A. Fabrication of mesostructured systems using multi-material additive manufacturing

A heterogeneous material system with a pre-defined mesostructure design is readily fabricated using multimaterial additive manufacturing technologies, such as multi-material inkjet 3D printing, multi-nozzle extrusion, and the LENS process. The mesoscale material distribution is generated by the computer-aided design files and implemented during the manufacturing process, in which each voxel is assigned to a given material or a mixture of multiple constituent materials with controlled volume ratios. To avoid changes in material distribution by interdiffusion, postprocessing heat treatment is generally not used. As a result, the local material properties strongly depend on the processing parameters; material parameters from conventional databases are not accurate to use for materials fabricated by multi-material additive manufacturing.

B. Deformation mapping using DIC or DVC

The deformation of a mesoscale heterogeneous material system cannot be fully characterized using a single stress–strain relationship, because two different mesostructured material systems can have the same stress–strain curve under axial loading. Instead, characterization of the local deformation at the material voxel level is necessary. In a mesoscale heterogeneous material system, the deformation of surface material voxels can be characterized using DIC, ^{Reference Pan, Qian, Xie and Asundi50} whereas the deformation of internal material voxels can be characterized using DVC, ^{Reference Bay, Smith, Fyhrie and Saad47} e.g., through X-ray tomography. The 2D image contrast in DIC comes from black and white speckling on the surface of the material. The 3D image contrast in DVC, either absorption contrast or phase contrast, ^{Reference Zhu, Zhang and Barbastathis53} comes from the mesoscale heterogeneity in the material systems.

C. Modeling of the deformation behavior of the mesostructured material systems

The success of mesostructure design and optimization depends on the capability of accurately modeling the deformation behavior. Once the constitutive behavior of each material voxel is known (e.g., linear elastic, elastic–plastic, or hyperelastic), the deformation of the material system can be simulated using finite element analysis. The part properties in additive manufacturing strongly depend on processing conditions. The values of material parameters from the conventional material databases are not suitable as they generally do not include information for additively manufactured parts. It is thus critical to calibrate the effective material parameters in a mesostructured material system from experiments.

D. Model calibration using voxel-level deformation data

In additive manufacturing, the thermomechanical condition can differ significantly in fabrication of a single material tensile bar or sheet, as compared to fabrication of a mesoscale heterogeneous material system consisting of multiple materials. Therefore, it is inappropriate to first calibrate the parameters for each constituent material (e.g., using uniaxial tension or biaxial tension) and then apply the calibrated values to model the mesostructure. Instead, we directly measure the mesoscopic mechanical response of the heterogeneous material system. Through the error minimization algorithm that accounts for thousands of data points from in situ DIC and FEM, accurate material parameters can be determined.

E. Mesostructure design

After model calibration, mesostructure optimization can be performed, which involves the computation of a deformation-related objective function with each iteration adopting a different mesostructure design. While deformation can be simulated using finite element analysis, the mutation of the mesostructure design between neighboring iterations and the acceptance criteria of the new design are defined using global optimization algorithms. Because this process involves calling a ‘black box-like’ FEM solver, metaheuristic global optimization algorithms such as genetic algorithms, ^{Reference Tino Stankovic, Egan and Shea54} particle swarm optimization, ^{Reference Kennedy and Eberhart55} and simulated annealing ^{Reference Yu, Cross and Schuh39,Reference Cross, Woollam, Shademan and Schuh56} are used to identify the optimal mesostructure.

F. Validation

The last component in the proposed design framework is to experimentally validate the optimization results, so the figure of merit (i.e., objective function in optimization) can be compared for the optimal and conventional mesostructured material systems. The validation involves fabrication of a material system with various mesostructure designs using multi-material additive manufacturing as well as mechanical testing.

In this article, we demonstrate the proposed design framework using a rectangular beam-shaped material system consisting of a stiff and a compliant polymeric material, which is fabricated using dual extrusion-based additive manufacturing. Within the deformation range of this work, the stiff material displays linear elastic behavior, while the compliant material displays hyperelastic behavior. The effective parameters in the constitutive models are calibrated by matching the deformation data of ∼3000 surface material voxels from in situ DIC measurement and FEM, when the beam is subjected to three-point bending. The optimal mesostructure for the maximum bending resistance is then determined based on the calibrated model and validated using experiments.

