Shriman Narayan Tiwari

Ngoc Q K Duong

Frédéric Lefebvre

Claire-Hélène Demarty

Benoit Huet

Louis Chevallier

DEEP FEATURES FOR MULTIMODAL EMOTION CLASSIFICATION

Keywords: Multimodal-based emotion classification, affective computing, deep learning, video segmentation, feature aggregation

Understanding human emotion when perceiving audio-visual content is an exciting and important research avenue. Thus, there have been emerging attempts to predict the emotion elicited by video clips or movies recently. While most existing approaches focus either on single modality, i.e., only audio or visual data is exploited, or build on a multimodal scheme with late fusion, we propose a multimodal framework with early fusion scheme and target an emotion classification task. Our proposed mechanism presents the advantages of handling (1) the variation in video length, (2) the imbalance of audio and visual feature sizes, and (3) the middle-level fusion of audio and visual information such that a higher level feature representation can be learned jointly from the two modalities for classification. We evaluate the performance of the proposed approach on the international benchmark, i.e., the MediaEval 2015 Affective Impact of Movies 1 task, and show that it outperforms most state-of-the-art systems on arousal accuracy while using a much smaller feature size.

Introduction

Endowing computers with human like emotion perception techniques would be a technological leap towards creating a real context aware machine. It offers a wide range of applications e.g., in health care system to make visually impaired people more autonomous, in parental control systems to detect violent scenes, or in content recommendation. Thus, understanding emotion elicited by the scene is a promising research avenue and within the past decade there has been an active ongoing research towards this trend [START_REF] Hanjalic | Affective video content representation and modeling[END_REF][START_REF] Soleymani | A bayesian framework for video affective representation[END_REF][START_REF] Kahou | Combining modality specific deep neural networks for emotion recognition in video[END_REF][START_REF] Paleari | Features for multimodal emotion recognition: An extensive study[END_REF].

Earlier approaches exploited Hidden Markov Models (HMM) for affective content analysis [START_REF] Kang | Affective content detection using HMMs[END_REF]. But such models allow transition only between the neutral state and other affective states, which made it restrictive. Other probabilistic This work was performed while the first author was doing an internship at Technicolor Research 1 http://www.multimediaeval.org/mediaeval2015/affectiveimpact2015/ models [START_REF] Hanjalic | Affective video content representation and modeling[END_REF][START_REF] Soleymani | A bayesian framework for video affective representation[END_REF] were also investigated for affective video content representation. Zhang et al. proposed a clustering based approach with arousal and valence features in [START_REF] Zhang | Affective mtv analysis based on arousal and valence features[END_REF], and then a support vector regression based model using user profile and multimedia features for analyzing music video in [START_REF] Zhang | Affective visualization and retrieval for music video[END_REF]. At the same time, a latent Dirichlet allocation model using kmeans clustering was also presented in [START_REF] Irie | Affective audio-visual words and latent topic driving model for realizing movie affective scene classification[END_REF]. Recently with the success of deep learning in various applications, it has also been investigated for the considered task [START_REF] Kahou | Combining modality specific deep neural networks for emotion recognition in video[END_REF][START_REF] Acar | Understanding affective content of music videos through learned representations[END_REF][START_REF] Acar | Fusion of learned multi-modal representations and dense trajectories for emotional analysis in videos[END_REF]. Some state-of-the-art approaches leverage classic image and audio features such as histogram of gradient (HOG), color intensity, scale-invariant feature transform (SIFT), dense trajectories, mel-frequency cepstral coefficients (MFCC) [START_REF] Acar | Fusion of learned multi-modal representations and dense trajectories for emotional analysis in videos[END_REF][START_REF] Yi | MIC-TJU in MediaEval 2015 affective impact of movies task[END_REF], etc., together with features extracted from convolutional neural networks (CNN) pre-trained on public datasets such as Ima-geNet [START_REF] Yi | MIC-TJU in MediaEval 2015 affective impact of movies task[END_REF][START_REF] Sermanet | Overfeat: Integrated recognition, localization and detection using convolutional networks[END_REF][START_REF] Dai | Fudan-huawei at MediaEval 2015: Detecting Violent Scenes and Affective Impact in Movies with deep learning[END_REF][START_REF] Lam | NII-UIT at MediaEval 2015 Affective Impact of Movies Task[END_REF] for the final emotion classification. In [START_REF] Dai | Fudan-huawei at MediaEval 2015: Detecting Violent Scenes and Affective Impact in Movies with deep learning[END_REF] features extracted from more dedicated CNN were also used, such as stacked optical flows trained on the UCF-101 dataset 2 for emotion classification in movies. All these works either use late fusion of classifiers [START_REF] Yi | MIC-TJU in MediaEval 2015 affective impact of movies task[END_REF][START_REF] Lam | NII-UIT at MediaEval 2015 Affective Impact of Movies Task[END_REF] or some mid-level fusion scheme, in which aggregated features are used as input to a final classification step [START_REF] Dai | Fudan-huawei at MediaEval 2015: Detecting Violent Scenes and Affective Impact in Movies with deep learning[END_REF][START_REF] Marin Vlastelica | KIT at MediaEval 2015-Evaluating visual cues for affective impact of movies task[END_REF]. For the later, SVM is the prefered classifier, only [START_REF] Seddati | UMons at MediaEval 2015 Affective Impact of Movies Task including violent scenes detection[END_REF] attempted to use neural networks as the final classifier, but reported low performance.

Compared to what can be found in the literature, we propose here to combine the extracted audio and visual features at a middle stage so that a higher level feature representation can be learned jointly from the two modalities by a multilayer perception (MLP) for classification. While classic audio features are used, visual features solely consist of features from pre-trained CNN. However since the extracted low-level audio and visual features usually have different size, our further contribution is to provide a mechanism for balancing their contribution in an aggregated feature. We will also compare our results with a baseline SVM-based classifier.

