Additive manufacturing has rapidly gained popularity, both in industry and in academia, mainly because of the possibility of fabricating complex geometries in a relatively straightforward way. Currently, a wide range of different materials that can be manufactured, be it rigid or elastic polymers, metals, ceramics, or even comestibles (Lipton et al., 2015; Godoi et al., 2016; Maroni et al., 2017), are being proposed.

Recent studies have been investigating the effect of patterned surfaces and lattice structures to archive new designs through the so-called metamaterials, allowing the tuning of the mechanical properties of printed structures (Franco-Martínez et al., 2022; Han and Wei, 2022). Furthermore, new printing technologies employ materials responsive to various stimuli, such as temperature or electrical changes in the environment. This allows for the creation of complex geometries, otherwise not manufacturable, or functional structures, which can actively interact with their environment (Hann et al., 2020; Andreu et al., 2021). Not only is there a large selection of printable materials but also different printing methods (Stansbury and Idacavage, 2016). One of the most widespread manufacturing processes is stereolithography, which is the focus of this study. In this process, a liquid photopolymer is irradiated on a layer-by-layer basis, with UV light, which causes it to cure and therefore create structural stability. Stereolithography, often regarded as the pioneering 3D printing technique, involves scanning the intended printing layer with a laser (Su et al., 2018). In contrast, digital light processing (DLP) cures the entire layer at once by exposing the desired area through mirrors or a display. The concept and the printing materials used are basically the same; however, the two methods differ in printing time and printing result, depending on the available printer (Zhang et al., 2019; Unkovskiy et al., 2021).

So far, current research on DLP has focused mostly on the geometrical accuracy and surface finish properties but less on the mechanical properties of the printed parts. To create structures with adaptable dimensions, weight and precisely defined deflections under load materials have been developed to cover a range of elasticity (McKittrick et al., 2010). However, particularly, when it comes to mimicking a natural tissue or building prostheses and scaffolds for tissue engineering (Melchels et al., 2010), raw materials with suitable mechanical properties might not be available off the shelf.

It would therefore be advantageous to mix different base materials and obtain “intermediate” properties depending on their mixture ratios. Depending on the available printing system, the base materials can be mixed before or during the printing process (Tee et al., 2020; Jandyal et al., 2022). While the PolyJet technology relies on the mixture during the printing process and has been extensively investigated, the mixing of materials before printing and its effect on final mechanical properties have not been sufficiently investigated yet. The printing process also determines whether certain gradients of mixing ratios can be incorporated (Ituarte et al., 2019; Zhang et al., 2020; Niu et al., 2022).

Studies on material designing to reach certain material properties, e.g., by changing the composition of the photocurable resin, either by creating completely new material compositions or with additives in a preexisting material exist (Ligon et al., 2015; Borrello et al., 2018; Slapnik and Pulko, 2021). Moreover, the material properties depend on the properties of the polymer that influence curing, like the absorbance spectra, UV-light wavelength used, and temperature during fabrication (Fuh et al., 1999; Bennett, 2017; Hofstetter et al., 2018). Furthermore, some printers allow the modification of properties of the printed object via the printing process. For example, it is possible to use a photopolymer with two photoinitiators that cure at different exposure frequencies and produce structures with different stiffnesses (Cazin et al., 2022). However, especially in the case of photopolymers, where longer exposure times usually result in higher stiffnesses, it should be noted that the materials still undergo changes after the printing process. This property can be exploited during post-curing but must be taken into account when using the printed part since most photopolymers used currently react with UV components that are also present in daylight.

Most of the existing studies employ an experimental or simulative approach, and only a small number of publications deal with mathematical models that predict the properties of the 3D-printed parts, estimating the curing grade and the associated stiffness (Yang et al., 2019).

The present study deals with additively manufactured multimaterials and their respective elastic properties, particularly the effects of mixing ratios of commercially available resins on a range of elasticities, including the effects of post-curing. Specimens produced using an upside-down stereolithography 3D printer and their respective measurements and multimaterial prediction methods are shown. The chosen method includes blending of two primary materials before the initiation of the printing process, and therefore, homogeneous samples were obtained for each printed set of specimens. To the best of our knowledge, this is the first paper that investigates the elastic properties of these multimaterials and describes the mixing behavior in both the as-printed and post-cured states.

This study showed that as-printed and post-cured samples of mixed materials exhibit different behaviors related to the primary materials. The prediction by averaging the pure materials was within the range of the measurements for eight out of nine sample sets of the post-cured materials. However, the as-printed samples have a higher Young’s modulus than that predicted by linearly interpolating the pure materials. This is most likely due to the different curing grades of the materials tested (Nowacki et al., 2021; Lang et al., 2022). In addition, there is likely to be a change in the cure behavior when the materials are mixed.

