# Accelerated Monitoring of Powder Bed Fusion Additive Manufacturing via High-Throughput Imaging and Low-Latency Machine Learning

Ayyoub Ahar, Rob Heylen, Dries Verhees, Cyril Blanc, Abdellatif Bey-Temsamani

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Ayyoub Ahar, Rob Heylen, Dries Verhees, Cyril Blanc, Abdellatif Bey-Temsamani. Accelerated Monitoring of Powder Bed Fusion Additive Manufacturing via High-Throughput Imaging and Low-Latency Machine Learning. 19th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2023, León, Spain. pp.250-265, 10.1007/978-3-031-34107-6_20. hal-05498065

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# Accelerated Monitoring of Powder Bed Fusion Additive Manufacturing via High-Throughput Imaging and Low-Latency Machine Learning

Ayyoub Ahar(✉) ID, Rob Heylen ID, Dries Verhees ID, Cyril Blanc ID, and Abdellatif Bey-Temsamani ID

Flanders Make, 3920 Lommel, Belgium
ayyoub.ahar@flandersmake.be

**Abstract.** Metal 3D printing in particular laser powder bed fusion is in the forefront of product manufacturing with complex geometries. However, these printed products are susceptible to several printing defects mainly due to complexities of utilizing high-power, ultra-fast laser for melting the metal powder. Accurate defect prediction methods to monitor the printing process are of high demand. More critically, such solutions must maintain a very low computational cost to enable feedback control signals for future low-latency laser parameter correction loops, preventing creation of defects in the first place. In this research, first we design an experiment to explore impact of several laser settings on creation of the most common defect called “keyhole porosity”. We print an object while recording the laser meltpool with an externally installed high-speed visual camera. After extracting keyhole pore densities, we annotate the meltpool recordings and use it to evaluate performance of a simple but fast CNN model as a low-latency defect detector.

**Keywords:** pore density · melt-pool monitoring · keyhole pores · defect detection · additive manufacturing · laser powder bed fusion

## 1 Introduction

Layer by layer 3D printing of objects with high-complex geometries has been possible in a process called Additive Manufacturing (AM) [12]. Its advantages over other traditional manufacturing methods among others are: on-demand production of uniquely designed objects in limited quantities, substantial cost reduction via shorter supply chain and less storage [3], reduced parts counts, faster and less complicated assembling and decreased time and complexity for replacing the used parts [10]. Various printing materials like polymers and metals can be utilized mainly via an AM method called laser powder bed fusion (LPBF). In this method the powder of chosen material is melted with a moving laser beam. The laser melting patterns are adjustable and derived from the 3D model
of the object to be printed. Industries such as dental [4], construction [5], and aerospace [7] are especially interested in utilizing such production method.

Nevertheless, LPBF is yet to displace conventional manufacturing due to its sensitive nature which depends on many factors to achieve a high-quality defect-free product. Multiple microstructural defects and anomalies can occur during the printing that can significantly degrade the structural integrity and quality of the build [9]. Among others, creation of tiny voids and pores inside the product is a common defect that can occur mainly due to unstable printing conditions and variations in laser power and speed [1]. Current solutions to reduce such defects are mostly heuristic and mainly involve costly post-production testing via both destructive and non-destructive approaches which thereafter will guide the readjustment of the printing parameters. However, following such approaches requires several repeated printings which impose high cost to the final product.

Ideally, an automated monitoring system embedded in a low-latency feedback control loop can provide on-the-fly correction commands during the print process to prevent generation of defects in the first place or compensate for it by remelting the faulty region. The first building block toward such solution is creating an efficient monitoring system to not only analyze the printing process in a close to real time manner but also be able to predict whether the ongoing print is going to create any defects or not. This requires more than just detecting anomalies in printing parameters. Because not all deviations from the nominal print settings are going to end up creating defects. From another point of view, being able to provide more detail on the spatial location of defect in the product being printed is desired for creating more accurate controlling signals as well. As a result, utilizing visual cameras over photodiodes as the input sensors are justified. Although, to be able to cope with the fast movements of the laser beam i.e. laser speeds up to 1500 mm mm/s, using high speed video cameras with sampling rates up to 20000 frames per second are proposed [1]. Nevertheless, in the same research authors emphasized that using deep learning prediction approaches imposes high computational cost beyond the requirements of an integrated monitoring system in a low-latency control feedback loop. To further clarify this, let us approximate printing one layer of a small 5 × 5 mm square object. To print 1 layer of this object with ≈50 scan lines and fixed laser speed of 1000 mm/s it will take quarter of a second to print a full layer and 5 ms per line. A high speed camera of 20K fps will produce 100 images of ≈14K pixels per print line which means to have at least one control feedback signal after printing each 20 lines, the full monitoring pipeline should process ≈1000 frames in ≈100 ms! This time slot includes capturing, writing/reading into memory, feature extraction, and finally defect prediction step. Even with best hardware integration and optimization solutions, the allocated time slot for the prediction model currently remain well below anything but the most simplistic regression techniques. Consequently, to alleviate this issue with a more practical solution, Booth et al. [1] proposed a simplified fully-connected network with couple of hidden layers combined with a heavier pre-process on the input data to engineer multiple features, extracted from the captured raw videos.