A. Constitutive behavior of individual constituent materials

To understand the constitutive behavior of individual constituent materials, we first characterize Material S and Material C by uniaxial tension testing. Figure 4 shows that Material S displays elastic–plastic behavior with the linear regime up to axial strain of 6%, whereas Material C displays typical hyperelastic behavior with a nonlinear stress–stretch relationship. For Material S in the linear elastic regime, the Young’s modulus is measured as E _S = 190 MPa and the Poisson’s ratio is measured as ν_S = 0.46. For Material C, the deformation can be described using the Mooney–Rivlin model (five parameter) ^{Reference Rivlin and Saunders62} that gives the relationship between the strain energy density W _S and I ₁, I ₂, which are the first and second invariant of the unimodular component of the left Cauchy–Green deformation tensor ^{Reference Bower42} :

(1) $${W_{\rm{S}}} = {C_{10}}\left( {{I_1} - 3} \right) + {C_{01}}\left( {{I_2} - 3} \right) + {C_{20}}{\left( {{I_1} - 3} \right)^2} + {C_{02}}{\left( {{I_2} - 3} \right)^2} + {C_{11}}\left( {{I_1} - 3} \right)\left( {{I_2} - 3} \right)\quad ,$$

where C _ij (i, j = 0, 1, 2) are the five fitting parameters. Fitting the engineering stress versus stretch curve in Fig. 4(b) gives the five fitting parameters: C ₁₀ = −2.38, C ₀₁ = 5.37, C ₂₀ = 0.004, C ₀₂ = 0.69, and C ₁₁ = −0.012 MPa.

FIG. 4.

(a) The stress–strain curve (solid) for the stiff material from a uniaxial tension test plotted alongside the slope in the linear elastic region (dashed). (b) The stress–stretch curve (solid) obtained through a uniaxial tension test that demonstrates the hyperelastic nature of the compliant material, which is plotted alongside the fitted curve (dashed) using the Mooney–Rivlin model (five parameter).

As discussed in Sec. II.D, the values of E _S and C _ij (i, j = 0, 1, 2) determined from single material calibration in Fig. 4 cannot be used as effective material parameters for mesostructure design. In dual extrusion, the thermal and thermomechanical processes differ in fabrication of a single material as compared to fabrication of a beam with two polymers. This can lead to different microstructure and material parameters between the single and multi-material parts. In addition, the hyperelastic Material C is pressure-dependent, so the material parameters cannot be determined by a single uniaxial tension test.

B. Calibration using the voxel-level deformation data from DIC

We calibrate the effective parameters for Material S (Young’s modulus E _S and Poisson’s ratio ν_S) and Material C [fitting parameters C _ij (i, j = 0, 1, 2)] by matching the voxel-level deformation data from FEM to that from in situ DIC measurement. The FEM is conducted using COMSOL Multiphysics Solver (COMSOL Inc., Stockholm, Sweden) equipped with the nonlinear structural mechanics module. The best calibration condition is achieved when the average displacement difference between DIC and FEM is minimized for all the surface material voxels.

Using a mesostructured beam with 3/4 volume fraction of Material S and 1/4 volume fraction of Material C as the model system [see Fig. 5(a)], we conduct the calibration for different loads up to P = 800 N. Under these loads, we have confirmed that the beam deformation is reversible using DIC measurement so that the deformation of Material S is within the elastic range. For a given set of effective material parameters, we calculate the average displacement difference, $\overline {\Delta S}$ , and use it as the objective function, which is to be minimized. For the current study, each in situ DIC measurement gives the in-plane displacement for N ∼ 3000 surface material voxels in a mesostructured beam. The objective function can be written as

(2) $$\overline {\Delta S} = {{\sum\limits_{i = 1}^N {\sqrt {{{\left( {u_i^{{\rm{DIC}}} - u_i^{{\rm{FEM}}}} \right)}^2} + {{\left( {v_i^{{\rm{DIC}}} - v_i^{{\rm{FEM}}}} \right)}^2}} } } \over N}\quad ,$$

where u and v refer to the horizontal and vertical displacement, respectively. The superscript DIC and FEM refer to the data obtained from in situ DIC measurement and from FEM, respectively.

FIG. 5.

(a) An illustration of the mesostructured beam used for calibrating the material parameters. (b) A comparison of the vertical displacement at P = 500 N measured by DIC to (top) FEM prediction based on single material calibration and (bottom) FEM prediction based on mesostructure calibration.

The calibration is accomplished by searching for the best set of effective material parameters [E _S, ν_S, and C _ij (i, j = 0, 1, 2)] that minimizes $\overline {\Delta S}$ . Such an optimization process is performed by coupling the finite element solver with metaheuristic optimization algorithms via the MATLAB LiveLink software package. A simulated annealing algorithm is used to mutate the parent set of effective material parameters [E _S, ν_S, and C _ij (i, j = 0, 1, 2)] in each iteration k. ^{Reference Lamberti63,Reference Moita, Correia, Martins, Soares and Soares64} If the objective function decreases, the new set of material parameters will be accepted; if the objective function increases, the new set of material parameters will be accepted with a finite probability, depending on the ratio of the change in the objective function $\left[ {\overline {\Delta S} (k + 1) - \overline {\Delta S} (k)} \right]$ to a temperature parameter T (the value of T exponentially decreases in each iteration). This algorithm allows for occasional acceptance of inferior values so that the search can effectively escape from local minima and rapidly move through valleys in the search space. We use the fitting parameters in Fig. 4 as the starting variables; after ∼100 iterations, the objective function converges, and the effective material parameters are calibrated as E _S = 164 MPa, ν_S = 0.4, and C ₁₀ = −1.90, C ₀₁ = 2.68, C ₂₀ = 0.008, C ₀₂ = 0.35, C ₁₁ = −0.019 MPa.

