# Instrumentation of silicone liquid deposition modeling by extrusion: Introduction and evaluation of laser profilometry and associated indicators for supervision

Loïc Mosser, Lennart Rubbert, Laurent Barbé, Pierre Renaud

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## To cite this version:

Loïc Mosser, Lennart Rubbert, Laurent Barbé, Pierre Renaud. Instrumentation of silicone liquid deposition modeling by extrusion: Introduction and evaluation of laser profilometry and associated indicators for supervision. Additive Manufacturing, 2025, 105, pp.104779. 10.1016/j.addma.2025.104779. hal-05061177

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Submitted on 9 May 2025

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Research paper

# Instrumentation of silicone liquid deposition modeling by extrusion: Introduction and evaluation of laser profilometry and associated indicators for supervision

Loïc Mosser<sup>ID*</sup>, Lennart Rubbert<sup>ID</sup>, Laurent Barbé<sup>ID</sup>, Pierre Renaud<sup>ID</sup>

ICube Laboratory, University of Strasbourg, CNRS, INSA Strasbourg, INSERM, Strasbourg, 67000, France

## ARTICLE INFO

Dataset link: entrepot.recherche.data.gouv.fr

**Keywords:**

Silicone additive manufacturing
Liquid deposition modeling
Laser profilometry
Process monitoring

## ABSTRACT

While silicone printing by extrusion is a promising technique, part production is still a challenge when support-free printing is considered. In this paper, we consider three main causes of defects, namely the management of silicone flow rate during printing, the part deformation under its own weight before polymerization and the deformations due to the nozzle-layer interactions during printing. An instrumentation strategy is here proposed to monitor layer deposition. Our approach, based on laser profilometry, is detailed with open source software to control printing and scanning. The process to build point clouds and extract data is presented. More importantly, indicators are introduced to build metrics related to the current main causes of printing failure, using prior geometric information on the planned layers. Through experimental evaluation, the adequacy of the indicators and their complementarity is discussed. This introduction of new indicators opens ways to implement efficient silicone extrusion supervision.

## 1. Introduction

Silicone liquid deposition modeling is gaining in interest, as it opens new ways to manufacture devices such as patient-specific prostheses [1], microfluidic systems [2,3] or to make flexible actuators [4,5] for soft robotics. Silicone additive manufacturing processes can be divided into four categories [6]: extrusion, vat polymerization, freeform reversible embedding and material jetting. Silicone extrusion is particularly of interest, as the range of printable silicones is wide: UV-cure [7], moisture-cure [8], HTV [9] and RTV [10] silicones were shown to be printable using extrusion. High-viscosity silicones can be deposited, whereas a technique such as material jetting is limited [6]. Printing without support was also demonstrated using extrusion [8,10], which is a critical feature when internal channels are needed in microfluidics or soft robotics. More generally, extrusion printing of soft materials such as hydrogels, polymers or bio-inks [11] is gaining in interest. Improvement of silicone extrusion usability could hence be beneficial also for other soft materials printing.

Printing using silicone extrusion remains indeed quite delicate. In the literature, three main causes of defects and failure have been outlined. First, silicone rheological behavior is complex, with a non-Newtonian behavior of fresh silicone [6], die swelling [12], and dependence of the material rheology on environmental conditions, such as temperature [13]. Adjustment of silicone flow during deposition is

thus difficult. Second, instability during the printing can occur because layers are added before the end of the polymerization process. The part weight can be responsible for geometric deviations and even part destruction [14]. Third, the interaction forces between the printer nozzle and the part under construction can also be significant, as analyzed in [8]. Interaction forces are a function of the material dispensing rate, and they can generate deformation of the printed part, or printing failure.

Some previous research efforts were dedicated to the prediction of part printability [14] with the elaboration of a specific criterion such as limit overhang angles. The printability prediction proposed in [14] is focused on issues related to the part weight. It can be used prior to printing, and possibly also to adjust a part design. However, in contexts such as microfluidics or robotics, it is difficult to modify a design without affecting the function. In addition, the indicator does not take into account the three failure causes outlined above. As a consequence, we consider in this paper the development of a supervision strategy for silicone extrusion printing.

To our knowledge, no such specific supervision strategy has been proposed. Various supervision strategies have however been proposed for other materials and processes, that differ by (i) the type of instrumentation, (ii) the observation's scale, which can be at the filament,

<footnote>* Corresponding author.
E-mail address: mosserl@unistra.fr (L. Mosser).</footnote>
layer, or part scale, and (iii) the use of prior information issued from the CAD description of the part, as detailed hereafter. Concerning the instrumentation, several works are based on sensor integration in the printer, to extract some information from the printer motion, using encoders mounted on the printer axes (Zmorph, Stella Move), or other indirect characteristics, e.g. vibration sensors [15]. The use of vision to directly monitor part creation has been quite often considered, and at all scales of observation: for nozzle tracking [16], to monitor the layer fabrication [17] (Inkbit) or the complete object [18]. An interesting alternate instrumentation is laser profilometry. Resolution, which can be of a few micrometers, was exploited to monitor the extruded filament [19] or layers [20]. In the latter work, the authors make use of both the acquired actual layer geometry and the 3D model to compute the presence and size of defects.

Even though laser profilometry can be limited by shadowing effects and edge effects [21], its interest has led to several interesting developments of supervision strategies. In the case of material jetting [22], the actual topography of the deposited layer is measured to compute the full layout of the next layer. The approach can however become complex and challenging in terms of implementation for large parts. For Fused Deposition Modeling (FDM) [23], Wire-arc Additive Manufacturing (WAM) [24] and Directed Energy Deposition (DED) [25], the use of classifiers enables the detection of incorrect printing conditions. This approach is interesting for effective monitoring, but not very useful for correcting printing conditions, as it involves non-quantitative indicators. In [26,27], layer observation is performed by scanning using a profilometer. Metrics are then developed for rigid parts, taking into account the particularities of the process: height variation, disparity in the layer, or, in the case of Laser Sintering (LS) for example, the curling phenomenon. In our view, this is a logical and interesting approach. Furthermore, in [28], profilometry was used for silicone Direct Ink Writing (DIW), demonstrating the feasibility of the measurement. Criteria have been introduced for layer height and appearance, but no investigation of the difficulties associated with support-free silicone printing, with possible deviations from nominal geometry, and the phenomena highlighted earlier.