A relevant aspect of reliable and repeatable 3D printing processes is the geometric dimensions of the printed parts. The geometries of the samples show a quite stable process, although the placement on the build platform has to be considered, since the printer resolution is 51 µm per pixel. In addition to some scatter, a 51 µm difference represents the variation in sizes among certain specimens within the same sample set, with some being larger in one direction than others. Knowing that the size of the samples does not correspond to an integer number of pixels, the slicer software had to select one of the following numbers of pixels for the width: 48 px (2.448 mm), 49 px (2.499 mm), or 50 px (2.550 mm) and for the height: 39 px (1.989 mm), 40 px (2.040 mm), or 41 px (2.091 mm), not considering the possible pixel edges in the stated dimensions. Naturally, the size of the samples depends on the number of pixels that are switched on, which again depends on how the slicing software interprets the position on the building plate. This grouping on several used pixels can be well observed, particularly in the scatter plot of the post-cured specimen, as shown in Figure 3.

Due to the pixel alignment on the building platform, a correlation between the width and height could be observed. The correlation coefficient between the width and height of the specimens for all samples is ρ _all = 0.5329, while the correlation coefficients in the three groups are much higher: ρ ₁ = 0.8333, ρ ₂ = 0.8942, and ρ ₃ = 0.8329. This indicates that the luminosity of the UV-light source is nonhomogeneous, and, therefore, some samples are more exposed than others, which results in larger geometric features.

Lastly, the geometry also depends on the light absorption and curing properties of the photopolymer itself; for example, the post-cured pure beige samples are on average larger than the post-cured blue samples (h _beige = 2.0834 ± 0.019 mm > h _blue = 2.0633 ± 0.018 mm, two-sample t-test p-value for non-equal means p = 0.0015).

Geometric mismatches due to the limited resolution in DLP stereolithography printers and the ill placement on the building plate can be treated in two ways. First, the positioning of identical geometries could be arranged with the same offset on the pixel grid of the matrix. However, this feature has to be supported during the slicing of the geometries. The slicer that comes with the printer, as well as most of the other freely available slicers, allows positioning on the building plate but does not offer information on pixels that are exactly turned on or off. Second, the pixels on the display are either activated or deactivated. Instead, employing pixels which are not fully illuminated (grayscale template) could enhance the overall geometry, both in terms of resolution (Zhou and Chen, 2012) and overhangs or rounded geometries (Mostafa et al., 2017; Guven et al., 2022).

Also important to consider before the fabrication is, that the elastic properties correlate with the curing state of the prints. The as-printed specimens seem not to have reached a fully cured state. This could explain the higher Young’s moduli for multimaterials, assuming that these materials have reached an advanced curing state after printing compared to the pure materials. Particularly for the blue-clear mixtures, the multimaterials showed less change in the elastic properties during post-curing than the pure materials. The supplier states a photoinitiator content of 2%–5%, and a higher photoinitiator content in the softer base material could explain the reason, why mixing with a harder base material results in a higher Young’s modulus of the multimaterial, especially since this effect is not present in post-cured samples, where the remaining photoinitiator gets activated during the post-curing process. Considering this, a refined model for predicting mixed material properties would not only include the base materials and their mixing ratio but also include information about the fabrication process and curing status of the materials.

Measurements also showed that post-cured specimens are stiffer and have higher flexural strength but break at lower deflection. Central fractures without secondary fracture origins are an indicative of the materials being well mixed, meaning that coarse material transitions are not present in the sample.

In this study, multimaterials from DLP stereolithography 3D printing systems were investigated for their mechanical and geometrical properties. For DLP-printed multimaterials, where various primary materials are blended before the printing process, it is reasonable to assume linearly interpolated Young’s moduli, particularly for fully cured 3D prints, which are typically employed. Only for some special applications, where non-post-cured samples are used, one needs to find a different material model or print some samples for preliminary tests. Since the material properties are not dependent on how the resin is exposed to UV light, these findings are expected to be generalizable to other types of SLA printers but not to other printing methods.

A large number of measurements of different materials have been recorded. These results lay a foundation for further studies to create elaborate simulation and prediction models for multimaterial stereolithography 3D printing. Precise predictions and planning of 3D-printed elastic properties will allow the fabrication of individualized and optimized substitutes, like tailored prostheses in healthcare or equipment adapted for athletes in professional sports.