W Zouhri

J Y Dantan

B Häfner

N Eschner

L Homri

G Lanza

O Theile

M Schäfer

Optical process monitoring for Laser-Powder Bed Fusion (L-PBF)

Keywords: Additive manufacturing, L-PBF, Quality process monitoring, Supervised learning

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Additive manufacturing (AM) is a fast-growing method that consists of creating parts or objects from 3D model data, usually by successively adding material layer by layer [START_REF] Busachi | A review of Additive Manufacturing technology and Cost Estimation techniques for the defence sector[END_REF]. AM has earned considerable attention from industry, due to its ability to produce custom, complex and lightweight parts for industrial applications [START_REF] Seifi | Progress Towards Metal Additive Manufacturing Standardization to Support Qualification and Certification[END_REF]. It is expected that, using AM processes, companies will be able to fully produce essential parts on-demand, as they will be able to reduce the lead-time and the production costs [START_REF] Seifi | Progress Towards Metal Additive Manufacturing Standardization to Support Qualification and Certification[END_REF]. For manufacturing metal parts, laser powder bed fusion (L-PBF) is one the most used additive manufacturing technologies [START_REF] Marrey | A Framework for Optimizing Process Parameters in Powder Bed Fusion (PBF) Process Using Artificial Neural Network (ANN)[END_REF]. L-PBF allows manufacturing components with good mechanical properties and complex shapes with small resolution (± 0.02 mm), which allowed the L-PBF technology to find several applications in different areas such as aerospace, manufacturing, medical etc. [START_REF] Ngo | Additive manufacturing (3D printing): A review of materials, methods, applications and challenges[END_REF]. However, the L-PBF process still suffers from some drawbacks, such as low reproducibility, porosity, and high residual stress, which are due to the process complexity that affects the part quality (more than 50 process parameters) [START_REF] Marrey | A Framework for Optimizing Process Parameters in Powder Bed Fusion (PBF) Process Using Artificial Neural Network (ANN)[END_REF].

To deal with the process variability and complexity, different monitoring techniques have been developed in order to improve the process reproducibility, and to ensure its reliability and quality [START_REF] Adam | Design for Additive Manufacturing-Element transitions and aggregated structures[END_REF]. These techniques allow a better understanding of the process through a systematic gathering and analysis of information. Generally, the monitoring techniques are based on consequent physical phenomena resulting from the laser beam interaction with the processed material. The phenomena are manifested as emissions that are either radiation, acoustic, or electromagnetic. Accordingly, being based on these emissions, the monitoring techniques can be divided into two main groups, which are optical and acoustical techniques [START_REF] Purtonen | Monitoring and Adaptive Control of Laser Processes[END_REF].

Optical monitoring methods

Optical methods are most frequently used for laser processing monitoring [START_REF] Purtonen | Monitoring and Adaptive Control of Laser Processes[END_REF]. Furumoto et al. [START_REF] Furumoto | Investigation of laser consolidation process for metal powder by two-color pyrometer and high-speed video camera[END_REF] investigated the behavior of the consolidation phenomena of powder during the selective laser sintering (SLS) and selective laser melting (SLM) processes. To do so, the surface temperature of the powder mixture in metallic additive manufacturing during laser beam irradiation was measured by two-color pyrometer, and the powder consolidation mechanism was monitored using a high-speed video camera. Therefore, by correlating the consolidation phenomena to the surface temperature, a better monitoring of the consolidation behavior became possible. Caggiano et al. [START_REF] Caggiano | Machine learning-based image processing for on-line defect recognition in additive manufacturing[END_REF] characterized layer-wise images of the SLM process using a new machine learning model based on bi-stream Deep Convolutional Neural Network (DCNN), in order to identify defects induced by process nonconformities. The proposed approach was evaluated, and the results were good with an accuracy of 99.4% in SLM defective conditions recognition.

Renken et al. [START_REF] Renken | Development of an adaptive, self-learning control concept for an additive manufacturing process[END_REF] presented a closed-loop control approach for the L-PBF process. The approach integrates a RGB sensor into the optical path of the laser beam. The sensor gathers the data of different manufactured parts resulting from different process parameters. These data are fed to an adapted self-learning strategy that updates the process parameters in order to optimize the quality of the produced parts. Also, Okaro et al. [START_REF] Okaro | Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning[END_REF] introduced a new semi-supervised machine learning approach for automatic detection of faults in L-PBF products. The approach extracts key features from large sets of photodiode data using Randomized Singular Value Decomposition, then, a Gaussian Mixture Model is trained to recognize builds that had been identified as 'faulty'. In order to identify the quality level of the PFB process, Zhang et al. [START_REF] Zhang | Extraction and evaluation of melt pool, plume and spatter information for powder-bed fusion AM process monitoring[END_REF] proposed an approach that consists at first of using a vision system with a high speed camera for process images acquisition, and extracting relevant features based on the AM process understanding. These features are then used as inputs for the Support Vector Machine (SVM) classifier, which allowed identifying the PFB process quality levels with an accuracy of 90.1%. Moreover, the raw acquired images were fed to a Convolutional Neural Network (CNN), and the PBF process quality level identification was slightly better with a 92.7% prediction accuracy.

In order to investigate the influence of SLM process parameters on the porosity development in AlSi10Mg alloy builds, Read et al. [START_REF] Read | Selective laser melting of AlSi10Mg alloy: Process optimisation and mechanical properties development[END_REF] put in place a Design of Experiments (DoE) that allowed having a statistical model that expresses the porosity in terms of these parameters. This statistical model allowed identifying the parameters that have the major influence on the porosity development. A genetic algorithm was used afterwards to define the optimal parameters values that minimize the porosity in the SLM builds. Imani et al. [START_REF] Imani | Layerwise In-Process Quality Monitoring in Laser Powder Bed Fusion[END_REF] investigated the L-PBF process conditions on the porosity development in Titanium alloy (Ti-6Al4V) parts. By varying the process parameters and by using X-ray computed tomography (XCT) images, the authors were able to quantify the number, size, and location of pores as a function of L-PBF process parameters. Using machine learning tools, the authors were also able to identify the process conditions that are likely to cause porosity with a statistical fidelity over 80% (F-score).

By using a high-speed digital camera, Zhang et al. [START_REF] Zhang | In-Process monitoring of porosity during laser additive manufacturing process[END_REF] developed a deep-learning-based method for porosity monitoring in laser additive manufacturing process. The authors designed a Convolutional Neural Network (CNN) that learns features from melt-pool data and allows predicting the porosity attributes in deposited specimens during laser additive manufacturing. A deep learning-based approach was developed by Yang et al. [START_REF] Yang | Investigation of Deep Learning for Real-Time Melt Pool Classification in Additive Manufacturing[END_REF] to investigate how the melt pool can be characterized in real-time for feedback control. A CNN was trained using 2763 melt pool images captured from a laser melting powder fusion and tested using another 2926 images. The proposed approach provided a faster detection of anomalies in melt pool formation compared to tradition methods (from 9.72 s to 0.99 s), as it showed good prediction performance with a classification accuracy of 91%. Another CNN-based approach that learns from layer wise powder bed images was proposed for autonomous detection and classification of anomalies related to interactions between the recoater blade, and the powder bed in L-PBF process [START_REF] Scime | A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process[END_REF]. This approach was tested on different materials, and it did show better performance when compared to other machine learning classifiers such as a Bag of Words (Bow) algorithm, and an AlexNet CNN model.

Acoustical monitoring methods

Acoustic sensors were also explored for laser processing monitoring. Rieder et al. [START_REF] Rieder | On-and offline ultrasonic characterization of components built by SLM additive manufacturing[END_REF] investigated the influence of the SLM process parameter 'laser power' on the quality of the manufactured components by resorting to an A-scans-based monitoring approach. The approach consists of collecting A-scans data during the manufacturing process, then to investigate the relationship between the buildup parameters and the acquired ultrasonic signals in order extract information about the dynamics of the layer build-up, the interface coupling, the local material properties, the formation of porosity, and distortions due to thermally induced residual stresses. Also, Slotwinski et al. [START_REF] Slotwinski | Porosity Measurements and Analysis for Metal Additive Manufacturing Process Control[END_REF] presented different measurement techniques for determining the porosity of SLM products, including an ultrasonic-based monitoring technique that links the ultrasonic velocity and the degree of the product's porosity. This technique was able to detect small absolute changes (~0.5 %) in porosity, which makes it suitable for SLM quality monitoring.

Zhu et al. [START_REF]Advances in Energy Science and Equipment Engineering II[END_REF] studied the acoustic signal generated during the SLM process by correlating it to the laser frequency, the laser power, and the laser scanning speed. A good mapping between acoustic signals and laser parameters has been found. This sets a good foundation for monitoring the process by acoustic signal, which allows improving the part quality during the powder-based laser sintering and melting processes. Another study [START_REF] Koester | In-situ acoustic signature monitoring in additive manufacturing processes[END_REF] emphasized the correlation between acoustic signals and the process noise. This correlation made it possible to identify different part anomalies such as cracks, solidification, keyhole collapse, and the process anomalies such as heat source/surface interaction, mechanical raster or bearing noise, particle impacts, etc.