Given these works, we propose several contributions through the development of layer observation with laser profilometry, building indicators for the supervision of part printing. Our approach is to analyze layers, given the resolution offered by profilometry. We consider the construction of quantitative indicators, i.e. metrics, based on the process and the types of defects mentioned above. In our view, it is therefore important to use both raw measurement data and CAD data to construct indicators. Therefore, our contributions are threefold: (i) the introduction of an instrumented silicone printing platform with open-source control software to ease the development of silicone printing, (ii) the introduction of specific indicators for the supervision of silicone part printing, and (iii) an experimental investigation with a discussion of the relevance of these supervision criteria, using test specimen geometries specific to silicone printing.

In Section 2, the methods to capture data using laser profilometry for computing indicators are discussed first, before the introduction of the proposed supervision indicators and the proposed instrumented platform. The experimental assessment is described in Section 3, with first the introduction of specific test parts, and then the discussion on the relevance of the proposed supervision indicators.

# 2. Materials & methods

## 2.1. Acquisition strategy and data processing

### 2.1.1. Proposed instrumented silicone printer

The conceptual view of the instrumented silicone printer is provided in Fig. 1. The silicone deposition is managed using a conventional cartesian architecture. Instrumentation is being integrated with a laser profilometer placed on top of the printer. Such a type of sensor provides

<figure><img src="image_3.png" /><figcaption>Fig. 1. Schematic representation of an instrumented silicone printer.</figcaption></figure>

an altitude measurement along the projected laser line (red dotted line in Fig. 1). A 3D point cloud is thus constituted by scanning the last printed layer, using the printing plate motion along the y axis. Even though other kinematics can be considered, one may note that such a kinematic decomposition simplifies the implementation: a single motion is needed for layer scanning, along the y axis as illustrated in Fig. 1.

### 2.1.2. Acquisition process

The sequence for scanning a layer is decomposed in Fig. 2. At the end of the layer deposition, a preparation phase is first needed (Fig. 2-a, b) with retraction of the silicone filament, to avoid material spillage on the printing plate, and nozzle displacement to clear the profilometer field of view. The printing plate is then moved to a predefined position (Fig. 2-c), to have a common reference for all scans during part production. The scanning phase starts, by performing acquisitions with the profilometer while the y axis is actuated (Fig. 2-e and f). After scanning the layer, the nozzle returns to its initial position so the new layer production can start (Fig. 2-g).

### 2.1.3. Point cloud registration and segmentation

**Overall process:** By capturing profiles regularly at the end of layers' printing while the printing plate is in motion, a dataset is built with the recorded profiles and the associated printing plate positions. So a 3D point cloud can be generated, initially expressed in the frame $R_m$ (Fig. 2-e) of the profilometer, distinct from the printer base frame, while our prior information on layer position and geometry is known in this base frame. A first data processing step is thus to register the dataset in the frame $R_p$ (Fig. 2-f) attached to the printing plate.

Second, the use of prior geometric information issued from the CAD design of the part requires to be able to superimpose the actual geometry of the layer with its planned geometry. The latter needs then to be correctly repositioned in the printing plane with respect to the dataset.

**Registration:**

Let $T_{R_m \to R_p}$ be the rigid-body transformation between frames $R_m$ and $R_p$. The registration is performed in four steps using a scan of the printing plate, achieved before the beginning of printing. This scan is used in the following way to build the frame $R_p(x_p, y_p, z_p)$:

1. A least-square method is used to compute the plane that best describes the printing plate. Plane fitting is performed for points located close to the planned part position. The corresponding area of interest is defined by the user. The normal to the plane is defined as the vector $z_p$ of the frame $R_p$.
<figure><img src="image_4.png" /><figcaption>Fig. 2. Illustration of the method for acquiring laser profile lines over the entire layer produced.</figcaption></figure>

2. The $x_p$ vector is constructed using the first line of measurement. The orthogonal projection of this line in the constructed plane gives the direction of the vector. It is then computed as a unit vector.

3. The $y_p$ vector is calculated as $y_p = z_p \wedge x_p$.

4. Finally, the translation component along $z_m$ of the transformation $T_{R_m \to R_p}$ is chosen to have the printing plate at zero altitude.

### Repositioning of printed part:

The scanned points can be used to determine the part position on the printing plate after printing of any layer. If part deformation occurs due to silicone printing issues, errors could however be introduced in the determination of the part position. To avoid that, estimation of part position is achieved using one of the initial layers. The process is composed of 3 steps (Fig. 3): (i) definition by the user on the dataset of a region of interest (ROI) belonging to the printed layer; (ii) computation of the average altitude $z_{mean}$ of the selected points; (iii) extraction of all points with this altitude. For this last step, it is necessary to take into account the sensor vertical resolution. So the extraction is performed while considering a threshold $\epsilon_v$ so that the condition for the selection of a point of altitude $z$ is $|z - z_{mean}| \le \epsilon_v$.

Then, the layer position on the printing plate can be determined by computing the barycenter $G$ (Fig. 3-c) of all extracted points. Using the barycenter of the corresponding layer as obtained after slicing to reposition it, data from measurements and from the slicing can be directly compared.