With a large interest of the research community, the Affective Impact of Movies task3 in the MediaEval challenge 2015 [START_REF] Sjöberg | The MediaEval 2015 Affective Impact of Movies Task[END_REF] has become a state-of-the-art benchmark which attracted more than ten research groups to evaluate their systems on the same dataset. The underlying emotion conveyed in short videos of the dataset is characterized by two wellknown metrics valence and arousal [START_REF] Greenwald | Affective judgment and psychophysiological response: Dimensional covariation in the evaluation of pictorial stimuli[END_REF][START_REF] Lang | International affective picture system (iaps): Technical manual and affective ratings[END_REF], where valence indicates the emotional value associated with a stimulus and arousal is a state of heightened activity in both our mind and body that makes us more alert. The Affective Impact of Movies task and its 2015 results will serve as a baseline to compare the performance of our system.

The rest of the paper is organized as follows. Section 2 presents the general workflow of the proposed approach with detailed description for each step. We conduct experiment to compare the performance of the proposed method with stateof-the-art systems using the MediaEval benchmark in Section 3. Finally, we conclude in Section 4 with remarks about some future perspectives.

Proposed approach

The general workflow of the proposed approach is presented in Figure 1. This framework contains three major steps as: 1) video segmentation and low-level modal-specific feature extraction, 2) feature dimensionality control and aggregation, and 3) classification and voting. Each of these steps will be described in detail in following subsections.

Video segmentation and low-level feature extraction

Given an input video, we propose first to segment it into short segments of length t seconds, typically t = 2, each and process each segment independently. Thus, with an input video of length T seconds, the number of segments N is given by:

N = T t (1)
where, is the greatest integer function. This simple segmentation scheme brings three benefits in our framework as follow. First, it allows us to capture local information presented in certain parts of the video sequence. Second, it allows to handle input videos with different lengths, which is a typical issue in most existing databases, since the extracted audio-visual feature also depends on the signal length. Third, when assigning label of the input video in training data set for all corresponding segmented short segments, it actually increases the number of examples, i.e., by N times, so that we can expect to learn a better classifier.

For each t-second length video segment, low-level features are extracted for the audio and visual modalities as follow. Concerning the visual modality, we select several key frames per segment, ranging from one to ten in our experiments, and compute a visual feature vector for each key frame. For this purpose, we extract the intermediate layers of the CNNs pre-trained by the ImageNet dataset given a key frame as an input image. As these CNNs are trained to Fig. 1. General work flow of the proposed approach classify objects on huge dataset, we can hope that the extracted features from some intermediate layers also provide discriminative visual characteristics that will be relevant for our task. Two well-known CNN networks,i.e., Overfeat proposed in [START_REF] Sermanet | Overfeat: Integrated recognition, localization and detection using convolutional networks[END_REF] and Visual Geometry Group (VGG)'s network described in [START_REF] Simonyan | Very deep convolutional networks for large-scale image recognition[END_REF], are considered in our experiments. From Overfeat [START_REF] Sermanet | Overfeat: Integrated recognition, localization and detection using convolutional networks[END_REF], features are extracted from layers 18 and 21 of the small network, and layers 21 and 24 from the large network. Similarly with VGG network, different features are extracted for small and large architecture. Overall, visual feature vectors of different length ranging from 128 to 4096 are computed for testing our framework with different feature sizes. Concerning audio feature, audio track corresponding to the video segment is devided into 40 ms frames with 50 % overlap. Then the standard Mel-frequency cepstral coefficients (MFCC) is computed for each frame. In our experiment, we compute 20 MFCCs, their first derivatives ∆, and second derivatives ∆∆, and concatenate them together. This results in a 57 length vector as audio feature for each 40 ms frames.

Feature dimensionality control and aggregation

As presented in Section 2.1, the final audio and visual features extracted per video segment have very different sizes. Indeed, we have an audio feature vector of size 57 for each 40 ms audio frame (thus a feature size of 5700 for 2s of video) and several hundreds or thousands as visual feature vector for each key frame image. Thus when aggregating the two modalities, the classifier may learn mostly from one of them, as their contribution is very imbalanced.

To overcome this issue, we propose to perform an additional step of feature dimensionality control before aggregating the two modalities. This is done by performing principle component analysis (PCA) on the input audio/visual feature matrices where the feature output, as the number of principle components, can be well-controlled. As a result, PCA will help setting the audio and visual features to equivalent sizes, so that the two modalities contribute equally to the task. Note that, in addition to controlling the respective modality dimensions as a main target, PCA also helps in eliminating redundancy in the extracted features. As a practical example, key frames extracted from some static video segments are very similar. Thus using visual feature vectors computed from all key frames may not be necessary.

Once the low-level audio and visual features are balanced, they are concatenated to create a single joint feature vector. In the next step, this balanced and aggregated feature will serve as input to our joint-modality classifier.

Classification and voting strategy

In order to learn a deeper feature for classification, the concatenated low-level audio-visual feature vectors are served as inputs to a fully connected neural network, known as multi-layer perceptron (MLP). We expect that MLP will learn a higher level feature representation jointly from the two modalities to improve the classification performance. In the implementation, an exhaustive grid-search is launched to find the optimal parameters of the MLP. More about the MLP will be discussed in Section 3.

The output layer of the MLP, which serves as a classifier for each video segment, offers probability values from the softmax function for each of the label classes. In order to obtain a final emotion label for the whole input video, which contains N segments, we then perform majority voting from the results obtained by classifying each individual segments as

l = arg max lm N k=1 S(l m |k), (2)
where, l is the emotion label given to the video and S(l m |k) is the score obtained from the classifier when observing label l m given the k th segment of the video. For the SVM classifier the score is the log-probability values associated with each possible outcome class for a video segment, whereas for MLP, the score is the class probability for each output class. This voting strategy can be understood as choosing the label which maximizes the total score over all classes for all video segments as the final label for the input video.

Experiment

After describing the dataset and the evaluation metric in Section 3.1, we present in Section 3.2 the result obtained by the proposed approach. This result is compared with an SVMbased baseline framework as well as with state-of-the-art systems which participated in the MediaEval 2015 campaign.

Dataset and evaluation metric

For easier and fair comparison with existing methods, we evaluated the performance of the proposed approach on the benchmarked dataset provided in the MediaEval 2015 Affective Impact of Movies task, which were designed for detecting the emotional impact of movies [START_REF] Sjöberg | The MediaEval 2015 Affective Impact of Movies Task[END_REF]. This dataset, is an extension of the LIRIS-ACCEDE dataset [START_REF] Baveye | Lirisaccede: A video database for affective content analysis[END_REF], consisting of 10,900 short video clips extracted from 199 Creative Commons-licensed movies of various genres. It is independently split into development set containing 6144 video shots from 100 movies, and test set containing 4756 video shots from remaining 99 movies. Each of these shots are between 8 and 12 seconds long and start and end with either a cut or a smooth transition. The dataset has discrete labels for both valence and arousal. As such valence is labeled as negative, neutral and positive, while for arousal the classes are passive, neutral and active.