Ye et al. [START_REF] Ye | Defect detection in selective laser melting technology by acoustic signals with deep belief networks[END_REF] proposed an approach to detect SLM parts defects. This approach consists of gathering acoustical signals by using a microphone, and classifying the different defects using a framework based on deep belief network (DBN). The experimental results showed the strong relationship between acoustical signals and tracks formation, which makes this approach practical for SLM quality monitoring. Moreover, the DBN classifier was able to reach high defect detection rate without resorting to feature extractions or signal preprocessing. Eschner et al. [START_REF] Eschner | Development of an acoustic process monitoring system for selective laser melting (SLM)[END_REF] developed an acoustic process monitoring system to distinguish different SLM process qualities. The most promising structure-borne sound concept was adopted and tested at first. Then cubes with different process qualities were produced according to a Design of Experiments (DoE) with specific process parameters. The acoustical data were transformed to the frequency domain via Fast Fourier transform (FFT), and then fed to an artificial neural network classifier. The proposed artificial neural network was able to classify the different cubes density (porosity) with a F1-score higher than 90%. A new in-situ real time quality monitoring method that combines acoustic emission and reinforcement learning was proposed by Wasmer et al. [START_REF] Wasmer | In Situ Quality Monitoring in AM Using Acoustic Emission: A Reinforcement Learning Approach[END_REF]. This method uses a fiber Bragg grating (FBG) sensor to collect acoustic data during a real manufacturing process, then it classifies the manufactured parts into poor, medium, and high quality in terms of porosity. The quality classification was performed on stainless steel 316L cubical specimens with a confidence level between 74% and 82%.

The short review above discussed different quality monitoring techniques for additive manufacturing laser processes. Most of these techniques aim to extract representative features of the observed phenomena from the gathered data (either optical or acoustical), using machine learning frameworks, or more specifically deep learning frameworks. These frameworks allow a better-quality monitoring of the process by linking the monitored data to an appropriate quality measure. Therefore, in this paper, an optical monitoring approach is developed for the evaluation of the quality of L-PBF process, and that by linking optical signals to the manufactured part density. Since these signals correspond to the different manufactured layers, the aim of the proposed approach can also be seen as to how to predict a global characteristic (final part density) from a local characteristic (layer's signal). To do so, relevant statistical features are extracted from optical data at first, and then they are fed to machine learning classifiers to identify the quality of the different manufactured parts. The approach is compared afterwards to deep learning frameworks that try to perform the quality classification from raw optical signals. This comparison indicates if the human knowledge (statistical features extraction) is important or not. The comparison is illustrated in Figure 1. In the following, section 2 defines the quality measure adopted in this work, as it presents the experimental setup and the followed Design of Experiment to manufacture the different parts. Section 3 depicts the proposed quality monitoring approach by applying it with two different machine learning classifiers. The proposed deep learning framework is presented in section 4. The different results are discussed in section 5, and finally a conclusion is given.

Instrumentation and Experiment

In this section, the quality measure is defined at first, then, the followed Design of Experiments and the monitoring setup are depicted, and finally, the quality measurement technique used is presented.

Definition of quality measure

According to the international standard ISO 9001:2015, "industrial products and their manufacturing processes must be designed to meet customer expectations through the specific engineering specifications of critical product characteristics" [START_REF] Sanchez-Marquez | Diagnosis of quality management systems using data analytics -A case study in the manufacturing sector[END_REF]. Therefore, it is required to define an appropriate quality measure specific to the manufactured products and their manufacturing process. Accordingly, for laser additive manufacturing processes, it is essential to assess the quality of a layerwise produced part. Spierings et al. [START_REF] Spierings | Comparison of density measurement techniques for additive manufactured metallic parts[END_REF] categorized the quality measures of a layer-wise produced part into three main topics:

-Mechanical parameters (density, mechanical strength, elongation to rupture, fatigue strength) -Surface quality -Dimensional accuracy Additionally, for all powder-based processes (L-PBF, SLS, etc.), it is almost impossible to produce parts with no internal pores. These pores are linked directly to the process parameters. The porosity affects the mechanical strength, fatigue strength and the elongation to rupture [START_REF] Spierings | Comparison of density measurement techniques for additive manufactured metallic parts[END_REF]. For that reason, a manufactured part should be sufficiently dense in order to avoid its failure during service [START_REF] Slotwinski | Porosity Measurements and Analysis for Metal Additive Manufacturing Process Control[END_REF].

Accordingly, and due to the useful process information it can provide, the density of the part (hence porosity) is defined as a quality measure in this article.

Experimental setup

Typically, the process parameters are optimized towards minimization of porosity to build parts with good mechanical properties. Therefore, in order to define a proper Design of Experiments (DoE), the process parameters that affect the density of the part need to be identified at first, then, a significant variation range should be assigned to each of these parameters.

Several studies were conducted to assess the effects of the L-PBF processing parameters on part density. It has been shown that a part porosity is correlated to the energy density (E), which is expressed in terms of the laser power (P), the scan speed (V), the hatch spacing (H), and the layer thickness (L) as shown in Equation ( 1) [START_REF] Tang | Prediction of lack-of-fusion porosity for powder bed fusion[END_REF]. Also, the optimal variation ranges that allow producing good quality parts were investigated by Eschner et al. [START_REF] Ye | Defect detection in selective laser melting technology by acoustic signals with deep belief networks[END_REF].

1 Accordingly, in this work, the L-PBF process parameters were intentionally tuned in order to manufacture 316 L stainless steel cubes with different densities, using the setup depicted in Figure 2. The laser power was varied between 80 Watt and 180 Watt, the scan speed between 200 mm/s and 1000 mm/s, the hatch spacing was 40µm and 50µm, and the layer thickness was fixed at 40µm, see Table 1. This DoE has allowed the testing of 18 configurations. Two cubes of 123 layers were then produced for each configuration. During the manufacturing of these cubes, the "Kleiber KGA 740-LO" pyrometer was used to record a corresponding signal for each layer. Pyrometers are well suited for monitoring the L-PBF process, since they allow recording and analyzing temperatures in a simple way, and without being in direct contact with the object being measured [START_REF] Everton | Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing[END_REF]. In particular, the "Kleiber KGA 740-LO" pyrometer has previously been used for monitoring the L-PBF process to record thermal emissions occurring at the melt pool [START_REF] Forien | Detecting keyhole pore defects and monitoring process signatures during laser powder bed fusion: A correlation between in situ pyrometry and ex situ X-ray radiography[END_REF]. Similarly, the signals in this work represent the recorded emissions occurring at the infrared wavelength range from the process zone.

Quality measurement

The Archimedes' method is used to measure the density (considered as quality measure) of the manufactured parts. This method is selected due to its easy, fast, and economic use compared to the "Micrograph of a cross-section" method and to the "X-ray scanning" method [START_REF] Spierings | Comparison of density measurement techniques for additive manufactured metallic parts[END_REF]. In this study, every cube was measured 5 times in order to take into account the measurement uncertainties. Table 2 presents the different measured densities of the 36 manufactured cubes. After manufacturing the cubical specimens according to the DoE, and after gathering the pyrometer data of every layer, the next section presents the L-PBF quality monitoring approach that links the optical data to the density of the manufactured parts.

Statistical Feature-based Approach for Quality Classification of L-PBF Parts.

In this section a new quality monitoring approach for the L-PBF process is introduced. The approach aims to identify the density of a part's layer based on its optical signal. To do so, relevant statistical features are extracted from each raw signal, then they are fed to a machine learning classifier in order to create a model for density prediction. In this approach two classifiers are optimized and tested, which are support vector machine (SVM) and multi-layer perceptron (MLP). The prediction accuracies of these two classifiers are compared in order to identify which one allows a better density prediction. Figure 3 gives a comprehensive description of the proposed approach.

Data set Preparation

The first step of the proposed approach consists of generating a dataset from the gathered optical data, where each row represents the extracted features from a raw signal. To do so, the following tasks are performed:

-Selection and extraction of the statistical features -Labeling the data -Splitting the data into training, validation, and test sets

The statistical features are validated at the end of the approach based on how the assessment of the prediction accuracy is. In the following, Table 3 lists the different statistical features selected for the proposed monitoring approach: With 36 cubes, each of which consists of 123 layers, a dataset of 4402 instances and 10 features was generated. Also, to complete the dataset, the datapoints are labeled. To do so, it is necessary to define the different density classes. As there is no uniform standard in the industry from which class boundaries can be derived [START_REF] Eschner | Classification of specimen density in Laser Powder Bed Fusion (L-PBF) using in-process structure-borne acoustic process emissions[END_REF], care has been taken to ensure that the resulting classes are as balanced as possible. A balanced dataset of 3 density classes (low, medium, and high) was therefore generated in this study, which means that each class consists of 12 cubes. Hence, as shown in Figure 4, all cubical specimens with densities higher than 98.15% are considered as high-quality products (Class 1), while the ones with a density lower 96.13% are consider as low-quality products (Class 3), which leaves the parts with a density between 96.13% and 98.15% as the cubes of medium-quality (Class 2). Once complete, the dataset is normalized and then randomly split into:

-Training set (50%): 6 cubes are randomly chosen from each class -Validation set (25%): 3 cubes are randomly chosen from each class -Test set (25%): 3 cubes are randomly chosen from each class Table 4 presents the density class of each part, as it shows in which set it does belong to. In the following, two machine learning classifiers are applied in order to train a density classification model.