## 2.2. Definition of silicone printing indicators for supervision

The proposed approach is to exploit prior geometric information available after part slicing. Each layer geometry is known, and it can be compared to the printed layer. To do so, a mask $\Omega_j$ is generated

for each planned layer of index $j$ (Fig. 4-a, pink). Given the possible part deformation during printing due to its weight, a second mask $\bar{\Omega}_j$ is generated, which is complementary to the first one (Fig. 4-a, blue). The presence of silicone in the volume delimited by that mask is indeed a sign of a printing defect. Generation of these masks requires to take into account the uncertainties due to the use of laser profilometry, identified in [20]. Therefore, for each layer $j$, three distinct domains are in the end generated:

1. the domain $\Omega_j$ where the layer should be located

2. a rejection domain, defined at the limit of $\Omega_j$ considering the in-plane sensor uncertainty $\epsilon_h$

3. the domain $\bar{\Omega}_j$, which is the rest of the domain

Points belonging to domain $\Omega_j$ are grouped together to form a point cloud $\Gamma_{\Omega_j}$. Points belonging to the $\bar{\Omega}_j$ domain are similarly grouped together in the $\Gamma_{\bar{\Omega}_j}$ point cloud (Fig. 4-c). To supervise the printing process, we define accordingly 3 indicators designated as $I_1, I_2$ and $I_3$. $I_1$ and $I_2$ are based on $\Gamma_{\bar{\Omega}_j}$ and $I_3$ is based on $\Gamma_{\Omega_j}$.

The indicator $I_1$ is built to monitor specifically the height during printing. This indicator is a function of the observed layer $j$, so $I_1 = I_1(j)$. Let $D(j)$ be the planned altitude of layer $j$, which is simply $D(j) = j \cdot h$ with $h$ being the layer thickness. Correct printing should be observed with an average altitude of points belonging to the layer $j$ being equal to $D(j)$. With $N_{\Omega_j}$ being the number of points $P_i, i \in [1, N_{\Omega_j}]$ belonging to the point cloud $\Gamma_{\Omega_j}$, and $z'_j$ being the altitude of $P_i$, one has

$$I_1(j) = Z(j) - D(j) \text{ with } Z(j) = \frac{1}{N_{\Omega_j}} \sum_{i=1}^{N_{\Omega_j}} z'_j \quad (1)$$

The nominal value of $I_1$ is zero. The motivation here is to monitor the effective flow rate of silicone through the part elevation during
<figure><img src="image_5.png" /><figcaption>Fig. 3. Illustration of the three-step process for measuring part position in the XY plane.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 4. Illustration of the segmentation process based on the creation of the $\Omega_j$ and $\bar{\Omega}_j$ using the sliced part (a) to obtain the point clouds $\Gamma_{\Omega_j}$ and $\Gamma_{\bar{\Omega}_j}$.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 5. Illustration of defects that can affect parts, with $\Gamma_{\Omega_j}$ and $\Gamma_{\bar{\Omega}_j}$ point clouds.</figcaption></figure>

printing, and also to observe vertical deviation due to part deformation before polymerization that could be introduced by part weight or nozzle-layer interaction forces (Fig. 5-a and b).

The indicator $I_2$ is built to monitor specifically the quality of the surface after the creation of the jth layer. It is also a function of the layer index j: $I_2 = I_2(j)$. Focus is here given to the standard deviation of the z-coordinate of the $N_{\bar{\Omega}_j}$ points belonging to $\Gamma_{\bar{\Omega}_j}$, and it is computed as a root mean square error:

$$I_2(j) = \sqrt{\frac{1}{N_{\bar{\Omega}_j}} \cdot \sum_{i=1}^{N_{\bar{\Omega}_j}} (z'_j - Z(j))^2} \quad (2)$$

The nominal value of $I_2$ is also zero. This indicator is specifically intended to detect irregularities during layer creation, due to nozzle-layer interactions, and also an inadequate silicone flow, that might cause shape defects without affecting the mean altitude of the layer (Fig. 5-b and c).

Last, the indicator $I_3$ is computed using the set of points $P_i$, $i \in [1, N_{\bar{\Omega}_j}]$ belonging to the point cloud $\Gamma_{\bar{\Omega}_j}$. This set of points includes a portion of the printing plate, and possibly regions of some previously printed layers, as shown schematically in Fig. 5-a and b. The presence

of such points is not a sign of printing difficulties. So the indicator is built using the available prior information on the altitude of the printed layer. The indicator $I_3$ is computed as the number of points $P_i$ belonging to the point cloud $\Gamma_{\bar{\Omega}_j}$ whose altitude $z_i$ is equal to the mean altitude of points in $\Gamma_{\bar{\Omega}_j}$. It is intended to detect situations related to part deformations, either due to the part weight, or the forces applied by the nozzle on the part (Fig. 5-b, c, d and e). Taking into account the sensor vertical resolution $\epsilon_v$, one has then:

$$I_3(j) = \text{card} \left( \left\{ P_i \in \Gamma_{\bar{\Omega}_j} \mid |z_i - Z_j| \le \epsilon_v \right\} \right) \quad (3)$$

## 2.3. Printing platform

The structure of a FDM printer (Tobeca 3, Tobeca) is used to build the setup. A precision progressive cavity pump (Vipro-head 5, ViscoTec) is mounted for extrusion management (Fig. 6), as considered in other research [29] and commercial platforms (Lynxter S300x, Innovatiq LiQ 320). Maximum dispensing flow is 6 mL/min with a dosing resolution of 0.004 mL (manufacturer specifications). Details of its integration into the printing platform and extrusion flow rate management are given in Appendix A.
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Description of the implementation, highlighting the main elements of the architecture and the communications between the laser profilometer, the 3D printing structure and the computer.</figcaption></figure>

A Gocator 2140-D laser profile sensor (Fig. 6) is installed in the system, with a nominal resolution along the profile line of 0.3 mm, and a 30-µm vertical resolution (manufacturer specifications). Colored polypropylene is used as printing plates because this material is both compatible in terms of silicone adhesion and reflection properties for use with the profilometer. Line acquisitions are performed every 0.3 mm, to get equal resolution of the 3D point cloud in the x and y directions.

We used a reference prismatic part of 20 mm to check our measuring tool and set tolerances $ε_v$ and $ε_h$. The part is attached to the printing plate, where a set of scans is performed. The dispersion of point heights is used to set $ε_v$ to 0.24 mm. In this case, 95% of the points associated with the surface visible to the sensor can be retained despite measurement noise. In addition, we set $ε_h$ to 0.6 mm, which removes points on the same surface impacted by the edge effect specific to laser profilometry.