In this research, our main objective is to further decrease the computational cost of the full monitoring pipeline by dropping the costly feature extraction
steps and analyzing the raw video inputs instead. We focus on the most common porosity defect called “keyhole pore”. We design a specific specimen in such a way that let us exhaustively investigate state of keyhole pore generation for several variations of off-nominal laser power (P) and speed (V). We then print it with 316L stainless steel powder using LPBF while recording the laser meltpool with an externally installed high speed visual camera. Next, we extract the keyhole pore densities from the X-ray computed tomography imaging (xCT) of the printed specimen to conduct a laser PV-space keyhole pore analysis of which to the best of our knowledge, provides the most detailed analysis of its kind at least for the case of the stainless steel LPBF. We prepare the ground-truth data to train our prediction models using those CT extracted pore densities and then use them to annotate each and every captured video frame. Finally, we propose clipping a region of interest (ROI) from each video frame to use it as input to a simple but fast Convolutional Neural Network (CNN). We then provide its performance analysis across entire tested PV space, to directly predict the laser power and speeds as well as keyhole pore occurrence. Additionally, We test a deeper CNN architecture on full (un-cropped) video frames as anchor to highlight the difference between model performances. We report that our fully-annotated dataset of this experiment is also available to download upon request.

# 2 Materials and Methods

## 2.1 Specimen Description

A test specimen was 3D printed on a 3D Systems ProX DMP320 laser powder bed fusion (LPBF) machine in 316L stainless steel. This machine employs a 500W power-adjustable IPG fiber laser to melt metal powder layers with configurable thickness between 30 and 60 µm. Each of these print layers are created by several laser scan lines. For our specimen, layer thickness was set at 30 µm, with a laser focal spot of 75 µm and hatching distance 100 µm. Powder particle size distribution is between 20–50 µm. The build chamber is vacuum-cycled and filled with an argon over-pressure before commencing the LPBF process.

The specimen (see Fig. 1a) is bar-shaped, with a 9 × 9 mm square in the horizontal plane, and an object height of 18 mm in total. A 10 × 9 mm base plate and a 1 × 1 × 1 mm cube were added at the bottom and top respectively to allow easy orientation of the object. The base plate was printed on 8 mm offsets and removed from the printer bed mechanically with a cutting disk after completion.

The machine is equipped with the Materialise Control Platform (MCP), which allows us to alter the print parameters for every line. The bulk of the material is printed with nominal settings, i.e., laser power *P* = 215 W and laser speed *V* = 900 mm/s. Hatching rotates with 90° between layers. A set of three subsequent layers is printed with nominal speed but low laser power (60 W) near the bottom and top of the object (layers 400–402 and 800–802). These print settings are in the lack-of-fusion (LoF) regime, inducing thin flat layers of unfused material in the object. These LoF regions are easy to locate on CT scans, and allow accurate alignment of the post-process CT scan.
<figure><img src="image_3.png" /><figcaption>(a) printed specimen objX</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>(b) laser PV space.</figcaption></figure>

**Fig. 1.** (a) An image of the specimen after printing, before detachment from the print bed. (b) The sampled settings in laser *PV* space. Each black star corresponds with a single error line printed in the sample. Nominal laser power and speed settings are indicated with a red star. (Color figure online)

Error layers (i.e. layers printed with off-nominal laser speed and power) are introduced in the object every 30th print layer, with 29 nominal layers (i.e. layers printed with nominal print settings) in between. For each error layer, most lines are also printed with nominal settings, except lines 10, 15, 20, ..., 80 that are printed with a unique off-nominal laser setting pair. The interline distance is the hatching distance, 100 µm. This spacing between error lines in two dimensions in our experimental setup allows us to identify each introduced porosity with the error line that caused it, with the assumption that nominal settings do not introduce porosities.