Density classification with support vector machine

Support vector machines (SVMs) are defined as supervised machine learning algorithms for binary classification [START_REF] Rostami | Review of data mining applications for quality assessment in manufacturing industry: support vector machines[END_REF]. separates the two classes, then to attempt to make correct predictions on novel data. Still, SVMs can be used for multi-classification by training as many SVM models as the number of the classes' combinations. In this study, having three classes results in having three combinations. Thus, three SVM models are trained and optimized.

To optimize the performance of SVMs, a genetic algorithm was developed in order to identify the best hyperparameters that allow maximizing the prediction performance of the SVM. The hyperparameters of the SVM are:

-Kernel used for non-linearly separation -Kernel parameters -Regularization parameter "C" that controls the trade-off between achieving a low error on the training data and maximizing the width of the margin Additionally, the genetic algorithm used to tune the hyperparameters of SVM, was applied with the following parameters:

-Crossover: uniform -Selection: tournament selection -Mutation: random resetting -Fitness: prediction accuracy of SVM on the validation set

The optimal hyperparameters resulting from the application of the genetic algorithm on the three SVM models are given in Table 5. The multi-classification using the three trained SVM models is depicted as illustrated in Figure 5. The multi-classification by SVM was applied on the whole dataset in order to predict the different density classes. Table 6 presents the different accuracies, where the overall accuracy represents the prediction accuracy of the entire dataset.

Accuracy on training set

Accuracy on validation set

Accuracy on test set

Overall accuracy

93.78% 99.09% 90.07% 94.18%

The SVM show a very good performance in learning from the statistical features, with a prediction accuracy above 93%. Also, the generalization ability of SVM is good, as the accuracies on the validation set and the test set are 99% and 90%, respectively. These results show that there is a significant correlation between the density and the optical signals.

Density classification with multilayer perceptron

A multilayer perceptron (MLP) is a type of artificial neural networks organized in several layers in which information flows from the input layer to the output layer [START_REF] Qi | Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives[END_REF]. Each layer is made up of a variable number of neurons, the neurons of the output layer being the outputs of the overall system.

In MLP, all neurons have connections between them, and these connections are identified by associated weights. MLP models operate similarly to SVMs, where the input is transformed using a non-linear transformation. However, unlike SVMs that perform binary classifications, MLP structures allow performing multi-classification tasks, i.e., only one structure is trained and optimized. In the following, the MLP hyperparameters and their variation ranges are identified, then a suitable optimization method is selected.

Generally, the first hyperparameters that define an MLP are the number of hidden layers, and the number of nodes in each hidden layer. It is commonly known that there are no general rules that allow defining these two hyperparameters, still, some rules-of-thumb are defined empirically. Among these rules-of-thumb, the one most commonly relied on is "the optimal nodes of a hidden layer is usually between the size of the input and size of the output layers" [START_REF] Sheela | Review on Methods to Fix Number of Hidden Neurons in Neural Networks[END_REF]. Moreover, the batch normalization is also tested in order to see if this regularization technique is relevant. The batch size is here varied between 32 and 512, and the number of epochs is fixed at 200 with a defined early stopping that interrupts the training phase once the validation accuracy stagnates. Also, the Adam optimizer is used, as it combines the advantages of AdaGrad in dealing with sparse gradients, and the ability of RMSProp in dealing with non-stationary objectives [START_REF] Kingma | Adam: A Method for Stochastic Optimization[END_REF]. Finally, the Relu function that accelerates the convergence of an artificial neural network is used as an activation function [START_REF] Forien | Detecting keyhole pore defects and monitoring process signatures during laser powder bed fusion: A correlation between in situ pyrometry and ex situ X-ray radiography[END_REF], and the He-Normal as a weight initializer. Based on this knowledge the hyperparameters and their variation ranges are identified as summarized in Table 7. Adam To define an optimal MLP artificial neural network, a grid search is performed testing all the different combinations of these hyperparameters (8190 combinations). This grid search allowed identifying an optimal MLP artificial neural network with the following characteristics:

-Hidden layers = 2 -Nodes of hidden layer 1 = 8 -Nodes of hidden layer 2 = 5 -Batch normalization = On -Batch size = 128

This MLP structure allowed getting the following results, see Table 8. The MLP also shows a very good performance in density prediction, with an overall accuracy slightly better compared to SVM. These results prove how the density is well correlated to the optical signals, as they validate the selected statistical features, and their ability in predicting the different density classes. In the following, the application of deep learning frameworks for density prediction is presented in section 4, and further discussions and analysis of the results are conducted in section 5.

Deep Learning Approach for Quality Classification of L-PBF Parts Based on Raw Signals

The aim of this section is to investigate a deep learning approach for L-PBF process quality monitoring. The main objective of the approach is the prediction of the density of the manufactured parts from the raw optical signals. A multilayer perceptron (MLP) and one dimensional convolutional neural network (1D-CNN) are tested in this article. The prediction performance of these two types of artificial neural networks are compared to the results of the quality monitoring approach defined in section 3. This comparison allows assessing the relevance of the identified statistical features, hence, assessing the relevance of the expert knowledge. Figure 6 gives a global illustration of the deep learning approach for density classification. Deep Neural Networks (DNNs) have been used during the last decade in many fields for pattern recognition and classification tasks by processing different types of data, such as images, or sequential data like text and audio [START_REF] Fawaz | Deep learning for time series classification: a review[END_REF]. Still, one of the most challenging problems in data mining is Time Series Classification (TSC) [START_REF] Esling | Time-series data mining[END_REF]. A TSC problem is defined as any classification problem that handles data registered according to a notion of ordering [START_REF] Gamboa | Deep Learning for Time-Series Analysis[END_REF]. Therefore, signal classification is regarded as a TSC problem. To deal with TSC problems, many algorithms have been proposed, such as the traditional Nearest Neighbors coupled with the Dynamic Time Wrapping Distance (NN-DTW) [START_REF] Bagnall | The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances[END_REF], or ensemble methods that include many classifiers like SVM and Decision Trees (DT) [START_REF] Bagnall | Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles[END_REF]. Remarkably, DNNs have not been used a lot to deal with TSC problems [START_REF] Fawaz | Deep learning for time series classification: a review[END_REF]. Nevertheless, some works have been conducted in order to define the most suitable type of DNNs for Time Series Classification by studying different datasets [START_REF] Fawaz | Deep learning for time series classification: a review[END_REF], [START_REF] Wang | Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline[END_REF]. The CNN performances in TSC problems did outperform other DNNs such MLP and RNNs (Recurrent Neural Networks). In particular, 1D-CNNs have been used for TSC problems, and the results were promising [START_REF] Sadouk | CNN Approaches for Time Series Classification[END_REF], [START_REF]Convolutional neural networks for time series classification[END_REF]. Accordingly, to classify the part density from optical signals, 1D-CNNs are tested in this article, as well as MLPs due to their easy implementation.

Having signals with different lengths, a linear interpolation was performed in order to up-sample all the optical signals. This interpolation allowed having a dataset of 4402 observations and 500000 columns, where each one of these columns represents a timestep. Also, in order to verify if the interpolation has an impact on the prediction performance, the approach defined in section 3 was applied to these interpolated signals. The results are given in Table 9. The results show that even if the signals are interpolated, density classification is possible with prediction accuracies similar to the results found in section 3. It can be concluded that the upsampling does not affect the link between the optical signal and the part density.

The interpolated signals were fed in to MLPs models with bottleneck layers, where the hidden layers were varied between three, four, five, and six, and the nodes were either, 50, 100, 200, or 500. The different MLP structures were trained using the Relu activation function, the ADAM optimizer, and the He-normal weights initializer. Testing all these MLP structures allowed getting the following density prediction accuracies, see Figure 7 and Figure 8. These results show that the MLP structures tested in this work are unable to predict well the different density classes, where the best structure allowed having an overall accuracy of 82.96% and a generalization accuracy of 71.96% calculated as average of the validation accuracy and test accuracy.