Printer motions, nozzle dispensing system and profilometer acquisition are all synchronized using ROS2-Humble running under Ubuntu 22.04 LTS. The developed code is available under an open-source license (github). It has been designed as a ROS interface for Marlin (source) firmware which is a community standard.

CuraEngine slicer is used for the generation of the nozzle trajectories. Printing speed is set to 7 mm/s following [5], so dynamic effects on the finished part remain low. Other slicer parameters are provided below.

# 3. Experimental assessment of indicators

## 3.1. Design of test parts

Our objective is to discuss the relevance of the proposed indicators for the main silicone printing sources of defects, outlined in Section 1. To do so, four test parts are designed. Their schematic representations are shown in Fig. 7.

The part dimensions are chosen with respect to the printing conditions. We use an alkoxy-based moisture-cure silicone (A8 Pro silicone, Würth Belux) and a standard nozzle diameter of 0.41 mm (Optimum Smoothflow, Nordson EFD) following [29]. Layer height $h$ is set at 0.41 mm. The temperature is controlled with the room air conditioning system and set at 18 °C. The printer is placed in an enclosure with measurement of humidity, that was stable during all experiments at a 60% value.

Part #1 (Fig. 7-a) is a 50-mm high part with a 15-mm square base. Two samples are printed simultaneously. Their production differs in the extrusion conditions: for the second one, an increase by 20% of

silicone flow rate is set on purpose during the production of layers 20 to 40 of the part, out of a total of 122 layers. There is no complexity in the geometry of the part, the goal being here to assess the capacity to monitor the impact of flow variation during the printing.

Part #2 is a modification of the design of test part #1 with the integration of a 3 × 3-mm vertical channel throughout the center of the part. The part is designed having in mind the interest of silicone printing for soft robotics and fluidic systems, with the need for internal channel production. As a consequence, the requirement is to have the channel remaining open, with no obstruction nor material inside. Two samples are printed simultaneously. As for the previous test part, an increase in flow rate is applied between layers 20 and 40 for the second specimen. The goal here is to assess the capacity to monitor the impact of flow variation in terms of overall part geometry and in particular the integrity of the inner channel.

Part #3 is designed as a 50 mm high prismatic shape which is inclined by an angle of 45° with respect to the printing plate. The angle value is chosen based on preliminary experiments, so that part deformation occurs during the printing, with a significant bending. The goal here is to assess the capacity to monitor deviations between the part nominal design and its actual geometry, during printing.

Parts #1 to #3 are sliced with a straight-line filling strategy. These lines change orientation between each layer at an angle of 90°. Filling is therefore a succession of lines oriented successively at 45° and -45° to the outer edges of parts #1 to #3. The filling density used is 100%.

Part #4 is designed as a thin prismatic structure. The rectangular section is 15 × 5 mm with a total height of 50 mm. The latter width is chosen to have significant effect of interaction forces between the nozzle and the part during deposition. The part is produced twice on the same printing plate. For the first one, silicone beads are deposited lengthwise along its base, which limits the impact of interaction forces. For the second one, silicone beads are deposited along the width of the part. The goal here is to assess the capacity to monitor geometrical deviations due to nozzle-layer interactions during printing.

## 3.2. Reference part

Each type of test part is produced on a separate printing plate, so 4 printing sessions are conducted. The value of indicators can vary because of the flow modifications introduced on purpose, and also the printing difficulties described above. They may also vary due to other environmental conditions, which might fluctuate slightly between the printing of different test parts. To verify this, the same design is produced for all sessions. Test part #1 is added as a reference when printing parts #2, #3 and #4. The position of this reference part is the
<figure><img src="image_9.png" /><figcaption>Fig. 7. Illustration of tested parts #1 to #4 used for indicators characterization.</figcaption></figure>

same on all printing plates. The other parts are positioned in relation to this reference part and spaced 30 mm apart in the x direction of the machine.

Each printing plate is therefore dedicated to printing a test part. Each printing plate consists of one or two test parts and a reference part. In the following, a part is designated by an identifier #n_a, where n ∈ [1, 4] designates the type of test part and a ∈ {R, 1, 2} indicates whether it is the reference part (R), the first part to be printed (1) or, depending on the test, the second one (2).

### 3.3. Results with part #1

#### 3.3.1. Manufacturing result for part #1

The three samples of part #1 are shown in Fig. 8. The different layers used to produce the three samples are visible on their outer surfaces. Small tips of filaments are visible on the outer surface of the reference part #1_R. One possible explanation for the presence of these filaments is the nozzle path at the end of the layer production, before the starting of the layer scan. It does not occur with the nominal part #1_1. The increase in flow rate between layers 20 and 40 of the third part #1_2 is clearly visible. The impacted layers are highlighted in Fig. 8. We note that the surface quality of part #1, both nominal #1_1 and with increased flow rate #1_2, is visually similar in the final layers.

#### 3.3.2. Indicators evolution along part #1 printing

The evolution of the three indicators $I_1$, $I_2$ and $I_3$ associated with the printing of part #1 are shown in Fig. 9-d. The value of $I_1$ increases for all three parts. For parts #1_R and #1_1, the trend is similar, with a maximum difference of 0.09 mm between the two plots. The indicator follows the same evolution for part #1_2 as for part #1_1 prior to layer 20. After this layer, the indicator variation changes immediately and strongly increases throughout the flow rate increase, as also outlined in the supplementary video 1. It reaches a value of 1.48 mm at layer 40. Once the flow rate has returned to its nominal value, after layer 40, the indicator's value stops increasing. The indicator value is in the end equal to the value obtained for the other two parts at the end of printing. The relative deviation between the desired final height $D(122)$ and the measured height $Z(122)$ is 2%, 2% and 3% for the reference, nominal and flow-increased parts respectively.