The induced affect detection is officially evaluated using the global accuracy metric [START_REF] Sjöberg | The MediaEval 2015 Affective Impact of Movies Task[END_REF], calculated separately for valence and arousal dimensions, according to:

A i = C i N i (3)
where, i ∈ {Valence, Arousal}, C i , is the total number of correct classifications in all three classes and N i , is the total number of test examples.

Algorithm setup and results

We tested the approach with different audio-visual feature sizes obtained using dimensionality reduction, ranging from 128 to several thousands. It was found that, the classification performance was not sensitive to the feature size ranging form 250 to 1000. Therefore we decided to use smaller feature sizes of the order of 500 as input for both the baseline SVM and the MLP classifiers to jointly learn the feature representation. Note that, with a view of adding temporal information, experiments were also performed with several key frames ranging from one to ten per video segment but, it turns out that, generally due to high correlation among the frames, equal performance was achieved by using only a few number of key frames (2 or 3 frames per segment).

In the MLP architecture, rectified linear units [START_REF] Zeiler | On rectified linear units for speech processing[END_REF] were used as nonlinear functions and stochastic gradient descent with minibatches was used for parameter updates. The best The hidden layers had a dropout factor of 0.5, which helps in avoiding the over-fitting the model. The number of hidden layers, the number of neurons per layer and the learning rate were varied and optimized by the development data. Finally, with the considered dataset, we obtain the following parameters of the MLP: 2 hidden layers with 100 hidden units in hidden layer one and 10 units in hidden layer two.

We compare the proposed approach, i.e., where MLP is used for jointly learning a higher level feature representation from the balanced visual and audio modality, with the baseline setup, i.e., where the balanced audio-visual feature is used directly as input to the SVM classifier (named "Proposed approach with SVM baseline" in Table 1). We also compare our results with state-of-the-art systems in the Me-diaEval 2015 challenge. The results are shown in Table 1, where the feature type column summarizes the features exploited by each approach. The classifier column shows the final classifier used by each approach and feature length column presents the final multimodal feature size required by each algorithm. As example, in [START_REF] Yi | MIC-TJU in MediaEval 2015 affective impact of movies task[END_REF] CNN visual features are used along with dense SIFT features, MFCCs, hue-saturation histogram (HSH) and improved dense trajectory (IDT), where as [START_REF] Mironica | Rfa at MediaEval 2015 Affective Impact of Movies Task: A multimodal approach[END_REF] uses Fisher kernels with CNN visual features. Also, some submissions used external datasets to train the models like HMDB-51 is used in [START_REF] Seddati | UMons at MediaEval 2015 Affective Impact of Movies Task including violent scenes detection[END_REF] and UCF-101 is used in [START_REF] Dai | Fudan-huawei at MediaEval 2015: Detecting Violent Scenes and Affective Impact in Movies with deep learning[END_REF]. Note that we propose here solely the results obtained for arousal classification. For valence, there was no significant improvement compared to the proposed methods in MediaEval 2015. Nevertheless none of these systems did improve much above the random baseline (+7% for the best system), compared to what was achieved for Arousal (+22%). Thus, it was a relevant choice to exhibit the results for arousal classi-fication only.

As can be seen, the proposed approach with MLP offers higher accuracy than the baseline workflow with SVM classifier. This supports our strong expectation that a higher level feature representation learned jointly from two modalities would improve the performance. It can also be inferred that multimodal feature fusion at middle stage, in which the contribution of each modality is well-balanced, could be a good strategy in a multimodal framework. As compared to the state of the art, the proposed approach outperforms all the proposed approaches but two. Note that the systems [START_REF] Yi | MIC-TJU in MediaEval 2015 affective impact of movies task[END_REF][START_REF] Lam | NII-UIT at MediaEval 2015 Affective Impact of Movies Task[END_REF] with slightly better performance (0.1%) employed much larger feature vectors than ours.

Conclusion

In this paper we have presented a multimodal, deep learningbased, framework for emotion classification on the arousal scale. This framework has the advantage of handling the variation in the video length and the imbalance of feature sizes from the different modalities through a mid-level feature aggregation scheme. The proposed approach was compared with those submitted by the participants of the MediaEval 2015 Affective Impact of Movies task. We have demonstrated that even with a small feature size jointly learned from only two features types, CNN-based visual feature and MFCCs, the proposed system outperforms all state-of-the-art systems but two in terms of arousal accuracy. The proposed multimodal framework could be further improved by adding external dataset for training. Also, taking into account the temporal aspect of videos, either by adding temporal features (e.g., motion-based) or by using recurrent neural networks, is worth being tested.

Table 1 .

 1 Comparison with state-of-the-art approaches participated in the MediaEval 2015: Affective Impact of Movies Task results were obtained using categorical cross-entropy loss function.

	Approach		Feature type	Classifier	Feature Length Arousal Accuracy (%)
	Yi et al. [11]		IDT + SIFT + MFCC + HSH +	Linear SVM	> 4,000	55.93
			CNN
	Lam et al. [14]	HOG + MBH +SIFT + MFCC +	SVM	∼ 40960	55.90
			VDFULL + CNN
	Trigeorgis et al. [22]	Low Level Descriptors + CNN	AdaBoost	∼ 1000	55.72
	Seddati et al. [16]	OFM + CNN trained on HMDB-	Two layer fully connected	∼ 20,000	52.44
			51[23]	neural network
	Marin et al. [15]	GIST + IDT + CNN	linear SVM	> 100,000	51.90
	Chakraborty et al. [24]	Low Level Descriptors	Artificial Neural Network	∼ 1000	48.95
	Dai et al. [13]	LSTM trained on UCF-101	SVM with linear and χ 2	> 10,000	48.7
				kernels
	Mironicȃ at al. [25]	Fisher kernel + CNN	non-linear SVM	∼10,000	44.36
	Proposed	approach	Intermediate CNN features +	SVM with RBF kernel	∼ 500	55.14
	with SVM baseline	MFCC
	Proposed approach	Intermediate CNN features +	MLP	∼ 500	55.85
			MFCC

http://crcv.ucf.edu/data/UCF101.php

The large variance of audio-visual content and different shooting styles makes the affective content analysis a very challenging task.