An alternative attempt was conducted using 1D-CNNs in order to predict the density classes from the raw signals. Two deep learning architectures derived from AlexNet and VGG models were tested and evaluated. The deep learning architectures were defined as shown in Figure 9. The architectures were then trained by varying the number of filters between 8 and 32, the strides between 1 and 4, and the filter size between 2x1 and 5x1. Additionally, the Relu activation function, the ADAM optimizer, and the He-normal weights initializer were used to train the networks. The best out of all these networks is depicted in Figure 10. Compared to MLP models, 1D-CNNs showed better prediction performances. The model illustrated in Figure 10 was able to identify the different density classes with an accuracy of 94.88% on the training set, and with accuracies of 88.54% and 81.24% on the validation set and the test set, respectively. Still, these results are inferior to the ones found in section 3, which emphasize the importance of the expert knowledge in data preprocessing and assisting the machine learning methods for a better model performance. For further analysis, the next section discusses the different results of section 3 and section 4.

Discussions and Conclusion

In this section, the results of the different approaches for density prediction are analyzed and discussed. Table 10 summarizes the results of the L-PBF quality monitoring approach based on the statistical features, and the results of the deep learning approach based on raw data. Then, different conclusions are derived from this. Therefore, by analyzing the results, the following conclusions can be made:

-The identified statistical features are validated due to their ability in allowing the prediction of the three density/quality classes of the L-PBF parts with an accuracy over 90%, which proves the strong link between the optical signals of layers and the density of the final part. -The machine learning classifiers, MLP and SVM, could be recommended for use in the statistical features-based approach, as they both showed similar predictive performance on either the training data or the test data. -The generalization performances of the statistical features-based approach did outperform the deep learning framework in predicting the different density classes. -Compared to the proposed statistical-based approach, the 1D-CNN is a time and resource consuming method, either for training the model or predicting novel data.

These results prove that even if the prediction performances of the different deep learning methods are quite fascinating, the human knowledge is still very relevant, especially if it is combined with suitable machine learning techniques. Also, being a fast response approach, the statistical featurebased approach can be used in the L-PBF process as an in-process quality monitoring technique by predicting the density of a layer from its optical signal, which can allow improving the process efficiency, and quality, e.g., predicting the density class of the final product in the early stages of the manufacturing process would make it possible to prevent costly end-of-line quality assurance.

To conclude, in this article, several L-PBF quality monitoring approach were introduced and compared. The approaches are based on gathering representative optical data using a pyrometer type Kleiber KGA 740-LO. On the one hand, statistical features were extracted from these monitored data, then they were fed to a machine learning classifier in order to identify if a manufactured part is of a low, medium, or high quality, and that by predicting its density. The approach was tested on 316L stainless steel cubical specimens manufactured with different process parameters. The results showed good density prediction performance, achieving accuracies over 90%. On the other hand, the results of the proposed approach were compared to a deep learning approach predicting the part density from the raw signals. This comparison emphasized the relevance of the expert knowledge and its role in assisting the machine learning methods to be faster and more accurate. by defining the statistical features.

L-PBF quality

Figure 1 :

 1 Figure 1: Optical quality monitoring approaches for L-PBF process.

  Figure 2: The L-PBF monitoring setup.

Figure 3 :

 3 Figure 3: Statistical feature-based approach for quality classification of L-PBF parts.

Figure 4 :

 4 Figure 4: Labels of the manufactured cubes.

Figure 5 :

 5 Figure 5: Multi-classification by SVM.

Figure 6 :

 6 Figure 6: Deep learning framework for quality classification of L-PBF parts.

Figure 7 :

 7 Figure 7: Overall accuracies of the tested MLPs structures based on interpolated raw signals.

Figure 8 :

 8 Figure 8: Average of validation and test accuracies of the tested MLPs structures.

Figure 9 :

 9 Figure 9: 1D-CNN architectures tested.

Figure 10 :

 10 Figure 10: Optimal 1D-CNN model.

Table 1 :

 1 Parameter set for Design of Experiments.

	Parameter		Values
	Laser power (P)	80 W	130 W	180 W
	Scan speed (V)	200 mm/s	400 mm/s	1000 mm/s
	Hatch spacing (H)	40 µm	50 µm	-
	Layer thickness (L)	40 µm	-	-

Table 2 :

 2 Design of Experiments.

	Configuration	Laser power (P)	Scan speed (V)	Hatch spacing (H)	Layer thickness (L)	Density part 1	Density part 2
	1	80W	200 mm/s	40µm	40µm	97.83%	97.78%
	2	80W	400 mm/s	40µm	40µm	95.89%	95.46%
	3	80W	1000 mm/s	40µm	40µm	94.58%	95.73%
	4	130W	200 mm/s	40µm	40µm	98.76%	98.88%
	5	130W	400 mm/s	40µm	40µm	98.33%	98.75%
	6	130W	1000 mm/s	40µm	40µm	96.68%	96.35%
	7	180W	200 mm/s	40µm	40µm	95.09%	93.61%
	8	180W	400 mm/s	40µm	40µm	98.55%	99.03%
	9	180W	1000 mm/s	40µm	40µm	98.13%	98.00%
	10	80W	200 mm/s	50µm	40µm	97.65%	97.76%
	11	80W	400 mm/s	50µm	40µm	95.28%	95.38%
	12	80W	1000 mm/s	50µm	40µm	95.80%	95.90%
	13	130W	200 mm/s	50µm	40µm	98.55%	98.56%
	14	130W	400 mm/s	50µm	40µm	98.01%	98.20%
	15	130W	1000 mm/s	50µm	40µm	95.62%	95.56%
	16	180W	200 mm/s	50µm	40µm	98.16%	97.14%
	17	180W	400 mm/s	50µm	40µm	98.56%	98.61%
	18	180W	1000 mm/s	50µm	40µm	97.77%	97.88%

Table 3 :

 3 List of extracted statistical features.

Table 4 :

 4 Class and set of every manufactured cube.

	Configuration	Density part 1	Class	Set	Density part 2	Class	Set
	1	97.83%	2	Test	97.78%	2	Validation
	2	95.89%	3	Test	95.46%	3	Training
	3	94.58%	3	Test	95.73%	3	Training
	4	98.76%	1	Training	98.88%	1	Test
	5	98.33%	1	Training	98.75%	1	Test
	6	96.68%	2	Training	96.35%	2	Validation
	7	95.09%	3	Training	93.61%	3	Test
	8	98.55%	1	Training	99.03%	1	Validation
	9	98.13%	2	Validation	98.00%	2	Training
	10	97.65%	2	Training	97.76%	2	Test
	11	95.28%	3	Training	95.38%	3	Training
	12	95.80%	3	Validation	95.90%	3	Validation
	13	98.55%	1	Validation	98.56%	1	Validation
	14	98.01%	2	Training	98.20%	1	Training
	15	95.62%	3	Validation	95.56%	3	Training
	16	98.16%	1	Test	97.14%	2	Training
	17	98.56%	1	Training	98.61%	1	Training
	18	97.77%	2	Test	97.88%	2	Training

Table 5 :

 5 Optimal hyperparameters of the SVM.

	SVM model	Kernel	Gamma	C	Accuracy of validation set
	Class1 vs Class2	RBF	0.07	79	99.18%
	Class1 vs Class3	RBF	1.851	983	100.00%
	Class2 vs Class3	RBF	5.651	16	99.45%

Table 6 :

 6 Prediction accuracy of SVM.

Table 7 :

 7 MLP hyperparameters to tune.

	Hyperparameter	Variation range
	Hidden layers	1-3
	Nodes	3-11
	Batch size	{32,64,128,256,512}
	Batch normalization	{On,Off}
	Epochs	200 -with early stopping-
	Weight initiliazer	He-normal
	Activation function	Relu
	Optimizer

Table 8 :

 8 Prediction accuracy of MLP.

	Accuracy on	Accuracy on validation	Accuracy on	Overall
	training set	set	test set	accuracy
	93.46%	98.36%	91.98%	94.32%

Table 9 :

 9 Prediction accuracy of SVM and MLP from interpolated raw signals.

	Classifier	Accuracy on training set	Accuracy on validation set	Accuracy on test set	Overall Accuracy
	SVM	93.88%	99.55%	89.80%	94.27%
	MLP	93.92%	98.36%	91.35%	94.39%

Table 10 :

 10 Summary of the two proposed approaches using statistical features and raw data.

Acknowledgement

The authors gratefully acknowledge the support by Siemens AG, the French -German Institute for «Industry of the Future», and sthe member of the research chair on reconfigurable, safe, and efficient production systems, co-founded by Thyssenkrupp, the European Regional Development Fund Programme opérationnel FEDER-FSE Lorraine et Massif de Vosges 2014-2020, UIMM F2I and UIMM.

To further explore its potential, the statistical feature-based approach should be tested on a larger dataset, and on parts with more complex geometries. This will further improve the robustness of the approach as it will allow a better monitoring of the L-PBF process.

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.