The $I_2$ indicator remains at a constant value before layer 20 for all three parts. From layer 20, the increase in flow rate induces a quite linear increase of $I_2$ for part #1_2. For parts #1_R and #1_1, the indicator value does not vary significantly. Once the flow rate has returned to its nominal value, after layer 40, the value of $I_2$ for part #1_2 comes gradually back to the value for parts #1_R and #1_1. The

increase of $I_2$ between layers 20 and 40 appears to be associated to a progressive deterioration in surface flatness, as illustrated by the point clouds for layers 20 and 40 in Fig. 9-a and b. There is at the same time a progressive in-plane layer shift. As outlined by Fig. 9-c, this shift leads to the presence of points in $\Gamma_{\Omega_j}$ which altitude differs from the current layer altitude. This causes an increase of the value of $I_2$ after layer 90 observed for part #1_1 and #1_2.

The $I_3$ indicator remains at zero for all parts before layer 20, and for parts #1_R and #1_1 up to layer 80. Non-zero values can be noticed between layers 30 and 45 for part #1_2. We then observe a slow increase leading to a gap between the time when the flow increase is applied and when points are counted in the computation of $I_2$ (Fig. 9-b). The value of $I_3$ increases from layer 80 for all three parts. This is again related to the in-plane layer shift, explaining also variations of $I_2$. These points can be seen in Fig. 9-b.

### 3.4. Results with part #2

#### 3.4.1. Manufacturing result for part #2

The printing results are shown in Fig. 10. As with the previous printing session, layers are visible on the outer surfaces of the parts. As with part #1, some filament tips can be seen on the outer surface of the reference part and also part #2_1. One simple way to check the impact of flow modification is to verify that the inner channel allows the printing plate to be visible when looking at the parts from the top. The plate is physically visible through the channel for part #2_1, but not for part #2_2. This means the channel is being obstructed.

#### 3.4.2. Indicators evolution along part #2 printing

Fig. 11-d shows the value of the indicators $I_1$ to $I_3$ during the production of the layers of parts #2_R to #2_2. Firstly, one can note that the progression of $I_1$ through the layers for the reference part #2_R differs from the progression observed for the previous print. The value of $I_1$ remains between 0.3 and 0.8 mm, so the variation remains limited. The evolution is similar for part #2_1, with a maximum value of 0.88 mm that is reached for layer 42, to be compared to the value of 0.82 mm for layer 71 of part #2_R. The flow modification clearly creates a change in the evolution of $I_1$ for part #2_2 between layers 20 and 40, reaching a maximum value of 1.88 mm at layer 40. The impact of flow rate can again be monitored via $I_1$. Once the flow has returned to its nominal value, the indicator's value for part #2_2 comes back gradually to the value computed for the other two parts.

The $I_2$ indicator shows a different evolution between part #2_R and the other two. The growth of $I_2$ for parts #2_1 and #2_2 is similar before layer 20. From this layer onwards, there is a different trend
<figure><img src="image_10.png" /><figcaption>Fig. 8. Photograph of the manufacturing of parts #1_R, #1_1 and #1_2.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 9. Values of indicators $I_1$ to $I_3$ (d) measured on part #1 with point clouds (a to c) highlighting the impacted part at key moments of the printing process. The situations corresponding to the plotted points clouds (a to c) are highlighted on the plot of $I_1$.</figcaption></figure>

between the values of $I_2$ for these two parts. The gap increases until layer 50, reaching a value of 1.1 mm. A decrease of $I_2$ for part #2_2 can be seen from layer 50 to 57.

The values of $I_2$ for parts #2_1 and #2_2 continue to increase from layer 57 until the end of the printing process. The maximum value of $I_2$ for the reference part is 0.14 mm. This value is very small compared to the maximum $I_2$ value of 2.1 mm for part #2_1 and 3.0 mm for part #2_2. The difference observed between part #2_R and #2_1 is probably due to the change of nozzle paths. For the latter part, the nozzle motions are generated to fill in the layer while avoiding the

inner channel. The geometrical quality of the layers is then modified. Differences between parts #2_1 and #2_2 due to the flow modification are distinct from those observed with $I_1$: the gap between the two curves does not appear instantaneously, but a modification of the rate of evolution of $I_2$ can be observed, followed by a quite similar trend of the indicator for both parts after layer 60.

Similar to the printing process of part #1 above, a non-zero value of the $I_3$ indicator's value for part #2_2 appears several layers after the flow rate has been increased (Fig. 11-d). Here, it is layer 26, where the first points belonging to $\Omega_j$ at the mean altitude $Z(j)$ of the layer
<figure><img src="image_12.png" /><figcaption>Fig. 10. Photograph of the result of manufacturing part #2_R, #2_1 and #2_2.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>Fig. 11. Values of indicators $I_1$ to $I_3$ (d) measured on part #2 with point clouds (a to c) highlighting the impacted part at key moments of the printing process. The situations corresponding to the plotted points clouds (a to c) are highlighted on the plots of $I_1$ and $I_3$.</figcaption></figure>

are detected. There are up to 53 points for layer 31. The corresponding point cloud of part #2_2 is shown in Fig. 11-a. $I_3$ then decreases for part #2_2 around layer 40. The point cloud associated with this layer of part #2_2 is shown in Fig. 11-b. Points are located in the area corresponding to the inner channel: one can notice the height of blue points in the channel in Fig. 11-b. The height of these points excludes them from selection in the calculation of $I_3$. Points are subsequently identified again, such as in layer 54, whose point cloud is shown in Fig. 11-c.

The supplementary video 1 shows that $I_3$ is almost constantly positive after layer 26. For some layers, $I_3$ is zero because the points' altitude detected in $\Gamma_{\Omega_j}$ is distinct from the current printed layer.

### 3.5. Results with part #3

#### 3.5.1. Manufacturing result for part #3

The printing plate associated with part #3 is shown in Fig. 12. The picture is taken right after printing. Part #3_R has similar markings to
<figure><img src="image_14.png" /><figcaption>Fig. 12. Photograph of the result of manufacturing part #3_R, #3_1.</figcaption></figure>

part #1_R on its side surfaces. Fig. 12 shows part #3_1 with its outer shape. The part has a curved slope at a non-constant angle, whereas a straight 45° slope is expected. The difference between the two slopes is highlighted in Fig. 12.