domain: sde sdv

text:
Amelia Carolina Sparavigna
email: amelia.sparavigna@polito.it

Patterned Vegetation Created by Red Harvester Ants and Evidenced in Satellite Images

Keywords: Pattern Formation, Patterned Vegetation, Ecosystems, Satellite Images. Subject Areas: Environmental Sciences, Ecosystem Science, Ecology

Large-scale areas of patterned vegetation, with distinctive and repetitive patterns, can arise because of natural phenomena that differentially influence plant growth or mortality and by biotic interactions of animals and plants. Such patterns can be easily observed in satellite images, if they have a high enough resolution for the specific observation. Here we will show that a large area of a patterned vegetation with a "polka-dot" arrangement can be observed in the Toroweap Valley of Arizona, near the Grand Canyon. In this case, the vegetation has been patterned by the presence of the nests of red harvester ants. The observation is remarkable for the large surface occupied by such colonies.

Introduction

Characteristic arrangements of forms and structures are often found in the natural world. Sometimes these arrangements are producing regular patterns that are recurring in different contexts, as shown by pattern formations observed in Biology, Physics, Astrophysics and Chemistry, and those that can be studies and simulated by Mathematics and Computer graphics [START_REF] Carbone | Pattern Formation in Biology, Vision and Dynamics[END_REF][START_REF] Bolton | Patterns in physics[END_REF][START_REF] Turing | The Chemical Basis of Morphogenesis[END_REF][START_REF] Regev | Chaos and Complexity in Astrophysics[END_REF][START_REF] Ball | Nature's Patterns: A Tapestry in Three Parts. 1: Shapes[END_REF]. Therefore, the origin of pattern formations is the subject of several scientific researches, having an interdisciplinary nature, which aim giving the theoretical models of the self-organization of natural phenomena, observed from microscopic to macroscopic scales. In the last case, it means that we are studying the pattern formation in a natural environment, which is visible on a so large scale that the best device to investigate it is the satellite imagery. Among these phenomena, we find those of the patterned vegetation. Large-scale areas of patterned vegetation, with distinctive and repetitive patterns, can arise because of natural phenomena that differentially influence plant growth or mortality and by the biotic interaction of animals and plants. Vegetation patterns are for instance the "tiger bush", an example of which is given in the Figure 1A, and the "fir waves" [START_REF] Tongway | Banded Vegetation Patterning in Arid and Semiarid Environments[END_REF][START_REF] D'avanzo | Fir Waves: Regeneration in New England Conifer Forests[END_REF]. Tiger bush consists of stripes of bushes which appears on the slopes of arid lands, where the vegetation growth is limited by rainfall; each stripe of vegetation absorbs water from the bare zone immediately above it [START_REF] Tongway | Banded Vegetation Patterning in Arid and Semiarid Environments[END_REF][START_REF] Vv | Pattern Formation, Wikipedia[END_REF]. Fir waves occur in forests on mountain slopes after wind disturbance, during regeneration processes [START_REF] Vv | Pattern Formation, Wikipedia[END_REF]. Besides the patterns of vegetation produced by environmental condition, such as the tiger bush, we have that large-scale regular spatial patterns can arise within homogeneous landscapes from local biotic interactions [START_REF] Pringle | Spatial Pattern Enhances Ecosystem Functioning in an African Savanna[END_REF][START_REF] Hassell | Spatial Structure and Chaos in Insect Population Dynamics[END_REF][START_REF] Gueron | Self-Organization of Front Patterns in Large Wildebeest Herds[END_REF][START_REF] Comins | The Spatial Dynamics of Host Parasitoid Systems[END_REF], where the organization of living beings naturally scales up to produce quasi-regular patterns across large landscapes. Due to the recent availability of satellite imagery of high resolution, such patterns have been increasingly reported in the ecological literature [START_REF] Pringle | Spatial Pattern Enhances Ecosystem Functioning in an African Savanna[END_REF]. For instance, it is possible to observe, in semi-arid East Africa, the mounds built by Odontotermes termites frequently occurring in uniform "polka-dot" arrangements [START_REF] Pringle | Spatial Pattern Enhances Ecosystem Functioning in an African Savanna[END_REF]. Another case investigated by means of the satellite images of Google Earth is that of the "fairy circles" of Namibia (see Figure 2B) [13][START_REF] Tschinkel | The Life Cycle and Life Span of Namibian Fairy Circles[END_REF][START_REF] Juergens | The Biological Underpinnings of Namib Desert Fairy Circles[END_REF]. According to Ref.15, the sand termite Psammotermes Allocerus generates local ecosystems which create rings of perennial vegetation that facilitate termite survival. Recently it had been reported that the "fairy circles pattern" exists in the remote outback of Australia [START_REF] Getzin | Discovery of fairy circles in Australia supports self-organization theory[END_REF] (see Figure 2). Using fieldwork, remote sensing, and spatial pattern and mathematical modeling, the researchers proposed a pattern-formation theory for the Australian environment. Here, we will consider the case of another biotic interaction of animals and plants, the patterned vegetation created by ants. Such patterns can be easily observed in satellite images, if they have a high enough resolution for the specific observation. Here we show that some large areas of patterned vegetation with a "polka-dot" arrangement can be observed in the Toroweap Valley of Arizona, in the National Park of Grand Canyon [17]. In this case, the vegetation is patterned by the presence of the nests of red harvester ants. The patterned vegetation is visible in the Google Earth images of an area 10 km long in the valley of Arizona, near the Vulcan's Throne of Grand Canyon.

Figure 1.