F Cleymand
email: franck.cleymand@univ-lorraine.fr

A Mamanov

A Poerio

K Elkhoury

J P Jehl

C J F Kahn

M Ponçot

E Arab-Tehrany

João F Mano

Development of novel Chitosan / Guar Gum inks for extrusionbased 3D Bioprinting: Process, Printability and Properties

Keywords: Chitosan, Guar gum, Ink, 3D Bioprinting, Hydrogel

The major limitation of 3D bioprinting is the availability of inks. In order to develop new ink formulations, both their rheological behavior to obtain the best printability and the target bioprinted objects conformities must be studied. In this paper, the preparation and the characterization of novel ink formulations based on two natural polysaccharides, chitosan (CH) and guar gum (GG), are presented for the first time, in our knowledge. Five ink formulations containing different proportions of CH and GG were prepared and characterized in terms of rheological properties and solvent evaporation. Their printability was assessed (by varying nozzle diameter, pressure and speed) using an extrusion-based bio-printing process performed directly in air at 37°C. Results showed that the incorporation of GG improved both the printability of the pure chitosan inks by increasing the viscosity of the solution and the shape fidelity by accelerating the solvent evaporation. We showed that the ink containing 15% of GG

Introduction

Due to the persistent problem of organ shortage, new artificial methods have been developed in order to find alternative solutions to transplantation [1][START_REF] Hoffman | Hydrogels for biomedical applications[END_REF][START_REF] Elkhoury | Soft-Nanoparticle Functionalization of Natural Hydrogels for Tissue Engineering Applications[END_REF]. 3D bioprinting has emerged as a promising method to fabricate complex devices to regenerate or replace tissues and organs that could be processed using natural polymers [START_REF] Kang | A 3D bioprinting system to produce human-scale tissue constructs with structural integrity[END_REF]. Among the different technologies, extrusionbased 3D bioprinting is the most commonly used in the field of health care applications [START_REF] Murphy | 3D bioprinting of tissues and organs[END_REF]. Two approaches can be used for the extrusion-based bioprinting: the older one that consist on the deposition of the bioink on a flat surface in air and a more recent one where the bioink is extruded into a suspension bath that prevent the material to collapse [START_REF] Mccormack | 3D Printing in Suspension Baths: Keeping the Promises of Bioprinting Afloat[END_REF]. However, the first one offers lower cost processability and less complexity. Despite the great progresses that have already been made, extrusion-based 3D bioprinting is still at its early stages and there are still lots of improvements to be made concerning the bioink availability [START_REF] Panwar | Current Status of Bioinks for Micro-Extrusion-Based 3D Bioprinting[END_REF]. One of the biggest limitations of this technology is the development of bioinks that, in form of hydrogels, present adequate mechanical properties to ensure a good printability and shape fidelity post-printing [START_REF] Jin | Self-Supporting Nanoclay as Internal Scaffold Material for Direct Printing of Soft Hydrogel Composite Structures in Air[END_REF] without compromising the cell viability [START_REF] Gillispie | Assessment methodologies for extrusion-based bioink printability[END_REF]. Either synthetic or natural materials can be used to prepare bioinks, which generally show opposite characteristics. In fact, while synthetic materials present strong mechanical properties simplifying the printing process and shape fidelity of the printed constructs, the natural ones present low immunogenicity and higher biocompatibility, biodegradability, and bioactivity properties [START_REF] Gopinathan | Recent trends in bioinks for 3D printing[END_REF]. Among the different biomaterials, natural polymers have been gaining increased interest because of their ability to mimic the extracellular matrix (ECM), offering the adequate environment to support cell proliferation and viability [START_REF] Fedorovich | Hydrogels as Extracellular Matrices for Skeletal Tissue Engineering: State-of-the-Art and Novel Application in Organ Printing[END_REF][START_REF] Mano | Natural origin biodegradable systems in tissue engineering and regenerative medicine: present status and some moving trends[END_REF]. Chitosan (CH) is the second most abundant biopolymer found in nature, obtained by the deacetylation of chitin that can be extracted from the exoskeleton of crustaceans [START_REF] Muzzarelli | Chitosan Chemistry: Relevance to the Biomedical Sciences[END_REF]. CH has several biological properties such as biocompatibility, biodegradability and bioactivity [START_REF] Rao | Chitosan and guar gum composite films: Preparation, physical, mechanical and antimicrobial properties[END_REF].

Furthermore, it has antimicrobial properties that make it suitable for tissue engineering applications [START_REF] Kong | Antimicrobial properties of chitosan and mode of action: A state of the art review[END_REF]. However, because of the lack of strong mechanical properties and slow rate of gelation (that is important for the shape fidelity after printing) CH alone is not suitable for proper and easy printing [START_REF] Demirtaş | A bioprintable form of chitosan hydrogel for bone tissue engineering[END_REF]. Therefore other macromolecules are often necessary. CH was already mixed with synthetic materials like PLA [START_REF] Almeida | Impact of 3-D printed PLA-and chitosan-based scaffolds on human monocyte/macrophage responses: Unraveling the effect of 3-D structures on inflammation[END_REF] or hydroxyapatite [START_REF] Ang | Fabrication of 3D chitosan-hydroxyapatite scaffolds using a robotic dispensing system[END_REF], natural ones like gelatin [START_REF] Ng | Development of Polyelectrolyte Chitosan-gelatin Hydrogels for Skin Bioprinting[END_REF] and catechol [START_REF] Lee | Chitosan-catechol: a writable bioink under serum culture media[END_REF] to produce bioinks for 3D bioprinting. We hypothesize that the combination of CH with other natural polysaccharides, such as Guar Gum (GG), could create a new component of bioink suitable for the extrusion-based 3D bioprinting. GG is a water-soluble microbial polysaccharide, composed of a linear chain of 1,4-linked mannose residues to which galactose residues are 1,6-linked at every second mannose [START_REF] Srivastava | Seed Galactomannans: An Overview[END_REF]. GG has been used as a stiffener agent and appropriate for many pharmaceutical and food applications [START_REF] Mohammadinejad | Recent Advances in Natural Gum-Based Biomaterials for Tissue Engineering and Regenerative Medicine: A Review[END_REF]. For example, CH-GG hydrogels have been used to create transdermal patch for the delivery of paracetamol and showed low cytotoxicity and antimicrobial activity in vitro [START_REF] Sami | Formulation of novel chitosan guargum based hydrogels for sustained drug release of paracetamol[END_REF]. Furthermore, thanks to its ability of forming gels in water, GG is used to increase the viscosity of solutions representing a great candidate as a component of bioinks [START_REF] Mudgil | Guar gum: processing, properties and food applications-A Review[END_REF]. The aim of this study was to develop CH/GG ink formulation using different proportions of the two polymers and optimizing the printing parameters in order to achieve a good printability. Ink formulations were then characterized in terms of viscosity, solvent evaporation, dimensional stability and shape fidelity before and after gelation by neutralization. Membranes created using the formulation that showed the best printability were object of further studies such as infrared spectroscopy, X-ray diffraction and mechanical properties (Young's modulus, strength and strain at break), before and after gelation.

We will show that the inclusion of GG improved the mechanical properties and consequently the printability of pure CH as well as its dimensional stability leading to a better post bio-printing behavior.

Materials and methods

Materials

Chitosan (CH) 90% deacetylated, 15 mPa.s viscosity was purchased from Heppe Medical Chitosan GmbH, Germany. Guar Gum (GG), Citric Acid monohydrate (CA) and Acetic Acid (AA) > 99.8% were obtained from Sigma-Aldrich. Ethanol absolute came from Carlo Erba.

NaOH was obtained from VWR.

Inks formulations

Ink formulations were adapted from the previous methods used to elaborate films [START_REF] Rao | Chitosan and guar gum composite films: Preparation, physical, mechanical and antimicrobial properties[END_REF][START_REF] Tang | Preparation and properties of chitosan/guar gum/nanocrystalline cellulose nanocomposite films[END_REF][START_REF] Wu | Processing and Properties of Chitosan Inks for 3D Printing of Hydrogel Microstructures[END_REF].

Pure CH solution and pure GG solution were mixed in different proportions to create the ink formulations (labelled C100, C95, C85, C75 and C65) described in Table 1, by pouring GG into CH [START_REF] Tang | Preparation and properties of chitosan/guar gum/nanocrystalline cellulose nanocomposite films[END_REF]. Pure CH solutions were prepared by dissolving 10% w/v CH in a 40% (v/v) acetic acid solution in Deionized Water (DW) at 25°C. Then, 3% (w/v) CA followed by 10% w/v CH was slowly added to the solution, and mechanical mixed overnight. Then, the solution was centrifuged at 3,000 rpm for 10 min (Heraeus Multifuge X) and kept to rest for 2 hours at 25°C to remove undissolved particles and air bubbles. To prepare pure GG solution, 1.5% w/v GG was first mixed with absolute ethanol (0.5 mL of ethanol for 100 mg of GG) to avoid GG aggregation in the solution, then poured into DW at 80°C and mixed for 5 mins [START_REF] Yagoub | The influence of thermal treatment on physical properties of guar gum[END_REF]. Then, the solution was kept resting for 2 hours at 25°C to let the viscosity increase with the decrease of the temperature.

Finally, different proportions of pure CH and pure GG solutions were mixed under agitation at 70°C for 30 mins and kept for 1 h at 25°C. Then, each ink formulation was centrifuged at 3,000 rpm for 10 mins to eliminate all remaining air bubbles, before being poured into a syringe.