### 3.5.2. Indicators evolution along part #3 printing

Indicators' evolutions of $I_1$ to $I_3$ for parts #3_R and #3_1 are shown in Fig. 13. For part #3_R, the indicator $I_1$ remains constant during the first 20 layers. It then starts to increase continuously until the end of printing. A similar trend is observed initially for part #3_1. There is however an inflection point around layer 50 for part #3_1, where the indicator $I_1$ begins to decrease until the end of production. $I_1$ reaches a value of 1.17 mm and -0.11mm for parts #3_R and #3_1 respectively. One possible cause of such variation of $I_1$ for part #3_1 is the part own weight, that becomes large enough to bend the part, so the altitude gap decreases when the part weight and the overhang length increases.

The indicator $I_2$ has a similar evolution for parts #3_R and #3_1 up to layer 47. The two indicator values $I_2$ for #3_R and #3_1 then differ more and more as manufacturing continues. $I_2$ increases for part #3_1 from layer 47 to the end of production. The final values of $I_2$ are 0.16 mm and 0.60 mm for #3_R and #3_1 respectively. This shows a gradual and ultimately significant degradation of the flatness of the layer visible in Fig. 12, on the point clouds in Fig. 13-b and c and in the supplementary video 1.

The point clouds associated with the maximum values of $I_3$ are shown in Figs. 13-a and b for parts #3_R and #3_1 respectively. As these point clouds show, the points counted in $I_3$ are associated with the print layer being translated in the directions of the printing plane. This may explain the occasional high values of $I_3$.

## 3.6. Results with part #4

### 3.6.1. Manufacturing result for part #4

The printing process of part #4 consists of 3 parts (Fig. 14), with the reference part #4_R, part #4_1 and #4_2. Part #4_1 is the sample

printed with the filament's main direction along the length of the part. It exhibits sharp edges. Part #4_2 is visually affected by the change in filament laying direction. While the base has a similar shape, the edges of the part are rounded, revealing the change of interaction between the material nozzle and the part.

### 3.6.2. Indicators evolution along part #4 printing

Fig. 15-d shows the evolution of indicators $I_1$ to $I_3$ during the printing process. The evolution of $I_1$ is common for parts #4_1 and #4_2 until layer 37. After this layer, $I_1$ starts to increase for part #4_2. Visual observation of the part shows that it corresponds to a change of shape of the layers, with a more rounded profile. The evolution of the layer geometry is also outlined in the supplementary video 1. The layer surface is slightly smaller, which may explain the change in the altitude evolution, as visible on the variation of $I_1$. The final value of $I_1$ is equal to 0.45 mm, 0.33 mm and 0.82 mm for parts #4_R, #4_1 and #4_2 respectively.

The distinct evolution of indicator between parts #4_1 and #4_2 is also visible for $I_2$. While the value of $I_2$ is very similar for parts #4_1 and #4_2 for the first 50 layers, the indicator notably increases from layer 50 for part #4_2. The maximum value is reached at layer 110 with a value of 0.79 mm. The associated point cloud is shown in Fig. 15-b. It highlights the rounded shape of the printed layer, with significant variations in the altitude of layer points. The values of $I_2$ for parts #4_R and #4_1 are stable along the printing process, with a mean value of 0.080 mm, which represents 10% of the maximum value obtained for the indicator $I_2$ for part #4_2.

The indicator $I_3$ has a non-zero value all along the printing process for part #4_R with a maximum obtained at layer 8. Fig. 15-a shows the corresponding point cloud. It highlights the presence of filaments captured at the current altitude of the layer, as seen in Fig. 14. Those filaments are captured at each layer, explaining the non-zero value of $I_3$ for part #4_R. $I_3$ increases from layer 75 for part #4_2. The maximum
<figure><img src="image_15.png" /><figcaption>**Fig. 13.** Values of indicators $I_1$ to $I_3$ (d) measured on part #3 with point clouds (a to c) highlighting the impacted part at key moments of the printing process. The situations corresponding to the plotted points clouds (a to c) are highlighted on the plots of $I_2$ and $I_3$.</figcaption></figure>

<caption>**Table 1**</caption>

Table summarizing the observed sensitivity of indicators to the causes of defects (o: low sensitivity, +: sensitive, ++: very sensitive).

<table><thead><tr><th>Cause of defect</th><th><i>I</i><sub>1</sub></th><th><i>I</i><sub>2</sub></th><th><i>I</i><sub>3</sub></th></tr></thead><tbody><tr><td>Management of flow rate</td><td>++</td><td>+</td><td>+</td></tr><tr><td>Part deformation due to its weight</td><td>+</td><td>+</td><td><b>o</b></td></tr><tr><td>Part deformation due to nozzle-layer interaction forces</td><td>+</td><td>++</td><td>+</td></tr></tbody></table>

value of $I_3$ for part #4_2 is 23 at layer 89. The corresponding point cloud is shown in Fig. 15-c. One can see on this figure the deviation of the layer in printing plane, especially in the y direction, corresponding to the impact of the layer-nozzle interaction force. This can be also seen on the point cloud in Fig. 15-b. The value of $I_3$ is below 1 for all the layers of part #4_1.

## 4. Discussion

The indicators were built to make them sensitive to the 3 causes of printing defects mentioned: variation or poor choice of silicone flow rate, deformation of the part under its own weight, deformation due to nozzle-layer interaction during manufacture. In the end, these indicators identify these effects, but in a less decoupled way than initially expected. The Table 1 summarizes the elements considered below, the section ending with discussion on the possible implementation strategies, taking into account the impact of scan time in the process.

### 4.1. Management of silicone flow rate

The impact of the flow rate on the part is clearly seen using the $I_1$ criterion, characterizing the evolution of the layer height. We have seen on the pairs of parts #1_1 and #1_2 or #2_1 and #2_2 that the indicator $I_1$ varies as soon as a variation in flow rate occurs. $I_1$ should therefore

be usable to monitor production and provide a warning of unsuitable dispensing conditions.