On the left (A), an example of "tiger bush" in Niger (Courtesy Google Earth). It consists of stripes of bushes which appear on the slopes of arid lands, where the vegetation growth is limited by rainfall; each stripe of vegetation absorbs water from the bare zone immediately above it [START_REF] Tongway | Banded Vegetation Patterning in Arid and Semiarid Environments[END_REF][START_REF] Vv | Pattern Formation, Wikipedia[END_REF]. On the right (B), fairy circles in Namibia (Courtesy Google Earth). Sand termites generate local ecosystems through removal of short-lived vegetation that appears after rain, leaving circular barren patches [START_REF] Juergens | The Biological Underpinnings of Namib Desert Fairy Circles[END_REF]. Since water is retained within the circles, rings of perennial vegetation are created that facilitate termite survival. "This termite-generated ecosystem persists through prolonged droughts lasting many decades" [START_REF] Juergens | The Biological Underpinnings of Namib Desert Fairy Circles[END_REF].

Vegetation and Ants

As shown by the example of the "fairy circles", from satellites, it is possible to investigate some aspects of the life of insects, in particular of the colonies that insects can create, such as those of ants and their nests. For in-stance, the red harvester ants create nests that, with their surrounding area can reach a dimension which is possible to be observed in satellite images having a high enough resolution. These nests are not isolated; each one can be surrounded by several other nests, giving rise to a patterned local vegetation, which is visible from above, much better than from the ground. The use of satellites for monitoring ant colonies was proposed in [START_REF] Fletcher | Detecting Red Harvester Ant Mounds with Panchromatic QuickBird Imagery[END_REF], for supporting natural resource managers that were interested in locating red harvester ant mounds. These are ants that can have a negative impact on human activities due to heavy infestations on pastures. However, they are also important for the survival of other animals, such as lizards [START_REF] Fletcher | Detecting Red Harvester Ant Mounds with Panchromatic QuickBird Imagery[END_REF]. In their study on the red harvester ants, the researchers evaluated the QuickBird imagery as a tool for detecting the ant mounds. The research gave the result that natural resource managers can use this imagery to determine the severity of infestations. As previously told, uniform "polka-dot" arrangements of the mounds termites are frequently occurring [START_REF] Vv | Red Harvester Ant[END_REF]. These arrangements can be observed in satellite images also for the nests of red harvester ants. The nests are visible for the following reasons. They are characterized by a lack of plant growth and small pebbles surrounding the entrance of them. Hulls of seeds may be found scattered around the nest. Therefore, in grassland areas, the lack of vegetation makes the red harvester ant colonies very easy to notice. The mound corresponding to the nest are typically flat and broad, 0.3 to 1.2 m in diameter [START_REF] Vv | Red Harvester Ant[END_REF]. Even larger bare areas, on the order of 10 m 2 , are created. Trails typically lead away from the mound, like "arms". These trails are used by ants to collect and bring food back to the mound. In [START_REF] Wild | Ants from a Kilometer Up[END_REF], the nest and the related bare areas are defined as "nest discs". Alexander Wild, in his web article [START_REF] Wild | Ants from a Kilometer Up[END_REF], is showing some examples of how they appear in images. One image from Google Earth is concerning an area South of Tucson, Arizona (31°38.097' N, 111°03.797' W). The area is showing a pattern of evenly-spaced "polka-dots" [START_REF] Wild | Ants from a Kilometer Up[END_REF]: the dots are the "nest discs of one of our most conspicuous insects in the Sonoran desert, the red harvester ant Pogonomyrmex Barbatus". On the ground, it is hard to recognize the even spacing of the nests, but they are plenty obvious. "The ants keep the large area around their nest entrance free of vegetation and other unwanted debris" [START_REF] Wild | Ants from a Kilometer Up[END_REF]. Reference 20 is fundamental to understand the patterned vegetation that we can find in another area of Arizona.

It is a valley, the Toroweap Valley, near the Vulcan's Throne, a cinder cone volcano and a prominent landmark on the North Rim of the Grand Canyon. This area is not reported in [START_REF] Wild | Ants from a Kilometer Up[END_REF], probably because in 2008 the satellite images had a low resolution. Today, using Google Earth for the areas marked in the map shown in the Figure 3, we can see a large landscape covered by discs, that look like those shown in Ref. [START_REF] Wild | Ants from a Kilometer Up[END_REF]. For this reason, in a previous discussion proposed in [START_REF] Sparavigna | Patterned Vegetation Created by Ants and Observed in Satellite Images of Arizona[END_REF] on this patterned vegetation, we proposed these discs as nest discs of ant colonies. The number of these discs, disseminated in the valley 10 km long, is impressing.

In the Figure 3, the markers are showing a large area, near the Grand Canyon rim, where, using Google Earth, we can see a patterned vegetation like that given in the Figure 4. The patterned vegetation has a "polka-dot" arrangement of discs (the pattern is given in images with smaller scales). The irregularity of the polka-dot pattern excludes an unnatural origin.

Local investigation

In a previous discussion, proposed in [START_REF] Sparavigna | Patterned Vegetation Created by Ants and Observed in Satellite Images of Arizona[END_REF], we compared the discs in the Figure 4, with a red harvester ant nest sketched in Ref. [START_REF] Davis | Management of the Red Harvester Ant Pogonomyrmex Barbatus[END_REF]. Comparison told that they are probably nests of these ants. However, since the dryland ecosystems can exhibit patterns of vegetation created by the competition among individual plants [START_REF] Penny | Local Properties of Patterned Vegetation: Quantifying Endogenous and Exogenous Effects[END_REF], in [START_REF] Sparavigna | Patterned Vegetation Created by Ants and Observed in Satellite Images of Arizona[END_REF] it was stressed the importance of a local investigation. In fact, after publications [START_REF] Sparavigna | Patterned Vegetation Created by Ants and Observed in Satellite Images of Arizona[END_REF][START_REF] Pappas | Las Vegas of Ants' Visible on Google Earth[END_REF], Robert Nagell, Doctor of Veterinary Medicine, [START_REF] Nagell | Communication by e-mail about his visit to the Toroweap Valley and survey of nests[END_REF], visited the area of Toroweap Valley of Arizona, confirming that spots are ant mounds. In his courtesy pictures, here adapted in Figure 5, it is visible the cone of the nest and the surrounding area cleared of vegetation. Then, the area of "polka-dot" vegetation of Toroweap Valley represents a remarkable large surface occupied by such colonies of ants.

Figure 2 .