Syringes were sealed, covered with aluminum foil and stored in a fridge at 4°C until usage. The density of each ink formulation, calculated by measuring the mass of 1 mL of ink (in triplicate), is presented in Table 1

3D Bioprinting processes

In this study, a 4th generation 3D-Bioplotter from EnvisionTEC controlled with Visual Machine software, was used to perform all the printing processes. This bioprinter was equipped with a temperature-controlled platform (-10 to 80°C) fixed at 15°C and a low temperature dispensing head (0 to 70°C) fixed at 37°C to print all the ink formulations under uniform conditions. The ink was inserted in a 30 mL syringe equipped with a blue end cap to prevent ink flow before use.

Then a white SmoothFlow TM piston was inserted and the syringe was fixed to a high-pressure blue adapter (Nordson EFD). Ink formulations were extruded through SmoothFlow TM tapered micronozzle (22 G: 410 µm, 25 G: 250 µm, 27 G: 200 µm, Nordson EFD) under applied pressure controlled by an air-powered dispensing system.

Rheological characterization of the ink formulations.

Shear viscosities and shear thinning property of the ink formulations were determined using a rotational rheometer Malvern Kinexus TM (Malvern Instruments Ltd., Malvern, UK) fitted with a cone plate geometry (4° and 40 mm diameter) at shear rates range of 0.01-1000 s -1 at 37°C. The edge of the cone-plate was covered with mineral oil to prevent solvent evaporation.

Solvent content and evaporation rate

The variation in the solvent content and the evaporation rates of all CH/GG ink formulations were investigated. Individual lines (length: 78 mm), called filaments in the following, were extruded through a 25 G micronozzle at a robot head speed of 5 mm.s -1 and at a pressure of 1.2 bars. The weight loss, representative of a decrease in the solvent content, was measured using a high precision balance (OHAUS Adventurer Pro) for 3-4 hours and after drying the filaments for 2 days at 25°C. The evaporation rate was then calculated by dividing the weight variation by the initial weight of printed filaments.

Determination of optimal bio-printing parameters

The flow rate of the five ink compositions under different pressures and nozzles were determined. In order to study the effect of the printing parameters, filaments (length: 78 mm) were printed on a Petri dish using the 5 ink formulations (presented in Table 1) under different pressures, robot head speed and extruded from different nozzles (22, 25 and 27 G). The images of the printed filaments were captured with an optical binocular Stevui S11. Three random parts of each filament (from three samples per each group) were measured and the best printability was obtained for the ink formulation that presented the lowest filament diameter (d), the lowest variation between the theoretical surface (Sth) and the real surface (Sr), the lowest sprawl and the highest dimensional stability. The ink formulation C65 was used to select the appropriate nozzle size. In order to do this, the ink was extruded using the 25 and 27 G nozzles at a fixed speed of 5 mm.s -1 under a pressure range of 0.9-2.5 bars. Then, the formulation C85, printed using a 27 G micronozzle, was used to evaluate the influence of different robot head speed (15, 20, 25 and 30 mm.s -1 ) and extrusion pressure (from 1.5 to 4 bars). Lastly, the influence of the ink composition on the diameter of printed filaments was assessed by printing the 5 different ink formulations with a 25 G micronozzle, a robot head speed of 25 mm.s -1 and a pressure range between 0.9 and 2.5 bars. Eventually, the ink formulation C85 was selected as an optimal composition for extrusion based-printing and was the object of further studies after printing.

Gelation by Neutralization

After printing C85 rectangular membranes (width = 20 mm, length = 80 mm) were printed using a 25 G micronozzle, under a pressure of 1.5 bar and a robot head speed of 10 mm.s -1 . Gelation was obtained by immersing membranes in a 1M sodium hydroxide (NaOH) solution for 18h.

After this time, membranes were washed with DW to remove excess of NaOH until neutral pH was achieved and dried in an oven at 50°C for 4h. For this and the following studies, neutralised membranes in a wet state will be named NW, while neutralized membranes in a dry state will be named ND. Similarly, as-printed membranes in a wet state will be named APW, while asprinted membranes in a dry state will be named APD.

Dimensional stability analysis

To determine the effect of gelation on dimensional stability, neutralized C85-based membranes (length=79 mm, width=39.5 mm) were compared to as-printed ones. The size variation (length and width) was measured using Panasis 2.3.3 Software while they were drying at 25°C, at 0, 2, 4, 18 and 24 hours. All measurements were performed in triplicate.

X-ray diffraction (XRD)

XRD spectra of as-printed in a dried state (APD) and neutralized in a dried state (ND) C85-based membranes were recorded using a D8 Advance diffractometer. Data were collected in the 2θ scan angle range of 5-55° with a scan rate of 0.6°/min (2s/step) and 0.02°/step. The machine was set at 40 kV and 40 mA at RT. All samples were dried in an oven during 4h at 50°C before use.

Fourier transform infrared spectroscopy (FTIR)

FTIR spectra were obtained with a Thermo Scientific Nicolet machine on as-printed in a dried state (APD) and neutralized in dried state (ND) C85-based membranes. Analysis were performed with 64 scans and a 4000-400 cm -1 spectral range (4 cm -1 resolution).

Mechanical properties of the printed rectangular membranes

Young's modulus and tensile properties (strength and strain at break) of the as-printed (APW and APD) and neutralized (NW and ND) rectangular membranes (width = 20 mm, length = 80 mm) prepared using C85 as ink were investigated using an electromechanical machine with a 100N load cell and following the standard of ASTM D3822/D3822M. To achieve a wet state, samples were immersed in DW during 24h before use. The tensile tests were done at least 5 times per each condition.

Results and discussion

Rheological properties

The size of the nozzle and the ink composition are two important parameters that determine the flow rate, described as the quantity of ink filament leaving the nozzle tip per unit time. The effect of different nozzle diameters and pressures on the C65 ink formulation and the effect of the composition of the inks extruded using the same nozzle diameter were examined. Figure 1A shows the volumetric flow rate of C65 ink under different extrusion pressures and three different micronozzles (22 G: 410 µm, 25 G: 250 µm, 27 G: 200 µm). Without any surprise, the flow rate increased when the nozzle diameter increased at a given pressure. Figure 1B shows the evolution of flow rate for all ink formulations, fixing the micronozzle (25 G), under different pressure. All the curves had a general tendency represented by a monotonous linear increasing as applied pressure increase. Using C100 as reference curve, two ink presented curves located below it (C85 and C95) while two inks presented curves located above it (C65 and C75). This phenomenon could be explained by the thixotropic behavior of inks. As mentioned in [START_REF] Rao | Chitosan and guar gum composite films: Preparation, physical, mechanical and antimicrobial properties[END_REF], it is worth noting that, on the one hand, the amino groups of CH are protonated to NH3+ in the acetic acid solution and on the other hand, the ordered structures of GG are destroyed with the solubilization process, resulting in intermolecular hydrogen bonding between NH3+ of the CH backbone and OH -of the GG. From pure CH to 15% of GG content the formation of hydrogen bonding increased the viscosity. When the concentration of GG increase from 15% to 35%, a reduction in viscosities of inks formultations containing GG was observed. Ink viscosity is one of the most important parameters for a successful bioprinting. Inks should preferentially present a shear thinning behavior, in order to allow an easy continued and regular flow through the nozzle when pressure is applied during the extrusion. Figure 2 shows the viscosity of the different ink formulations. All ink formulations exhibited a shear thinning behavior and could be suitable for extrusion-based bioprinting because they decrease their viscosity under increasing pressure. Results show that the inclusion of GG on pure CH solution increase its viscosity, although this increase in not directly dependent on the added quantity. In

B

fact, C75 presented the highest viscosity at share rate of 0.1s -1 , followed by C65, C95, C85 and lastly C100. The higher viscosity of C95 compared to C85 could probably be explained by the fact that the initial increase in GG's concentration resulted in intermolecular hydrogen bonding between NH3+ of the CH backbone and OH -of the GG, but further addition of GG led to decrease the puncture force as mentionned in [START_REF] Rao | Chitosan and guar gum composite films: Preparation, physical, mechanical and antimicrobial properties[END_REF], and could be attributed to the aggregation of the GG double helices. These observations can be linked to the amphiphilic nature of the two polysaccharides used. Both C95 and C85 results show that a reduced shear rate is necessary in order to achieve the loss of viscosity: 0.8 Pa.s is obtained at 63 s -1 for C95 and 125.9 s -1 for C85, against 158 s -1 for C100. This property is interesting because it allows a potential reduction in applied pressure during bio-printing, that could be advantageous if the inclusion of cells during the bioprinting is considered. Furthermore, the use of low pressure and thus the creation of thinner filaments could improve the shape fidelity after printing. For instance, viscosity of C85 and C95 dropped from 100 Pa.s at a steady state of 0.3 Pa.s at a shear rate of 200 s -1 . All this type of ink became unstable above 100 s -1 shear rates. Results concerning C75 and C65 were unstable, probably due to their lack of homogeneity and phase separation of the ink over time.