We aim to investigate the creation of keyhole porosities in our system, to construct a power/velocity (PV) diagram that identifies the keyhole regime, to obtain statistical information on these keyhole porosities, and to predict keyhole creation events and system setting deviations from high-speed video measurements of the process. The focus therefore lies on system settings that potentially create keyhole errors, while at the same time sampling a wide enough area in PV space to cover settings around nominal as well.

The system settings from the error lines were chosen from the diagram shown in Fig. 1b. This distribution was inspired by results from literature [8, 13], where PV diagrams of similar LPBF setups were experimentally derived or simulated. We employed the following reasoning:

- 215 error settings in total (15 error layers with 15 error lines each)

- Reasonable minimal and maximal values: $P \in [50-500]$ and $V \in [100-1800]$. Values outside this range are incompatible with the machine, or are highly unrealistic.
- A larger PV space sampling density for lower speed and energy values to obtain better understanding of the expected shape of the keyhole border in this region.

- The energy density $E$ should remain within realistic values, we chose energy densities between 25% and 400% of nominal. As $E \sim P/V$, these restrictions denote two lines with different slopes in the PV diagram.

- We are less interested in values where we know a priori that the chance on keyhole pores is very low. Therefore, we cut off a part of the PV space with high $V$ and low $P$.

## 2.2 Printing Process Monitoring Setup

The meltpool was monitored during the printing using a custom setup explained in [11]. This setup utilizes an off-axis high-speed camera (Mikrotron Eosens 3CL) placed with an inclination angle of about 25° with respect to the normal of the build plate. This off-axis monitoring setup is particularly advantageous for recording the spatter signature in the vertical direction and being in a stationary position independent from the movements in optical path of the laser beam. The video stream is recorded at 20,000 frames per second, with each frame having 120 × 120 pixels. Consequently, each pixel corresponds to approximately 100 µm × 100 µm over a total field of view of 12 mm × 12 mm. To protect the sensor from the excessive brightness of laser, a short-wavelength pass filter with a cut-off value of 975 nm was placed before the camera. The monitoring system was capable to record wavelength range between 350 nm and 975 nm which corresponds to the visible to near infrared range of the spectrum. The exposure time of the camera was set at 30 µs, combined with a 50 mm lens with an aperture of f/16. The aperture and shutter time are set manually to optimize the visibility of the spatters while recording the meltpool.

The video recording was accompanied with the metadata provided by the MCP which was collected at 100 kHz. The metadata included the x, y coordinates of the laser on the build area, the laser on/off signal, the laser power, the laser speed, and timestamps. The high-speed camera was connected via Camera Link to NI PXI. Data was stored to an NI SSD drive with a high-speed data transfer rate of around 1.5 GB/s.

## 2.3 Prediction Methodology

In this section first we explain the prediction model input, their format, their structure and utilized preprocessing steps. Then, structure of the utilized neural networks and their characteristics are discussed.

### *Structure of the Input Data*

Initially we synchronize and annotate each of the captured images with their corresponding metadata collected from the MCP controller. Next, they are associated to the extracted pore densities from the CT scan data. Then, we randomize their capture order and for each experiment, two separate subsets of
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** (a) Position of the installed monitoring camera and (b) a sample image captured from the meltpool</figcaption></figure>

the images for training and testing are selected from the total image pool. The default training subset selected in the way that includes 100 images from each one of 225 printed lines with specified system settings shown in Fig. 1b, yielding a total of 22500 images. After subtracting the training set, the testing subset is selected from the remaining pool in the way that includes 10 images from each one of 225 printed lines with specified system settings shown, yielding a total of 2250 images. For certain experiments we use only the part of each image that shows the close region around the meltpool. To do so, a 21 × 21 ROI around the brightest point is extracted from each image which we assume it contains the most important information of the captured scene. No other image processing is applied on the images at this point.

### Prediction Models

As it was emphasized in the introduction section, in these experiments complex models and deep neural networks are categorically ignored mainly due to strict runtime limitations of the problem. Additionally, a quick scan through the captured video frames indicates that our input data is mainly consists of a small bright spot representing the laser melt pool in the presence of large dark background (See Fig. 2b) with occasional exceptions due to arbitrary appearance of spatters and noise. Therefore, for such simple scenes content-wise, deep model architectures commonly overfit which potentially lead those models to quickly memorize the patterns, the noise and random fluctuations present in the training set all together. Instead, in this experiment we utilize straightforward CNN structures. The number of hidden layers and trainable network parameters are empirically chosen for this experiment.