 2 Figure 2. Vegetation pattern as seen in Google Earth satellite image of 14 July 2013, of a location in Australia. This is an example of "fairy circles" pattern outside Namibia given in [16].

Figure 3 .

 3 Figure 3. The markers show a large area, 10 km long, in the Toroweap Valley of the Grand Canyon, where it is possible to observe a patterned vegetation, like that given in the following Figure 3.

Figure 4 .

 4 Figure 4. On the left, the patterned vegetation having a "polka-dot" arrangement (Courtesy Google Earth, coordinates: 36°15'19.18'' N, 113° 04' 55.38'' W). On the right, the "dots" are shown in detail (Courtesy Google Earth, coordinates: 36°14'55.07'' N, 113°05'05.22'' W). The sites are in the Toroweap Valley of the Grand Canyon.

Figure 5 .

 5 Figure 5. After visiting the Toroweap Valley of Arizona, Robert Nagell, Doctor of Veterinary Medicine, confirmed that spots are ant mounds. In his courtesy pictures, it is visible the cone of the nest and the surrounding area cleared of vegetation.

Acknowledgements

Amelia Carolina Sparavigna thanks Dr. Robert Nagell for his communication and pictures.


Franck Amar Hammoutène

come

Laser-ultrasonic Poisson's ratio assessment of non-conducting isotropic materials with temporal convolution effects corrected


Alain Hénault

Alain Cournoyer

Franck Enguehard

Lionel Bertrand

A study of


Franck Enguehard

Lionel Bertrand

published or not. The documents may come

Optical characterization by temporal deconvolution of one-dimensional laser-ultrasonics signals -Part 2 : experiment


Franck Enguehard

Lionel Bertrand

published or not. The documents may come

Optical characterization by temporal deconvolution of one-dimensional laser-ultrasonics signals -Part 1 : theory


B Mezrag

F Deschaux-Beaume

M Benachour

steel/ aluminium joining using Cold Metal Transfer process

Keywords: Gas metal arc welding, Cold metal transfer, Aluminium-steel joining, Metal transfer, Heat transfer, Intermetallic

Control of mass and heat transfer for

Introduction

Steel and aluminium are commonly joined by mechanical means like screwing, riveting or clinching, but also with solid state welding processes like explosion welding, [START_REF] Han | Effect of interlayer thickness on shear deformation behaviour of AA5083 aluminium alloy/SS41 steel plates manufactured by explosive welding[END_REF] friction welding, [START_REF] Fukumoto | Friction welding process of 5052 aluminium alloy to 304 stainless steel[END_REF] or more recently by friction stir welding. [START_REF] Kimapong | Friction stir welding of aluminium alloy to steel[END_REF] In contrast, steel to aluminium fusion welding remains a technological issue due to the great difference in physical properties of both materials (melting temperature, coefficient of thermal expansion…) and most of all because of the near zero solid solubility of iron in aluminium, which involves the creation of brittle intermetallic phases. Despite the difficulties of assembling steel to aluminium by liquid state processes, several studies have been carried out using conventional processes such as arc welding, [START_REF] Andrews | Joining aluminium to mild steel by argon arc welding[END_REF][START_REF] Bel | chuk: 'Investigation of certain features of the argon-arc welding of aluminium and its alloys to steel[END_REF] resistance spot welding, [START_REF] Yasuyama | Spot welding of aluminium and steel sheet with an insert of aluminium clad steel sheet: dissimilar metal joining of aluminium and steel sheet (1 st Report)[END_REF][START_REF] Sun | Resistance spot-welding of aluminium alloy to steel with transition material -from process to performance -part I: experimental study[END_REF] brazing, [START_REF] Roulin | Strength and structure of furnace-brazed joints between aluminium and stainless steel[END_REF][START_REF] Lin | Brazability of dissimilar metals tungsten inert gas butt weldingbrazing between aluminum alloy and stainless steel with Al-Cu filler metal[END_REF] or laser welding. [START_REF] Mathieu | Laser brazing of a steel/aluminium assembly with hot filler wire (88% Al, 12% Si)[END_REF][START_REF] Sierra | Steel to aluminium key-hole laser welding[END_REF] The reactive-wetting or braze-welding technique in a lap-welding configuration specially gives promising results. [START_REF] Sierra | Galvanised steel to aluminium joining by laser and GTAW processes[END_REF][START_REF] Peyre | Generation of aluminium-steel joints with laser-induced reactive wetting[END_REF][START_REF] Sierra | Steel to aluminium braze-welding by laser process with an Al-12Si filler wire[END_REF] It consists in melting aluminium base metal or a low melting point aluminium filler metal, producing its spreading on the solid steel base metal. However, if the wetting of liquid aluminium is possible on zinc coated steels, it requires the use of a brazing flux on non coated steels. [START_REF] Sierra | Galvanised steel to aluminium joining by laser and GTAW processes[END_REF][START_REF] Peyre | Generation of aluminium-steel joints with laser-induced reactive wetting[END_REF] The quality of the bonds then greatly depends on the thickness of Fe-Al intermetallic phases formed at the interface between steel to aluminium. Good mechanical properties can be achieved when the thickness of this intermetallic layer is ,10 mm. [START_REF] Peyre | Generation of aluminium-steel joints with laser-induced reactive wetting[END_REF] The thickness of the intermetallic layer can be reduced by the addition of Si on the Al alloy. [START_REF] Sierra | Steel to aluminium braze-welding by laser process with an Al-12Si filler wire[END_REF][START_REF] Dong | Dissimilar metal joining of aluminum alloy to galvanized steel with Al-Si, Al-Cu, Al-Si-Cu and Zn-Al filler wires[END_REF] It is generally accepted that the growth kinetic of intermetallics is mainly controlled by the diffusion thought the intermetallic layer. [START_REF] Sierra | Galvanised steel to aluminium joining by laser and GTAW processes[END_REF] This phenomenon depends on temperature of the steel-aluminium interface, that has to be reduced to prevent an excessive growth. During the past few years, new controlled short-circuit Gas Metal Arc Welding processes (GMAW), allowing the melting of filler metals with low heat input, have been developed. The Cold Metal Transfer process (CMT) developed by Fronius seems to be the more promising. Due to a fine control of current waveform coupled to an alternate feeding of the filler wire, the metal transfer is possible with a very low heat input, promoting the welding of thin sheets with low distortion and gap bridging ability. [START_REF] Feng | The CMT short-circuiting metal transfer process and its use in thin aluminium sheets welding[END_REF] This process has been tested to join aluminium to zinc coated steel by braze-welding in a lap joint with Al-Si filler wires. [START_REF] Zhang | Interfacial microstructure and mechanical properties of aluminium-zinccoated steel joints made by a modified metal inert gas weldingbrazing process[END_REF][START_REF] Zhang | The arc characteristics and metal transfer behaviour of cold metal transfer and its use in joining aluminium to zinc-coated steel[END_REF][START_REF] Cao | Cold metal transfer joining aluminum alloys to galvanized mild steel[END_REF][START_REF] Cao | Cold metal transfer spot plug welding of AA6061-T6 to galvanized steel for automotive applications[END_REF] The formed intermetallic layer is ,10 mms if the heat input remains low. [START_REF] Zhang | Interfacial microstructure and mechanical properties of aluminium-zinccoated steel joints made by a modified metal inert gas weldingbrazing process[END_REF][START_REF] Cao | Cold metal transfer joining aluminum alloys to galvanized mild steel[END_REF][START_REF] Cao | Cold metal transfer spot plug welding of AA6061-T6 to galvanized steel for automotive applications[END_REF] It has been observed that the Zn coating plays an important role by promoting the wetting of aluminium, but also by preventing the formation of a thick intermetallic layer. [START_REF] Yang | Welding of aluminum alloy to zinc coated steel by cold metal transfer[END_REF] However, a too high heat input can promote the vapourisation of the Zn coating, producing a detrimental effect on the steel-aluminium bond.