Solvent evaporation rate

Solvent evaporation rate is an important parameter that can affect the dimensional stability and printability of bioinks [START_REF] Wu | Processing and Properties of Chitosan Inks for 3D Printing of Hydrogel Microstructures[END_REF]. In fact, a faster solvent evaporation rate leads to a faster solidification of the printed structure. Consequently, the possibility to build complexes 3D structures without collapsing phenomena increases. Figure 3 shows the evolution of the solvent content (%) as a function of time for all ink formulations. All samples exhibited a large tendency to stabilize around a solvent content of 25%, proving that GG did not influence drastically the final solvent content. This amount was similar to the results presented by Wu et al. [START_REF] Wu | Processing and Properties of Chitosan Inks for 3D Printing of Hydrogel Microstructures[END_REF] for a 8% CH solution prepared in acetic acid while the same solution prepared in an acidic mixture of 40% AA, 20% Lactic Acid (LA) and 3% CA lead to a final solvent content of 40% due to its lower vapor pressure (0.0813 mmHg at 25°C for LA and 15.7 mmHg for CA). These results showed that acetic acid is more prone to evaporation into air than lactic acid. But the acid mixture used contained non-volatile CA, which results in residual solvent in the filaments and can therefore minimize the drying-induced shrinkage. The evolution of the solvent content of C75, C85 and C95 follow similar trends with a fast initial decrease while C100 and C65 show a more linear dependence. Furthermore, there is no significant difference in the curve of C85 and C75 in comparison to C65. This can be explained by the fact that the presence of GG reduces the oxygen barrier property of the bioprinted structure which led to a better exchange with the atmosphere [START_REF] Rao | Chitosan and guar gum composite films: Preparation, physical, mechanical and antimicrobial properties[END_REF]. Moreover, according to Kester and Fennema [START_REF] Wittaya | Edible films and coatings: Characteristics and properties[END_REF], a higher concentration of GG (as in C65) increases the oxygen permeability, meaning that a part of the evaporated solvent is rapidly replaced by oxygen. The ink evaporation rates, showed in Table 2, were obtained from the linear regression of the solvent content loss curves in its linear part. The evaporation rate was lower for the pure CH ink and for the formulation containing the highest % of GG (C65) at values of 0.0016 s -1 and 0.0130 s -1, , respectively. However, the ink formulations with decreasing content of GG presented an increase of the evaporation rate. to increase solvent retention and to induce lower shrinkage [START_REF] Wu | Processing and Properties of Chitosan Inks for 3D Printing of Hydrogel Microstructures[END_REF]. However, to be used as bio-ink, it would be preferable to have a faster solvent evaporation rate to avoid negative consequences after the incorporation of cells. Because of the faster evaporation rate, the ink formulations containing a percentage of GG comprised between 5 and 25 resulted more appropriate in order to obtain greater shape fidelity after printing.

Influence of speed, pressure, nozzle diameter and ink composition on printability

The study of the printability of filaments is an effective way to define the potential of inks for 3D bioprinting. Despite the absence of a standard definition of the term printability there are some basic requirements for an ink to be considered printable. First of all, the extrudability that represent the capability to achieve extrusion and second of all the shape fidelity of the final construct that can be evaluated on single layers by measuring the size of printed filaments [START_REF] Gillispie | Assessment methodologies for extrusion-based bioink printability[END_REF].

Figure 4A shows the diameter of C65 printed filaments at a robot head speed of 5 mm/s under different pressure using two different gauges (25 and 27 G). Using the 27 G it was possible to print filaments with smaller diameters for any applied pressure, due to the smaller nozzle diameter and higher shear rate in it. The difference in filament diameter was from 2.84 (0.9 bars) to 4.36 times bigger (2 bars) when using the 25 G. This phenomenon can be attributed to a high pressure in the micro-nozzle, inducing an increase in the shear rate and, a fortiori, an alignment of the ink macromolecules. Then, after extrusion, the macromolecules tend to reorganize and take on a less dense configuration. Moreover, while the pressures rise, the amount of ink that flows out is much higher as well. Therefore, 27 G allows a higher precision during printing. The influence of different robot head speed (15, 20, 25 and 30 mm/s) and extrusion pressure (from 1.5 to 4 bar) was examined by printing C85 ink with a 27 G micronozzle (see Figure 4B).

Results show that when a 200 µm nozzle (27 G) was used and the pressure was higher than 2.1 bars, the diameter of the filaments was higher than 300 μm. In addition, the high-pressure relaxation effect indicated that it would be more interesting to work with pressures below 2. formulations were extruded under different pressures (from 0.9 to 2.5 bar). Figure 4C shows a representative 3D map of the diameter of printed filaments. As expected, the diameter of the filaments was smaller for lower pressures, while, when the pressure increases, the ink flow was higher and consequently, the diameter. For a given pressure, we obtained better results for the inks referenced C75, C85 and C95. Indeed, an almost symmetric tendency around C85 was observed, which means that C85 was the best candidate. The lowest possible diameter for C75 and C95 using a pressure of 0.9 bar was between 1 and 1.5 mm, whereas using the same pressure but C85 as ink, the diameter decreased at values comprised between 0.5 and 1 mm. As a consequence, keeping in mind the wish to have filaments diameters the closest possible to the diameter of the nozzle used (D ≅ DG), the optimal parameters for 3D bio-printing (speed 25 mm/s and pressure below 2 bars) were given by the green region of the 3D map in Figure 4C. Instead, the filament printed using C85 as ink had straight and smooth edges. The filaments printed with C85 ink had had the best stability right after printing. An alternative method for the study of the sprawling effect is presented in Figure 6. This method is based on the measure of the difference between the apparent and the theoretical surface, calculated by multiplying the average diameter of the filament by the length of the filament (hypothesis of the zero-default ink). The apparent surface was measured using Panasis software.

When this value is close to 0% it means that the apparent surface of printed filament fit the theoretical surface it should have. C65 and C100 had values between 5% and 10% of difference between the apparent and theoretical surface, confirming the sprawling effect. C75 and C95

showed also a difference between 5 and 10% for low pressure and high pressure respectively.

Instead, C85 had values between 0 and 5% for any applied pressure, representing the best ink formulation in terms of sprawling effect. These results suggested also that the inclusion of GG between 5% and 25%, allowed a good printability with reference to the diameter of the filaments and the sprawling effect (Figure 4C, 5 and 6).

Dimensional stability of as-printed and post-neutralized membranes

Membranes printed with C85 ink formulation were used to study the dimensional stability. Right after printing, membranes were stored at 25°C to allow solvent evaporation. The shrinkage of the printed membranes was evaluated by measuring their length (L) and width (W) after 0 (asprinted), 2, 18 and 24h. The same data were collected on membranes after neutralization with 1 M NaOH. Experiments were done in triplicate. Because of there were no variations in the width, only the length was measures. Figure 7 shows the effect of this shrinkage over time on filaments as printed and after neutralization. corresponds to as-printed or directly post-printing membrane (initial state). The printed membranes directly after printing were completely smooth. After 24h at 25°C, the dried membranes (Figure 8B) presented a wrinkled surface and air bubbles that got stuck. After 24 hours of neutralization with 1M NaOH, the samples (Figure 8C) had a visible size reduction and started to shrink (2.5 to 10% reduction) due to the evaporation of water. During the neutralization, NH3 + groups are deprotonated, the ionic repulsion between the chains of the two polysaccharides decrease and the release of water increase thus the packing of the macromolecular chains and gel shrinkage were promoted [START_REF] Ng | Development of Polyelectrolyte Chitosan-gelatin Hydrogels for Skin Bioprinting[END_REF]. A shrinkage of 55-62% of the initial length of neutralized membranes was observed after 24 h drying (Figure 8D). Membranes were completely deformed after drying 24h in air. In order to study the shrinkage post process, comparison between as printed, after neutralization and after the washing step, were proceeded. Membranes (length=79 mm, width=39.5 mm) were prepared using C85 ink. The shrinkage was quantified measuring the relative size reduction over the process. Table 3 shows the dimensional variation of membranes after neutralization and after washing with DW. Washing step allow to remove excess of NaOH solution and to obtain membranes at neutral pH. This step could be interesting if the introduction of cells is wished after printing.

A B C D

The length and width of the samples were reduced from 5% and 3% to 10% respectively. This may be explained by the fact that the printed sample absorbed a great amount of water during neutralization and washing steps. During these steps, all samples maintained their shapes.

XRD characterization

XRD patterns of as-printed in a dried state (APD) and neutralized in a dried state (ND) C85based membranes are presented in Figure 9. ADP membranes presented a sharp peak at 2θ = 12°

(from 10° to 14°) and a broad peak from 16° to 26° with a maximum at 20°. Wu et al. [START_REF] Wu | Processing and Properties of Chitosan Inks for 3D Printing of Hydrogel Microstructures[END_REF] showed that pure CH printed filaments did not present the peak at 12° while it appears after neutralization with NaOH. The presence of this peak in CH/GG membranes could mean that the introduction of GG allows a higher crystallinity leading to greater mechanical properties [START_REF] Kong | Antimicrobial properties of chitosan and mode of action: A state of the art review[END_REF].