We also found that its value can return to the obtained value for the case of production without flow fluctuations after a certain number of layers, as seen with parts #1_2 and #2_2. This means, on the one hand, that a part can be finally compliant in terms of layer height, despite a variation in flow rate during manufacturing. This is also an encouraging sign as to the possible effectiveness of flow control during manufacturing, if deviations are detected via monitoring of $I_1$. The test cases of #1_2 and #1_2 can be seen as extreme cases, in the sense that the correction strategy consisted solely in returning to the nominal flow rate. In the end, still, this ensures the correct layer height at the end of production. This shows that here, at a scale of approximately fifty layers, we return to the desired geometry.

In addition, we note that this indicator highlights small variations, less than 0.76 mm between parts #1_R to #4_R, despite the constancy of temperature and humidity conditions, nozzle trajectories and positions on the tray. Small variations may be due to the dispensing system, and it would seem appropriate to monitor them systematically using the $I_1$ indicator.

The increase in flow rate also has an impact on indicators $I_2$ and $I_3$, but with a slow increase in front of $I_1$. This increase is associated with a material accumulation effect involving, on the one hand, an irregularity in the layers produced and, on the other hand, the eventual appearance of material at the level of the layer extending beyond the perimeter planned at the time of slicing. This leads to two conclusions. Firstly, $I_2$ and $I_3$ can be used to monitor the impact of flow rate. Depending on the part under consideration, the speed of increase of $I_2$ and $I_3$ may or may not be problematic. Secondly, the existence of the slow increase constitutes interesting information, as it shows that the appearance of defects on the part occurs after a certain number of layers. It must therefore be possible to modify the flow rate before the effects of an initially inadequate value become apparent in terms of surface irregularities or failure to achieve the desired geometry in
<figure><img src="image_16.png" /><figcaption>Fig. 14. Photograph of the result of manufacturing part #4_R, #4_1 and #4_2.</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>Fig. 15. Values of indicators $I_1$ to $I_3$ (d) measured on part #4 with point clouds (a to c) highlighting the impacted part at key moments of the printing process. The situations corresponding to the plotted points clouds (a to c) are highlighted on the plots of $I_1$ to $I_3$.</figcaption></figure>
the layer. Finally, the increase in $I_3$ observed during production of the part #2_2 channel confirms the interest of the indicator in ensuring the integrity of internal structures. If the difference of altitude with the current layer obstructed structures becomes high, $I_3$ can, however, underestimate the phenomenon.

## 4.2. Part deformation due to its weight

The production of part #3_1 shows that part deformation can be tracked via the indicator $I_1$, which outlines the drop in the average altitude of points in the layer. This indicator then shows the presence of part bending. It may be possible to use the variation of $I_1$ with the number of layers to determine the intensity of the corrective action required to compensate for the defect.

The results for part #3_1 show that the indicator $I_2$ can also be used to determine the onset of part sagging. The latter leads to a deterioration in surface quality and a defect in layer orientation, the combination of their effects being detected.

As it stands, however, the $I_3$ indicator is more difficult to use to detect and manage this source of defects. The results obtained for part #3_1 show that it is difficult to discriminate between the impact of sagging due to difficulties in manufacturing the layer, with the presence of material outside the expected mask of the current layer. The deformation must result in a significant shift, which is hardly visible in the example. So the indicator $I_3$ does not seem the most appropriate way to track this defect.

## 4.3. Part deformation due to nozzle-layer interaction forces

The indicator $I_3$ allows us to evaluate the appearance of material at the layer level, when the desired layer geometry is not respected. For parts #2_1 vs #2_2, then #4_1 vs #4_2, we see an immediate variation, with an evolution of the value of the indicator between layers that can be significant. By using a threshold, we should be able to use $I_3$ to tell if a problem is being encountered, and stop production if the variation is too large. In the future, it would be possible to refine its use to identify the direction of translation in the plane and establish a dispensing strategy to correct the defect: for example, by associating a deformation model to determine the correction to be made, by changing the paths or their directions with the nozzle to modify the direction of the forces.

The $I_2$ indicator is designed to track the quality of the layer. In the case of part #2_2, there is indeed a degradation of the layer quality when the flow rate is temporarily modified. For the part #2_1, the indicator is also non-zero. The trajectories followed by the nozzle are different from the ones adopted for the reference part #2_R, which must induce different nozzle-layer interaction forces. This causes a degradation of the surface quality which is observed with $I_2$. The variations in $I_2$ are however not very regular: we can see that there is a need to build up a trend, via filtering for example, to then construct a correction strategy.

The production of the #4_1 and #4_2 parts shows that the choice of trajectories followed by the nozzle, and therefore ultimately the forces to which the part is subjected during production, have an impact which can also be seen through $I_1$. Depending on the trajectories, the adhesion between the deposited beads and the previous layer may fluctuate, leading to a possible variation in cross-sectional shape and ultimately a variation in $I_1$ for the same flow rate. The impact is indirect, however, unlike $I_3$ or $I_2$.

## 4.4. Scan time and possible implementations

The proposed indicators are computed based on 3D data issued from laser profilometry. The possibility to use the printer kinematics to achieve the 3D scan simplifies the implementation of the measurement phase. The latter can at the same time be seen as time consuming, an

aspect we share with other authors having considered laser profilometry for AM supervision, and the impact of this additional time can be discussed.

As a basic approach, it can be considered that the scan and then the computation of the indicators are achieved after each layer production, as it was implemented during this study. In that situation, the manufacturing time will obviously be increased, being doubled if scan and deposition speeds are chosen equal. These two speeds are however not correlated, and the scan can be achieved with higher velocities.

The extent to which the impact of additional measurement time is significant can be analyzed in several ways, depending on the use made of the instrumented approach. Instrumented production can be used to set up printing conditions that open up the possibility of mass production. Monitoring can then be carried out on control samples. In this way, correct production can be achieved, and the extra cost in time is limited to the initial adjustment phases. In this case, monitoring and ultimately the identification of correct printing conditions could also make parts printable, whereas they would otherwise fail during manufacture. Ensuring feasibility is undoubtedly an important criterion, even if there is an additional cost in terms of printing time.

