract
As additive manufacturing has evolved, 3D inkjet printing (IJP) has become a promising alternative manufacturing method able to manufacture functional multi-material parts in a single process. However, issues with part quality in terms of dimensional errors and lack of precision still restrict its industrial and commercial applications. This study aims at improving the dimensional accuracy of 3D IJP parts by developing an optimization-oriented simulation tool of droplet behaviour during the drop-on-demand 3D IJP process. The simulation approach takes into consideration the effect of droplet volume, resolution of processed TIFF image, contact angle of the ink on the solid substrate and coalescence performance of overlapping droplets, in addition to the number of printed layers. Following the development of the simulation tool using MATLAB, its feasibility was validated using already printed parts. The simulation results are found to be in a good agreement with the dimensions of the printed parts. The developed tool was then used to elucidate the effect of resolution of processed TIFF image and droplet diameter on the dimensional accuracy of 3D IJP parts.
... Chief among other polymer-based AM technologies, inkjet printing has shown a high capability to fabricate functional components made of multiple materials with 3D intricate features. With ability of on-demand digital jetting, inkjet has seen successful implementations to produce 3D printed components of high resolution and integrated functionality [15,16] (Fig. 1). Over the last decades, inkjet printing has seen noticeable adoption by industrial community for successful applications ranging from printed electronics, smart sensors, bioinspired components, micro-and 4D active devices and drug delivery [17]. ...
The dawn of mainstream electric vehicles is here, with almost all major automotive manufacturers now offering fully electric alternatives to traditional internal combustion-based cars. This is in alignment with major trends in sustainability in today’s world where a reduction in the usage of fossil fuels and other fossil fuel-dependent technologies is being realized. Additive Manufacturing technologies that have in the past been driven by the automotive industry, now present an opportunity for the automotive sector, where greater gains and benefits can be made by its adoption. Additively manufactured parts enable complex optimized design and also promote material efficiency. Moreover, system wide benefits in terms of streamlined supply chains and logistics operations are also possible and promised. This manuscript presents these benefits, the reason for the growing interests and an outlook for the future of integrated, metal- and polymer-based, additive manufacturing within traditional automotive manufacturing process chains.
Friction stir consolidation (FSC) is a solid-state process that recycles metal scraps economically and eco-friendly compared to the conventional melting method. The process parameters especially processing time and rotational speed, have a crucial role in achieving a sound disc during FSC. The current study answers the research question of how far these process parameters can be effective when the mass of chips to be recycled increases. In specific, an experimental setup was analyzed that was previously identified as challenging for recycling 20 g chips of aluminum alloy AA 2024-O. Rotational speed was set doubled, and processing time was increased up to 1.5 times of their initial values. The results were found opposing to the reported one. It was noticed that raising the processing time and rotational speed are not always promising to achieve a quality consolidated disc with better mechanical properties. In contrast, they can lead to unconsolidated discs with more non-homogeneous mechanical properties. Thus, this research work highlights the hidden challenges in producing a sound disc during friction stir consolidation.
This article reports on the investigation of the effects of process parameters and their interactions on as-built part quality and resource-efficiency of the fused filament fabrication 3D printing process. In particular, the influence of five process parameters: infill percentage, layer thickness, printing speed, printing temperature, and surface inclination angle on dimensional accuracy, surface roughness of the built part, energy consumption, and productivity of the process was examined using Taguchi orthogonal array (L50) design of experiment. The experimental results were analyzed using ANOVA and statistical analysis, and the parameters for optimal responses were identified. Regression models were developed to predict different process responses in terms of the five process parameters experimentally examined in this study. It was found that dimensional accuracy is negatively influenced by high values of layer thickness and printing speed, since thick layers of printed material tend to spread out and high printing speeds hinder accurate deposition of the printed material. In addition, the printing temperature, which regulates the viscosity of the used material, plays a significant role and helps to minimize the dimensional error caused by thick layers and high printing speeds, whereas the surface roughness depends very much on surface inclination angle and layer thickness, which together determine the influence of the staircase effect. Energy consumption and productivity are primarily affected by printing speed and layer thickness, due to their high correlation with build time.
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This paper presents the first investigations towards closed loop feedback control of the selective laser melting (SLM) process. Insight gained from this work can be applied to facilitate in-process optimization of the SLM process for maximizing part quality and minimizing surface roughness.
Abstract
Additive manufacturing provides a number of benefits in terms of infinite freedom to design complex parts and reduced lead-times while globally reducing the size of supply chains as it brings all production processes under one roof. However, additive manufacturing (AM) lags far behind conventional manufacturing in terms of surface quality. This proves a hindrance for many companies considering investment in AM. The aim of this work is to investigate the effect of varying process parameters on the resultant roughness of the down-facing surfaces in selective laser melting (SLM). A systematic experimental study was carried out and the effects of the interaction of the different parameters and their effect on the surface roughness (Sa) were analyzed. It was found that the interaction and interdependency between parameters were of greatest significance to the obtainable surface roughness, though their effects vary greatly depending on the applied levels. This behavior was mainly attributed to the difference in energy absorbed by the powder. Predictive process models for optimization of process parameters for minimizing the obtained Sa in 45° and 35° down-facing surface, individually, were achieved with average error percentages of 5% and 6.3%, respectively, however further investigation is still warranted.