However, the large peak around 20°, associated with amorphous phase of CH [START_REF] Lee | Chitosan-catechol: a writable bioink under serum culture media[END_REF], implies that some solvent remained after drying. GG macromolecules can expand the spacing between chitosan chains, interaction between the amino groups of chitosan and the carboxyl group in citric acid. Same observations have been done when using lactic acid [START_REF] Srivastava | Seed Galactomannans: An Overview[END_REF]. After neutralizing, the same peak is seen at 2θ = 12° but the peak at 20° became much thinner and lost its intensity. This could be due to the loss in acidity and to an increase of crystalline degree. In general, both states showed the same spectrum with no significant difference in peaks position. In both states, a broad peak is observed between 3100-3600 cm -1 with a maximum at 3550 cm -1 , linked to stretching vibration of -OH groups [START_REF] Tang | Preparation and properties of chitosan/guar gum/nanocrystalline cellulose nanocomposite films[END_REF][START_REF] Iqbal | Synthesis and characterization of chitosan and guar gum based ternary blends with polyvinyl alcohol[END_REF]. In addition, the peak at 2890 cm -1 is characteristic of C-H vibrations from CH2 groups. A tiny residual peak due to ring stretching of GG at 1700 cm -1 has been also determined. Amide I groups represented at 1530 cm -1 from CH, and at 1350 cm -1 one sharp peak representing C-H bending. Then an intense peak of all C-C groups at 1200 cm -1 . Finally, at 1070 cm -1 and 980 cm -1 there were ether C-O stretching vibration and C-OH stretching [START_REF] Sami | Formulation of novel chitosan guargum based hydrogels for sustained drug release of paracetamol[END_REF][START_REF] Tang | Preparation and properties of chitosan/guar gum/nanocrystalline cellulose nanocomposite films[END_REF][START_REF] Iqbal | Synthesis and characterization of chitosan and guar gum based ternary blends with polyvinyl alcohol[END_REF]. There was no evidence of linkage between GG and CH that would form a durable polymer. Adding a cross-linker would solve this problem as mentioned before [START_REF] Sami | Formulation of novel chitosan guargum based hydrogels for sustained drug release of paracetamol[END_REF]. Indeed, chitosan forms NH3 + when exposed to acidic environment, offering opportunities for anionic groups to establish electrostatic interactions. . FTIR spectra of as-printed in a dried state (APD, black) and neutralized in a dried state (ND, red) C85-based membranes-ADP was dried in an oven after printing, ND was neutralized after printing before being dried in an oven.

Mechanical characterization

Young's modulus and tensile properties of as-printed membranes at a wet (APW) and dry state and neutralized membranes at a dry (ND) state were investigated. Due to their fragility, it was not possible to perform the mechanical tests on the neutralized membranes in a wet (NW) state. All membranes were printed using C85 as ink and had a rectangular shape (length = 80 mm, width= 20 mm). Young modulus values of the mentioned membranes were 1MPa, 280 MPa and 1GPa for APW, APD and ND membranes, respectively. The values are determined at around 20%. As expected, ADP membranes stiffness was higher than APW. Drying increases the rigidity of the membrane and the neutralization step makes the membranes more flexible. This is consistent with regard to the different protocols used for these steps. APD membranes presented a stress at break of 35 MPa whereas the stress at break for APW and ND was only 0.3 MPa and 7

MPa respectively (Figure 11). This last value was similar to the neutralized wet membranes made of pure CH (6 MPa) presented by Wu et al. [START_REF] Wu | Processing and Properties of Chitosan Inks for 3D Printing of Hydrogel Microstructures[END_REF]. Figure 11 also shows the strain at break. Dry printed samples showed a low strain, which is close to the pure CH. Chains flexibility was not influenced by the presence of GG. The wet bioprinted membranes deformed to 31% of its initial length. Finally, GG mainly increase the limit strength break by increasing the interaction between CC and GG. Nonetheless crosslinking will be still necessary in order to maintain a better stability at the wet state.

Conclusions

We proposed for the first time the combination of chitosan (CH) and guar gum (GG) to develop new inks for extrusion-based 3D bioprinting. The influence of the ink composition, robot head speed, pressure and micronozzle size on the printability of 5 different ink formulations has been studied. Moreover, the inks characteristics such as rheological properties, solvent evaporation at different proportion of CH/GG gave important information on its printability and stability. 1D and 2D structures were successfully printed and analyzed. Improved mechanical properties and better dimensional stability were obtained with a 85% CH -15% (w/v) GG ink. Further investigation must be made to examine the feasibility in the creation of 3D structures and to examine the biological properties such as biocompatibility and biodegradability. The ink presented in the study showed a good printability and the potential to be used in the field of the health care applications.

Figure 1 :

 1 Figure 1: Volumetric flow rate as a function of applied pressure. (A) C65 ink deposited using three micronozzles (22, 25, 27 G) and (B) five different inks (C65 to C100) extruded with a 25 G micronozzle.

Figure 2 .

 2 Figure 2. Viscosity (Pa.s) as a function of shear rate (s -1 ) for C65, C75, C85, C95 and C100 ink formulations.

Figure 3 .

 3 Figure 3. Time dependency of the solvent content inside the filaments printed with all inks, obtained through weight measurements at 25°C.

1 bars.

 1 Under this pressure, the values of the printed diameters split in two distinct regions: the first one for speeds of 15 and 20 mm/s with filament ranging [300 µm -400 µm], and the second one for speeds 25 and 30 mm/s with D ranging [200 µm-300 µm]. To study the influence of the ink composition, the nozzle (25 G) and the speed (25 mm/s) were fixed and the different ink

Figures 4 .

 4 Figures 4. (A) Process map illustrating the diameter of C65 ink obtained filaments (colors) with respect to the applied pressure during extrusion and micronozzle used. (B) Process map illustrating the diameter of C85 obtained filaments (colors) with respect to the applied pressure and robot head speed used. (C) 3D map of the of all the ink formulation diameter obtained with respect to applied pressure and ink used. Zones where the diameter is the lowest are privileged.

Figures 5 .

 5 Figures 5. Optical images taken by a binocular showing the visual sprawling of all inks at 2 bars, 5 mm/s and 25 G micronozzle.

Figure 6 .

 6 Figure 6. Mapping of the difference in percent between theorical printed surface and real surface in function of pressure and ink used. Value equal to 0 means that there are absolute no sprawl.

Figure 7 .

 7 Figure 7. Length variation over time of C85 printed membranes post-printing and postneutralization.

Figures 8 .

 8 Figures 8. Morphological variation of rectangular membranes (20 mm x 80 mm) using C85 ink formulation during different steps: (A) as-printed or directly post-printing (initial state), (B) after 24h drying at 25°C, (C) post-neutralization and (D) after 24h drying after neutralization.

Figure 9 :

 9 Figure 9: XRD spectrum of as-printed in a dried state (APD) and neutralized in a dried state (ND) C85-based membranes

Figure 10

 10 Figure10. FTIR spectra of as-printed in a dried state (APD, black) and neutralized in a dried state (ND, red) C85-based membranes-ADP was dried in an oven after printing, ND was neutralized after printing before being dried in an oven.

Figure 11 .

 11 Figure 11. Tensile stress at break (red) and strain at break (blue) of as-printed membranes at a wet (APW) and dry (APD) state and neutralized membranes at a dry state (ND) C85-based.

. Table 1. Composition of the different ink formulations with respective densities CH

						:
	chitosan, GG: guar gum
		C100	C95	C85	C75	C65
	CH (%)	100	95	85	75	65
	GG (%)	0	5	15	25	35
	Density (g.cm -1 )	1.186	1.183	1.182	1.175	1.158

Table 2 .

 2 Evaporation rate of the ink formulations obtained from the linear regression of the solvent content loss curves during the first 50 mins for C95, C85 and C75 inks and the first 90 mins for C100 and C65 inks in the initial stage.

	Ink	C100	C95	C85	C75	C65
	Evaporation rate (%.s -1 )	0.0116	0.0333	0.0254	0.0273	0.0130

In comparison with

Wu et al. results

, an acidic mixture of AA and LA could be more appropriate

Table 3 :

 3 Dimensional variation over the process of C85 printed sample with 79 mm length and a square section of 39.5 mm width. Measures were taken right after the steps without any modification.

		As-Printed	After neutralization	After Washing
	Length (mm)	1	0.96	0.95
	Width (mm)	1	1.02	0.98
	Width (mm)	1	0.95	0.91

Acknowledgment

This work was supported partly by the French PIA project "Lorraine Université d'Excellence", reference ANR-15-IDEX-04-LUE, FC and JFM would also thanks "le cercle Gutenberg" and canceropôle Grand-Est for their financial support.

Author contributions

The manuscript was written through the contributions of all authors. All authors have given approval to the final version of the manuscript.

Conflict of interest

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