The *Model-A* is a CNN with more hidden layers is used only for the full-size video frame data in order to better capture the underlying patterns of the whole scene. It has 8 hidden layers and 4 pooling layers, connected to a final dense layer
for regression. To predict the laser settings, We utilize the results of this model as a point of reference to evaluate the performance of the faster but shallower proposed model-B.

The *Model-B*, has only 4 hidden layers and 2 pooling layers, connected to a final dense layer for regression. This model is mainly intended to be used for the 21 × 21 pixels ROI inputs extracted from video frames, but we also test it on the full-frame videos to compare the impact of the ROI extractions on its performance. Both models are implemented in python language using Keras neural-network library [2] and simply reproducible. To facilitate better comparison conditions, all models are trained with 100 epochs.

# 3 Experimental Results

In this section first we explain the results of post-print scanning of the test object and analysis of the created pores in response to the tested laser settings. This analysis effectively generates the ground-truth data for evaluation of prediction models. Prediction model performance analysis is presented separately for predicting the laser parameter settings and pore density respectively.

## 3.1 CT Segmentation

After printing, detachment from the printer bed, and grinding and polishing to remove the supports, the object was CT scanned with a FleXCT micro CT imaging system, resulting in a CT reconstruction with 10 µm square voxels. The CT scan was denoised with a total variation denoising method and thresholded to create a pore segmentation following the procedure described in [6].

Registration of the CT reconstruction with the experiment's coordinate system was performed by minimizing the deviation of several features from their theoretical values with respect to translation and rotation degrees of freedom: The locations of the LoF layers, and the alignment of top and bottom horizontal cuts with squares.

The aligned pore segmentation is shown in Fig. 3 in a 3D view and a side view. The error lines that created these pores are indicated as well. It is clear that the majority of *PV* setpoints do not create keyhole pores. When pores are created, significant differences can be observed on the number and depth of the pores. This is to be expected: The penetration depth of the laser and the subsequent keyhole pore creation processes show a complex dependence on the laser parameters [13].

To quantify these qualitative observations, two measures were calculated from each error track and the observed pores below (track and pore identifications are shown by red/green boxes in Fig. 3):

- The total number of segmented voxels in box beneath the line track. This is a measure for the total pore volume
- The weighted depth of the pores, calculated by averaging the distances of each segmented voxel with the green line, which corresponds to the print layer (or Z-coordinate) where the error line was introduced.

These measures are shown in Fig. 4 as contour graphs over the *PV* space. Only parts that effectively contain pores are color coded. These graphs show a clearly delineated area in *PV* space where keyhole pores will be present.

We observe that keyhole pores are formed when laser speed *V* is low and laser power *P* is high. This is inline with the reports from other independent experiments like those reported in [8,13]. But the exact relation between these two settings and keyhole pore formation is complex and nonlinear, which is also confirmed by our experimental results. Furthermore, our experiment results indicates that there is a strong dependence of the volume of pores and the depth where these pores occur on both system parameters: Lower speeds induce a large pore volume, while the depth of the pores correlates better with increasing the laser power.

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Segmentation of the pores inside the object, where pores are denoted as transparent white voxels. (a) A 3D view with the borders of the object in gray. (b) A side view with the total number of segmented voxels in grayscale. Error lines are printed perpendicular to the screen/paper. The box around each group of pores is used to identify pores with the error line that created them. The middle of each green line is where the error line was printed, with the created pores below. (Color figure online)</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** (a) The number of pore voxels found below each error line, visualized as a contour graph in the PV diagram. Note the nonlinear color coding. (b) The average depth in µm of the pore voxels in an analogous visualization.</figcaption></figure>

## 3.2 System Settings Prediction

In this section, we investigate the performance of the tested CNNs for prediction of two prominent system parameters namely the laser speed and laser power from the off-axis camera images capturing the object printing process.

### Model-A Trained on Full-Frame Raw Video Recordings

As We explained in Sect. 2.3, Model-A is utilized to predict the laser settings from the raw captured video frames. Figure 5(a), (b) show the prediction error maps for the laser power and speed respectively. The error in the maps corresponds to the Mean of Absolute Difference (MAD) between the predicted and target settings. Results demonstrate reliable laser power predictions throughout majority of the setting space with prediction errors ≤ 50w particularly for large off-nominal settings. The best accuracy also belongs to low laser power and speeds. Laser speed predictions though prove to be more tricky especially for the high-laser speed regime where prediction errors can scale up to 300 mm/s. Similar to the Power predictions, best speed prediction accuracy achieved when both laser power and speed are substantially lower than nominal value.