The complex waveform of the welding current in the CMT process and the 'back feeding' of the filler wire that mechanically forces the metal transfer make difficult to understand the relation between welding parameters, metal transfer and heat transfer. Pickin et al. investigated the effect of the CMT process on the dilution during aluminium alloy welding, compared to a pulsed GMAW process, and concluded the dilution is lower with CMT, for the same filler wire speed. [START_REF] Pickin | Characterisation of the cold metal transfer (CMT) process and its application for low dilution cladding[END_REF] However, the effect of the various parameters of the CMT current waveform on the heat and metal transfers has never been discussed.

In this paper, we investigate the effect of various current waveforms of the CMT process on the characteristics of transfer and deposit of a Al-Si filler wire on Zn coated steel samples. The relations between waveform parameters, energy, melting rate of the filler wire, wetting of the deposited metal, and thickness of intermetallic layer are discussed.

Experimental details

During lap braze-welding of aluminium alloy with a Zn coated steel using an aluminium alloy filler metal, the main issue concerns the interaction of molten aluminium alloy with steel, generally remained in a solid state. This high temperature interaction promotes the forming and growth of brittle Fe-Al intermetallic compounds. In order to study the effect of welding parameters on the characteristics of the joining interface, weld deposits of Al alloy filler metal are achieved on Zn coated steel samples.

Welding conditions

Weld deposit of a 4043 (AlSi5) aluminium alloy filler wire (Table 1) of 1?2 mm diameter is achieved on samples of base metal of 606150 mm 2 . The base material is a zinc coated S235 steel sheet of 1mm thick.

A TransPuls Synergic 2700 CMT power source is used in this study. The GMAW-CMT process allows welding under low heat input with a controlled short-circuit metal transfer, thanks to a complex current waveform coupled to the alternate feeding of the filler wire. A typical electrical cycle obtained with this process is given in Fig. 1a. The voltage is controlled by the arc length, depending on the filler wire moving. The current waveform is characterised mainly by the 'boost' current I b , the duration of the pulse t b , the 'wait' current I w , and the short-circuit current I sc . Seventeen set of parameters were chosen for this study (Table 2). The filler wire speed V f (feed and retreat) is the same for every test, as well as the short-circuit current. The boost current and the wait current are different for each test, as well as the pulse time. As the durations of the wait current and the shortcircuit do not change drastically (they are governed by the alternate moving of the filler wire), the change in pulse time also changes the frequency of the short-circuits.

The shielding gas is a commercial argon with purity higher than 99?99% (Argon Technique, Air Products), with a flow of 12 l min [START_REF] Yang | Welding of aluminum alloy to zinc coated steel by cold metal transfer[END_REF] . The welding speed is the same for all the tests, equal to 60 cm min [START_REF] Yang | Welding of aluminum alloy to zinc coated steel by cold metal transfer[END_REF] .

Instrumentation of the welding process

The electrical parameters as well as the welding speed are recorded during the whole welding test with a frequency of 80 kHz. The welding current is measured with a Hall effect based current transducers (LEM LT 505-S), and the voltage is measured between the steel sample and the contact tip of the welding gun. In order to observe in situ the melting of the filler wire and the metal transfer, video recordings of the filler wire tip are made with a high speed camera (Phantom ir300) at 5000 images per second. To partly eliminate the arc radiation, a 808 nm interferential filter is placed on the camera, and coupled to a laser diode to light the tip of the filler wire.

The CMT process is characterised by an alternate movement of the filler wire. To control the wire speed, a DV 25 ST wire feed sensor is connected to the welding torch. In addition, the measure of wire feedrate by image correlation is made on some high speed video recordings using Phantom PCC 2?14b software to calibrate and validate the measurements of the wire feed sensor. Image analyses on high speed video recordings are also performed using ImageJ software to measure geometrical characteristics of the filler wire tip.

Characterisation methods

After each weld deposit, the total weight of deposited metal is measured using a Ohaus Explorer balance with a precision of 10 24 g. The mean width of the deposits is measured by image analysis on top views using a Leica Z16 APO macroscope with the Leica LAS software. In addition three cross-sections are cut on each sample by water jet cutting, to measure geometrical characteristics of the deposit (width, height). Finally, cross-sections were polished and finished with a 1 micrometre diamond suspension, in order to clearly observe the intermetallic layer formed at the interface between the steel and the aluminium weld deposit. The thickness of the intermetallic is measured using a Leica DMI5000 M optical microscope and the LAS software.

