# PeakForce-QNM AFM measurement of photopolymerization-induced surface wrinkles in 3D printed objects

Kübra Sekmen, Xuyang Chang, Simon Hallais, Kostas Danas, Andrei Constantinescu

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Kübra Sekmen, Xuyang Chang, Simon Hallais, Kostas Danas, Andrei Constantinescu. PeakForce-QNM AFM measurement of photopolymerization-induced surface wrinkles in 3D printed objects. 30ème Colloque National sur la Déformation des Polymères Solides (DEPOS 30), Sep 2021, Mandelieu-La Napoule, France. hal-05002503

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https://hal.science/hal-05002503v1

Submitted on 24 Mar 2025

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# PeakForce-QNM AFM measurement of photopolymerization-induced surface wrinkles in 3D printed objects

K. Sekmen¹,*, X. Chang¹, S. Hallais¹, K. Danas¹, and A. Constantinescu¹,*

¹ Laboratoire de Mécanique des Solides, CNRS, École Polytechnique, Institut Polytechnique de Paris, 91128 Palaiseau, France

*kubra.sekmen@polytechnique.edu; andrei constantinescu@polytechnique.edu

**Keywords:** Photopolymerization, DLP printing, PeakForce-QNM AFM, Wrinkling, Instability

*Digital light processing* (DLP) is one of the main additive manufacturing (AM) techniques that robustly builds 3D objects in a layer-by-layer fashion, via simultaneous in-plane photo-polymerization¹,². Compared to stereolithography (SLA), in DLP printing, each layer of liquid resin is exposed to a spatial UV-light distribution prescribed by the image patterns, thus applying the curing simultaneously at any given volumetric pixel (voxel) in the exposure plane. This in-plane parallelization of curing makes DLP-based printing extremely efficient and interesting for manufacturing products with large-scale in-plane dimensions, since the overall printing time of DLP techniques now depends only on the out-of-plane dimensionality instead of all three dimensions.

However, in practice, during DLP printing, the previously printed layer is continuously exposed to non-zero UV light radiation, resulting in continuous photopolymerization. Consequently, the robust layer-by-layer DLP protocol introduces notable heterogeneity in the degree of curing along the out-of-plane direction.

Coupled with solvent swelling caused by isopropanol cleaning after printing, the layer-wise heterogeneity in curing leads to the formation of surface wrinkle patterns because the stiffer top layer tends to relax excessive stresses by introducing out-of-plane strain through buckling. The presence of nanoscopic wrinkles can be seen as a precursor to critical cracks and dimensional variations that are frequently reported in DLP-printed products.

Meanwhile, to the author's best knowledge, the relationship between process parameters, the spatial heterogeneity in curing, material properties, and the formation of wrinkle patterns during DLP printing is not fully established. The lack of a well-established model remains a major obstacle for further industrial applications of DLP printing.

In the present work, commercial acrylate-based photosensitive resin PR48 (Colorado Photopolymer Solutions, USA) is used as a printing material. 3D printed objects are fabricated using an Autodesk Ember DLP 3D printer equipped with a digital micromirror device and a UV LED light source. The printer has a resolution of 50 × 50 µm², corresponding to one pixel projected by the digital micromirror, and a processing layer thickness range of 10 – 100 µm.

The objective of this presentation is to conduct an intuitive exploration of the wrinkle formation mechanism during the aforementioned DLP printing process using a recently developed atomic force microscopy mode, PeakForce AFM mode (PF-QNM). As a new expansion of Pulse Force mode, PF-QNM allows for the acquisition of pixel-to-pixel force-indentation responses, enabling measurement of properties such as stiffness, dissipation, and adhesion, along with topographical features, at the same spatial coordinates within a short acquisition time. The comparison between material properties and morphological features provides an initial inspection of wrinkle formation inside a completed DLP-printed voxel.

Moreover, it should be emphasized that the acrylate-based resin is non-linear and viscous, and its material properties vary as a function of the degree of curing. Hence, the voluminous dataset of
printed pixels poses great challenges to the built-in AFM modulus-based segmentation tools. As an alternative solution, a model-reduction technique (POD) developed by Chang et al.³ can be used to achieve a low-dimensional representation of the initial data. It will be shown that the model-reduction technique is highly efficient in hierarchically organizing such a large dataset, allowing the decoupling of phase nature from topographical features.

The resulting force-indentation curves are analyzed to extract topography and adhesion properties from the output of Bruker's post-processing software. As shown in the map of topography in Fig. 1, nanoscopic wrinkling patterns are observed on the surface in the form of 'valleys' and 'hills' due to a potential mismatch between two different layers. More interestingly, most topographical features with a strong surface gradient lead to a decrease in adhesion properties. On the contrary, for all the pixels located in the valleys, $h(x) \le 990$ nm, $\forall x$, adhesion surprisingly increases compared to the region of flat 'hills', $h(x) \ge 1000$ nm, $\forall x$. The contrast between adhesion and topographical features may suggest that even on the top layer, the degree of curing is still highly heterogeneous.

As for our future study, we are currently working on combining the model-reduction analysis with PF-QNM AFM measurements. This low-dimensional representation of the initial dataset can enable an intuitive pattern recognition analysis to identify patterns with the most similarity (using Gaussian mixture model fitting). The proposed post-processing of the resulting datasets will offer novel insight into the evolution of mechanical parameters during DLP printing.

<figure><img src="image_3.png" /><figcaption>Figure 1: Map of topography (left) and adhesion (right) of the DLP printed pixel</figcaption></figure>

## References

[1] D. K. Patel, A. H. Sakhaei, M. Layani, B. Zhang, Q. Ge, S. Magdassi, Advanced Materials, 2017, Vol 29 (15), 1-7.

[2] Q. Mu, L. Wang, C. K. Dunn, X. Kuang, F. Duan, Z. Zhang, H. J. Qi, T. Wang, Additive Manufacturing, 2017, Vol 18, 74-83.

[3] X. Chang, S. Hallais, S. Roux, K. Danas, Measurement Science and Technology, 2021, In press.