Also, Fig. 5c shows the standard deviation of the prediction distribution for each point in our setting space which represents the dispersion or level of uncertainty for predictions per tested laser setting. The blue and red circles represent values for the laser speed and power respectively. It can be observed that the speed predictions are generally more dispersed than the power predictions. This dispersion is also more significant for large off-nominal speed settings on both high and low speed regimes.

### Model-B Trained on Extracted ROI from Raw Video Recording

Next, we would like to repeat the laser setting prediction experiment this time using Model-B with raw images redacted into only extracted ROIs. Figure 6(a), (b) show the corresponding prediction error maps where their colormap ranges has been kept equal to the ones of Fig. 5 for easier comparison. Here, in Fig. 6(a),
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Prediction errormap for (a) laser power and (b) for the laser speed for CNN trained on full-size image. The error corresponds to the mean absolute difference. The nominal setting is indicated by a red star. (c) Dispersion of predicted settings per sampling point demonstrated by the size of the red and blue circles for the laser power and speed respectively. The radius of each circle corresponds to the standard deviation of the prediction value distribution for the corresponding point in PV space. (Color figure online)</figcaption></figure>

a clear improvement on laser power predictions can be observed compared to the previous case. In Fig. 6(b) though, the laser speed prediction accuracy got poorer in general. However, compared to Fig. 5, here a rather distinct color gradient can be observed. In the error map of Fig. 6(a) highest and lowest accuracy indicated with light and dark blue respectively appear to be inversely related to the low and high laser power regimes. In the speed prediction error map (Fig. 6(b)) though, the direction of color gradient seems to be horizontal and again inversely related to the low and high laser speed regimes. From another perspective, Fig. 6c demonstrates large dispersion for speed predictions especially in low power regions.

Comparison of the results for these two experiments demonstrates that utilizing a low-complexity shallow CNN model (Model-B) combined with significantly reducing the model input size (by choosing only a small region of interest), not only improves the prediction runtime of our monitoring system effectively, but
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Prediction errmap for (a) laser power and (b) for the laser speed for CNN trained on ROI. The error corresponds to the mean absolute difference. The nominal setting is indicated by a red star. (c) Dispersion of predicted settings per sampling point demonstrated by the size of the red and blue circles for the laser power and speed respectively. The radius of each circle corresponds to the standard deviation of the prediction value distribution for the corresponding point in PV space. (Color figure online)</figcaption></figure>

also lead to better prediction results for laser power while retaining acceptable margin of error for the laser speed predictions across the whole laser setting space. Our further experiments demonstrated similar performance of Model-A when compared to Model-B. To avoid cluttering, in the following section we only present the analysis of the Model-B performance which is the main focus of the experiment due to faster run-time overall.

## 3.3 Pore Density Prediction

In the final part of our experiments, we would like to utilize the keyhole pore density information that we extracted from our CT scan data analysis to identify the performance of Model-B for direct prediction of keyhole pore density from the captured video. Apart from the ROI extracted input, we conduct an additional test for Model-B this time using the raw video inputs. This way we
can identify impact of proposed ROI extraction to the keyhole pore density
prediction performance of same model compared to using full-frame videos.

<figure><img src="image_10.png" /><figcaption>**Fig. 7.** Contour map of pore density (a) reference values extracted from the CT of the object and (b) corresponding predicted values with Model-B trained on full-frame raw video. The nominal setting is indicated by a red star. (Color figure online)</figcaption></figure>

*Model-B Trained on Full-Frame Video*

Figure 7(a) demonstrates the contour map for the detected pore densities in the CT scan of the object which we call them as the “target pores” hereafter. Figure 7(b) shows the prediction results of Model-B. While the pore prone region has been identified by the model, it is observed that amount of false positives in the non-porous regions of the PV space is significant. Also the scale of the predictions are not the same as the target pore densities as depicted in the colorbar ranges.

To better observe the difference between the target and predicted pore densi-
ties, we have calculated the error map as well, which can be seen in the Fig. 8(a).
The dispersion of the predictions per laser setting point is demonstrated in
Fig. 8(b). In both cases it is clear that the accuracy of the predictions and the
dispersion of their distributions don't demonstrate a reliable performance. One
particular point of attention is the area around the nominal settings (depicted
with an star in the center of PV space). The prediction model is particularly
expected to identify this region as the pore free setting area, but this is clearly
not the case here.