Figure 5(b) compares the calibration approaches using the voxel-level deformation data in the mesostructured beam and using the single material data in Fig. 4. The left side shows the distribution of vertical displacement (v) in the DIC data under P = 500 N, whereas the right side shows the FEM prediction based on (upper) single material calibration and (lower) mesostructure calibration. Notably, mesostructure calibration using the voxel-level deformation data leads to a much more accurate prediction and should be used for mesostructure design in the optimization step. Quantitatively, the prediction error $\overline {\Delta S}$ decreases from ∼30% of the average total displacement with single material calibration to ∼10% with mesostructure calibration.

Although the calibration was performed using only one mesostructure design, our preliminary calibration exercises suggest a weak correlation between the mesostructure and calibration results. This suggests that the influence on the optimization results is also inconsequential. If the correlation between calibration results and mesostructure design is significant, a data-driven approach needs to be used to quantify this correlation, and to establish a surrogate model. ^{Reference Higdon, Gattiker, Williams and Rightly65,Reference Besag, Green, Higdon and Mengersen66} However, even a strong correlation will not affect the structure of the proposed framework; once the correlation is properly determined, it can be inserted as an additional procedure during the iterative optimization.

A. Computational optimization

With the calibrated model, we can design the mesostructure for a given application. The goal here is to determine the optimal distribution of Material S and Material C for the maximum bending resistance, while maintaining 3/4 volume fraction of Material S and 1/4 volume fraction of Material C as in the model system for calibration. The objective function that is to be minimized is defined by the beam center deflection. The beam is then divided into 80 material domains, each with dimensions of 5 × 5 × 20 mm. The best material distribution is found using a simulated annealing algorithm through the COMSOL–MATLAB LiveLink software package, a similar approach to what is used for model calibration. The simulated annealing algorithm mutates the initial distribution for every iteration by randomly selecting 30 domains on the left half of the beam and assigning them the calibrated parameters of Material S while assigning the remaining 10 domains the calibrated parameters of Material C. These domains are then reflected across the center line to provide a symmetrical part for every iteration. By forcing every predicted design to be symmetrical, the number of iterations necessary to reach the optimal design is significantly reduced.

Even with different initial material distributions, the objective function converges at the same value after 1000–1500 iterations, as shown in Fig. 6(a). The best distribution of Material S and Material C determined by computational optimization is shown in Fig. 6(b), which has a beam center deflection of 2.85 mm at 500 N. This result is physically meaningful and intuitive; a heavier distribution of the stiff polymer near the loading regions maximizes the resistance to bending. The von Mises stress distribution in Fig. 6(c) shows that the presence of the stiff polymer effectively shields the compliant polymer from high local stresses in the optimal mesostructure design.

FIG. 6.

(a) Evolution of the objective function in simulated annealing, with two different initial material distributions. (b) The optimized mesostructure design to minimize the deflection during three-point bending. (c) An illustration of the von Mises stress distribution in the optimized structure, showing the concentration of stress in the stiff material domains.

B. Experimental validation

To validate the computational optimization results, three beams with different mesostructures but with the same volume fractions of Material S and Material C, are manufactured and subjected to a three-point bending test. The first sample is the optimal design shown in Fig. 6, the second sample is the design used for model calibration in Sec. V, and the third sample is a layered structure. Figure 7(a) shows the printed sample surface, with the regions of Material C highlighted in gray. The load versus displacement for each sample is shown in Fig. 7(b), which confirms that the optimized structure (the first sample) yields the least amount of deflection at 500 N. Furthermore, the experimentally measured deflection at the center point, 2.81 mm, is very close to the predicted value of 2.85 mm from the calibrated model. By contrast, the prediction using the uniaxial, single material calibration yielded a deflection of 2.41 mm, again demonstrating the importance of mesostructure calibration. The second sample also has high bending resistance, though still lower than the first sample below 500 N, with the measured beam center deflection of 2.95 mm. This result is not surprising, because this mesostructure design also has a high concentration of the stiff polymer near the loading regions to resist bending. The significance of the optimization can be more readily observed when comparing the optimized sample to the sample with a layered structure, which experiences over 7 mm of deflection.

FIG. 7.

(a) Pictures of three manufactured beams: (from top to bottom) the first sample with the optimized mesostructure; the second sample with the mesostructure used for calibration; the third sample with a layered mesostructure. The material domains of the compliant polymer are highlighted in gray. (b) The load versus displacement curves for the three samples, showing the minimum deflection in the optimized beam.

It should be noted that the load–displacement curves for the first and second design intersect at ∼600 N. From this point on, the second design outperforms the optimized design. Our optimization was performed at 500 N. While the optimal design was the best for the particular loading condition, it is not necessarily optimized for all bending conditions. Even so, this demonstrates the ability of the framework to achieve high performance for a specific loading condition based on the intended application.