A lattice Boltzmann (LB) formulation, which is consistent with the phase-field model for two-phase incompressible fluid, is proposed to model the interface dynamics of droplet impingement. The interparticle force is derived by comparing the macroscopic transport equations recovered from LB equations with the governing equations of the continuous phase-field model. The inconsistency between the existing LB implementations and the phase-field model in calculating the relaxation time at the phase interface is identified and an approximation is proposed to ensure the consistency with the phase-field model. It is also shown that the commonly used equilibrium velocity boundary for the binary fluid LB scheme does not conserve momentum at the wall boundary and a modified scheme is developed to ensure the momentum conservation at the boundary. In addition, a geometric formulation of the wetting boundary condition is proposed to replace the popular surface energy formulation and results show that the geometric approach enforces the prescribed contact angle better than the surface energy formulation in both static and dynamic wetting. The proposed LB formulation is applied to simulating droplet impingement dynamics in three dimensions and results are compared to those obtained with the continuous phase-field model, the LB simulations reported in the literature, and experimental data from the literature. The results show that the proposed LB simulation approach yields not only a significant speed improvement over the phase-field model in simulating droplet impingement dynamics on a submillimeter length scale, but also better accuracy than both the phase-field model and the previously reported LB techniques when compared to experimental data. Upon validation, the proposed LB modeling methodology is applied to the study of multiple-droplet impingement and interactions in three dimensions, which demonstrates its powerful capability of simulating extremely complex interface phenomena.
Droplet jetting behavior largely determines the final drop deposition quality in the inkjet printing process. Forming such behavior is governed by the fluid flow pattern. Therefore, a measurement of the flow pattern is of great importance for improving the printing quality of the inkjet process. Most of the current works use static images for the study of the drop evolution process. The problem of the static images is that the images cannot recognize the motion information (i.e., temporal transformation) of the droplet. Thus the information of the jetting process in the temporal domain will be lost. Instead of using the images, this paper takes the video data as the study subject to investigate the droplet evolution behavior in the inkjet printing process. Moreover, this paper introduces a deep learning method for the study of such video data. Compared to most of the current learning approaches conducted in a supervised/semi-supervised manner for manufacturing process data, we propose an unsupervised learning method for studying the flow pattern of the droplet, which does not require well-defined ground-truth labels. Regarding the spatial and temporal transformation of the droplet in video data, we apply a deep recurrent neural network (DRNN) to implement the proposed unsupervised learning. To verify the hypothesis that the proposed method can learn a latent representation for reproducing original data, the proposed DRNN is trained and tested on both simulation and experimental datasets. Experimental results demonstrate that the proposed method can learn latent representations of the droplet jetting process video data, which is very useful for the prediction of the droplet behavior. Furthermore, through latent space decoding, the learned representations can infer the droplet forming stimulus parameters such as material properties, which would be very helpful for further understanding of the process dynamics and achieving real-time in-situ droplet deposition quality monitoring and control.
We have studied the stability of ink-jet printed lines of liquid with zero receding contact angle on a homogeneous substrate. Such lines can become unstable by forming a series of liquid bulges, at various wavelengths, connected by a ridge of liquid. The instability was studied with a simple dynamic model. It was shown that the line becomes unstable when the contact angle of the liquid with the substrate is larger than the advancing contact angle. This condition, however, is not a sufficient condition. When the transported flow rate is sufficiently small compared to the applied flow rate a printed line can be shown to be stable, i.e. the width of the printed line is constant. This was found to depend on the advancing contact angle of the liquid over the substrate.
Coalescence of a falling droplet with a stationary sessile droplet is studied experimentally. High-speed video images are
presented to show coalescence dynamics, shape evolution and contact line movement. Emphasis is put on spread length, which
is the length of two coalesced droplets along their original centers. Experimental results have shown that the spread length
can be larger or smaller than the ideal spread length, which is the spread diameter of individual droplet plus the center-to-center
distance between the two droplets. Three different coalescence mechanisms based on comparing the maximum and the minimum spread
lengths to the ideal spread length are identified. Correlations for the maximum and the minimum spread lengths are developed,
which can be combined with the coalescence domains to determine the deposition conditions for forming continuous or discontinuous
lines.
To produce stable lines with parallel sides through inkjet printing, individual drops are deposited on a surface so that they coalesce; this initial liquid line (or bead) must remain stable until it forms a solid. The stable line width is shown to be bounded by two limits, with the lower bound (minimum line width) determined by the maximum drop spacing for stable coalescence and the upper bound determined by the minimum drop spacing below which a bulging instability occurs. The maximum stable track width is also a function of the velocity at which an inkjet printhead traverses the substrate. These bounds are presented in dimensionless form and are shown to agree well with experiment. To enable easier determination of the stability of an arbitrary ink/substrate combination, both the upper and lower bounds are presented in graphical forms to define a region of bead stability in an appropriate parameter space.
Inkjet Printing of Functional and Structural Materials: Fluid Property Requirements, Feature Stability, and Resolution
- B Derby
B. Derby, "Inkjet Printing of Functional and
Structural Materials: Fluid Property Requirements,
Feature Stability, and Resolution", Annual Review
of Materials Research, 2010; 40: 395-414.