Another approach is to exploit scanning during production, and not only during an initial set-up. In this case, it is important to emphasize that it is not mandatory to scan all layers in order to use the indicators. Time can of course be saved by choosing to scan layers corresponding to critical phases (cavity closure, thin walls reaching significant heights, for example). At the moment, it is up to the user to decide, depending on the part to be produced. In the future, however, it may be possible to make automatic decisions based on geometry. A practical example of a pneumatic cavity closure is shown in supplementary video 1. In this example, a cavity is closed with different closing overhang angles. The scan could then be used in the cavity closure phase to monitor the possible collapse of the cavity.

Finally, it should be pointed out that scanning time is largely dependent on hardware and software implementation. In this study, software synchronization of laser profilometry and printed part motion is used. This simplifies integration. Motion synchronization based on an encoder measurement would enable synchronization of part motion and measurement, significantly reducing printing time. However, this requires the integration of an encoder on the axis of the scan direction, which must be anticipated in the machine design.

## 5. Conclusion and perspectives

In conclusion, we proposed an instrumented silicone extrusion printing platform to acquire point clouds on the part in production. The hardware and software integration of a laser profilometer with the printing system has been detailed to enable the exploitation of the solution, including open access code for the community.

We proposed three indicators to assess the appearance of defects during printing, taking into account the main causes of defects in this context. Experimental evaluation has shown that the three proposed indicators can be used to monitor defects associated with flow rate management, deformation of part geometry under the weight of the part, or interaction forces between the nozzle and the layer produced.

The indicators have different characteristics in terms of sensitivity and evolution dynamics. Firstly, the appearance of difficulties linked to material flow seems to be almost immediate via the $I_1$ indicator. A first perspective is then to manage the flow according to the value of $I_1$. However, each indicator is not only sensitive to one type of defect. It will therefore be necessary either to rely on a prior analysis of the part to check whether $I_1$ can be biased by other defects, or, for example, simply to use a reference part on the printing plate to specifically check the flow management.

The $I_2$ and $I_3$ indicators can then be used to supervise the printing of parts sensitive to other sources of defects. As it stands, the $I_3$ indicator seems to have little sensitivity to part deformation under its
own weight. It should therefore be possible to decorrelate the causes of defects and correct them. A learning approach could be envisaged, followed by compensation during manufacturing.

The use of instrumentation is increasing the production time. As a perspective, it would be of interest to develop a decision criterion based on the geometry to produce to define the optimal scanning strategy, based on the automatic identification of critical features like overhang angles or thin walls sensitive to nozzle-part interaction.

We propose a process monitoring method based on the comparison of the acquired point cloud with the 3D model of the layer to be produced. The focus is on the geometric integrity of the part produced, as this is the first criterion to be monitored in 3D printing. However, the integrity of a part also involves monitoring broader mechanical properties, such as the watertightness of the structure for soft pneumatic robots. So, although our supervision method enables us to identify local material shortages, it seems potentially interesting to generalize our approach and move towards monitoring the mechanical integrity of the part in the supervision method. This will require the addition of measuring instruments dedicated to monitoring the extruded filament and the contact between the extruded filament and the material already present. The measuring instruments to be added must be able to monitor the interaction between the bead and the previously extruded material. A first approach is to integrate several profilometers with the print head to track the extruded filament in 3 dimensions. A supervision strategy that merges the data acquired by the various sensors will then have to be put in place.

The supervision method was applied to silicone, which falls into the category of soft materials. One long-term perspective aim is to consider the method for the extrusion printing of other flexible materials. Two obstacles can be anticipated: (i) soft materials are a group of materials with very diverse optical properties. It is therefore necessary to ensure that the laser profilometry device is adapted to the extruded material. (ii) The various applications that exploit the extrusion of soft materials aim to produce parts of various dimensions. The use of small-diameter nozzles means that the profilometer must have the resolution required to track the structures produced.

### CRediT authorship contribution statement

**Loïc Mosser:** Writing – original draft, Software, Investigation. **Lennart Rubbert:** Writing – review & editing, Supervision, Methodology. **Laurent Barbé:** Writing – review & editing, Supervision, Resources, Methodology. **Pierre Renaud:** Writing – review & editing, Writing – original draft, Funding acquisition, Conceptualization.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgments

This work has been partially supported by the French program Investissements d'avenir (ROBOTEX 2.0 ANR-10-EQPX-44-01, TIRREX ANR-21-ESRE-0015, PEPR O2R AS1 ANR-22-EXOD-0005, RAMSAI ANR-22-CE10-0001).

## Appendix A. Control of the silicone dispensing system

The printer is initially dedicated to FDM nozzle control, with thermoplastic extrusion. The silicone extrusion system is driven by a step-per motor, controlled from the printer control board, so the latter needs to be configured correctly.

During the part slicing, a diameter $d$ is set for what is here a virtual filament of thermoplastic material. In all the experiments, the value of $d$ was set to its default value of 1.75 mm. For a filament feed speed $V_{fil}$ chosen during the slicing, the material flow rate $Q_{in}$ is then

$$Q_{in} = V_{fil} \frac{\pi \cdot d^2}{4} \qquad (4)$$

During FDM printing, the filament speed is controlled through a firmware parameter noted $\phi$ which is the number of nozzle motor steps to be actuated per mm of filament to be extruded. In our situation, the volume of dispensed silicone per motor step is set by the design of the progressive pump. With the Vipro-head 5, it is 0.0013 mm³ per motor step (manufacturer's data). So the effective flow rate $Q_{out}$ of silicone when sending a G-Code instruction of extrusion with filament speed $V_{fil}$ is

$$Q_{out} = 0.0013 \cdot \phi \cdot V_{fil} \qquad (5)$$

The parameter $\phi$ is determined to have $Q_{in}$ and $Q_{out}$ being equal, here 1850 steps/mm.

## Appendix B. Supplementary data

A supplementary video has been produced to enhance understanding of the evolution of the indicators during the various printings of parts #1 to #4.

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.addma.2025.104779.

### Data availability

Point clouds measured in this paper are completely made available for the community and can be found on entrepot.recherche.data.gouv.fr.

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