*Model-B Trained on Extracted ROI from Raw Video Recording*

Figure 9(a) shows the contour map of the target pores. Figure 9(b) shows the prediction results of Model-B. It is obvious that the general trend and pore prone region has been nicely detected by the model. Although, two points are important to notice here. First, despite being much less than the case of training
with full-frame video, there are still some false positive regions in the predictions close to the pore prone region. Second, similar to the full-size video input case, the scale of the predictions are not the same as the target pore densities as depicted in the colorbar ranges. Nonetheless this can be alleviated by a constant multiplication knowing that such issue exists.

<figure><img src="image_11.png" /><figcaption>**Fig. 8.** (a) Prediction err map for pore prediction using Model-B trained on full-frame raw video. The error corresponds to the mean absolute difference. (b) Dispersion of predicted pore density per sampling point in the PV space demonstrated by the size of the blue circles. The radius of each circle corresponds to the standard deviation of the prediction distribution for the corresponding point in setting space. (Color figure online)</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>**Fig. 9.** Contour map of keyhole pore density (a) reference values extracted from the CT of the object and (b) corresponding predicted values from Model-B trained on ROI. The nominal setting is indicated by a red star. (Color figure online)</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>**Fig. 10.** (a) Prediction errmap for pore prediction using Model-B trained on ROI. The error corresponds to the mean absolute difference. (b) Dispersion of predicted pore density per sampling point in the PV space demonstrated by the size of the blue circles. The radius of each circle corresponds to the standard deviation of the prediction distribution for the corresponding point in setting space. (Color figure online)</figcaption></figure>

The error map however, represents a much improved prediction accuracy which can be seen in the Fig. 10(a). The dispersion of the predictions demonstrated in Fig. 10(b) also shows improved dispersion for the prediction distributions.

Considering the fact that we used both full-frame video and the proposed ROI as the inputs for the same prediction model, it is demonstrated that utilizing the proposed ROI extraction clearly improves the prediction performance while it will provide a significant advantage on the prediction runtime of the monitoring system via substantial reduction of the input data.

# 4 Conclusions

In this paper we present an industrial machine learning usecase for the defect detection problem where highly pragmatic solutions are of the main interest. In this context, we utilized our LPBF machine to 3D print a metal object with a wide range of laser settings variations. Utilizing post-print CT scan of the print, we identified the boundaries of the keyhole pore subspace within the practical bounds of the laser PV space. Together with the high-framerate video clips captured from the printing process, we generated an extensive annotated dataset which is also publicly available upon request. To further ease the computational load of the prediction process, we propose using only a small region of interest from the full-size video frames which effectively redact each frame to slightly more than 3 percent of its original pixel count. Having much smaller input, we then opt for a compact CNN with only 4 hidden layers to further reduce the overall computational cost. Our results reveal that using proposed ROI instead of full-frame video, will improve the keyhole pore prediction by removing the redundant and noisy information which clutter and destabilize the prediction
of the network. We report that our proposed keyhole prediction pipeline provides a competitive performance across the laser PV space with highly improved computational cost. Nevertheless, we acknowledge that there is a lot of room for improvement on the prediction accuracy of the proposed pipeline as well as reducing the false positive cases. Also, the final verdict in terms of suitability of the proposed monitoring pipeline for a low-latency control feedback loop can only be achieved after fully integrating the pipeline into the hardware and control unit of the printer which remains for future works. We are hoping that the presented analysis together with access to our annotated dataset attracts more researches to address this and many other similar time-critical problems in the industry with more pragmatic solutions where low computational complexity and accuracy are equally valued.

**Acknowledgment.** This research is supported by the Flemish Innovation and Entrepreneurship Agency through the research project 'VIL-ICON' (project NO: HBC.2019.2808) and by Flanders Make (the strategic research center for the manufacturing industry & imec (the international research & development organization, active in the fields of nanoelectronics and digital technologies). The research is also supported by AI Flanders that is financed by Economie Wetenschap & Innovatie (https://flandersairesearch.be/en) and the CoE RAISE project (https://coe-raise.eu), which has received funding from the European Union's Horizon 2020 - Research and Innovation Framework Program H2020-INFRAEDI-2019-1 under grant agreement no. 951733.

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