Results

Analysis of the CMT transfer cycle

The high speed videos coupled to electrical and wire speed measures first allow to analyse the various phases of the CMT process and the regularity of the metal transfer. Fig. 1 shows an example of transfer cycle, with the evolution of the current, voltage and wire speed measured during a test, and images corresponding to various steps of the metal transfer. The boost current begins just at the end of the short circuit phase, and allows the restriking of the electrical arc (image 2 of Fig. 1c). During all the boost phase, the wire stays in static position (the wire feedrate is about zero) and a liquid drop is formed at the wire tip (image 3). After this first phase, at the beginning of the wait phase, the current decreases drastically to avoid the growth and the detachment of the drop, and the filler wire rate increases up to a rather constant value, before decreasing again when the short-circuit phase, characterised by a near zero voltage, begins (images 4 to 6). Note that the maximum wire speed measured with wire feed sensor and image analysis (32 m min [START_REF] Yang | Welding of aluminum alloy to zinc coated steel by cold metal transfer[END_REF] ) is higher than the fixed value (20 m min [START_REF] Yang | Welding of aluminum alloy to zinc coated steel by cold metal transfer[END_REF] ). The wire feed speed then becomes negative, indicating the retreat of the wire during the end of the short-circuit phase at a constant speed slightly higher (34 m min [START_REF] Yang | Welding of aluminum alloy to zinc coated steel by cold metal transfer[END_REF] ) than the feed speed of the wait phase. During the short-circuit phase, the current is at a constant value of 40 A. Note that the arc voltage is constant during the boost phase, but continuously decreases during the wait phase due to the decrease in the arc length. The boost time is fixed as a setpoint in the CMT welding programme (Table 1). Measurements indicate the setpoint values are well followed (Fig. 3). At the opposite, the wait time and short-circuit time are not fixed as setpoints. The wait phase duration is controlled by the time required for the filler wire tip to touch the base metal surface from the distance corresponding to the arc length of the boost phase, and the duration of the short circuit phase by the time required to 'detach' the liquid metal drop under the effect of the wire retreat at constant speed. As the wire speed is constant on all the tests, the duration of the wait and short circuit phases depend on the drop volume and geometry. A large drop for instance will tend to increase the short circuit duration. Figure 3 shows the wait and short-circuit times are rather constant at ,5 ms except on tests 5 to 8, and specially on test nu 7 where it is higher than 6 ms. It is probably due to a higher drop volume that increases the short-circuit duration, and then the phase of wire retreat, producing a higher arc length during the boost phase (confirmed by a higher voltage shown in Fig. 2), and then a longer wait phase during which the wire is feeded to reach the short-circuit.

Evolution of waveform parameters and power for the various tests

As a consequence of the decrease in the boost time, the frequency of the short-circuits, calculated by Fourier transformation of the voltage signal, increases up to test nu 10, and then remains rather constant (Fig. 3). Note that the arcing time (t b plus t w ) is higher than the shortcircuit time on the first tests, whereas the arcing and short-circuit times are quite similar from test nu 10.

Figure 4 shows the evolution of the mean welding power. The mean values are numerically computed by time integration of the instantaneous electrical power on the total welding period (10 s):

P~1 t f ð tf t~0 UIdt
where U and I are respectively the instantaneous voltage and current measured, and t f the total welding period. The mean power varies from 320 W to 790 W. Note that during the short-circuit phase, the instantaneous power becomes very low (the voltage is near zero). To compare the arcing phase of the various tests, we also have computed the mean arc power. The differences between the various tests are greater for the mean arc 2 evolution of the mean current and voltage of the three phases of the CMT cycle for the various tests power, from ,370W to 1150W. Note that the mean power increases up to test nu6 mainly due to the increase in the boost current, but decreases after, the effect of the decrease in boost time being more important than the effect of the increase in boost current. After test nu10, the power increases again due to the combined effect of a slight increase in the wait current and boost duration.

In welding, the energy transmitted to the base metal is generally expressed by the linear energy corresponding to the power divided by the welding speed (UI/V, in J mm [START_REF] Yang | Welding of aluminum alloy to zinc coated steel by cold metal transfer[END_REF] ). The total linear energy has been decomposed in three contributions corresponding to the boost, wait and short-circuit phases. Figure 5 shows the evolution of these parameters on the various tests. As the welding speed is constant on all the tests, the tendency for the total linear energy is the same as the mean power. One can observe the boost energy is the main contribution to the total energy on tests 1 to 9, whereas the wait energy turns to the same order of magnitude on tests 10 to 17, because the boost time is very short.

Metal transfer

The weight of deposited metal, measured with a precision balance, is shown in Figure 6. The deposited weight per second increases up to test nu 7, then stabilises and decreases at test nu 10, before increasing again. These tendencies are globally similar to the power evolution (Fig. 4). However, one can see the deposit rate is higher for the last tests, where the short-circuit frequency is high, than for tests 6 to 8, in spite of a higher power in this last case.

The deposited weight for each short-circuit is deduced using the short-circuit frequency computed in the previous section. In addition, the evolution of the surface drops at the wire tip is measured for some tests on high speed video by image analysis. An estimated deposited weight per short circuit is then computed, considering an axisymetric drop geometry. Some variations are observed considering a series of drop of one welding test, due to the non axisymetric real geometry of the drops, but the tendencies observed by direct weight measurements are well restituted, except for tests 5 to 7 where the calculation overestimates the measured weight (Fig. 7). Image analysis can thus be used to follow the evolution of the drops weight at the wire tip during the CMT cycle.

At the end of the short-circuit phase, when the arc restrikes, a weight of molten metal of ,2 mg remains attached to the wire tip, whatever the welding parameters (Fig. 8). The drops grow during the boost phase, and continue to grow during the wait phase. One can deduce from Fig. 8 the main heat input producing the drop growth comes from the boost phase, but the wait phase contributes also significantly to the melting, the molten weight during this phase may represent ,30% of the deposited weight for some welding conditions. The weight of each deposited drop is globally higher for the first tests than for the last ones from nu 10, where the boost phase time is only ,1ms. However, due to the high short-circuit frequency, the deposit rate is as high for the last tests than for the first ones (Fig. 6).

Wetting of weld deposit and intermetallic thickness

The wetting of aluminium on zinc coated steel is a main point to guaranty a good mechanical strength of the assembly, because the width of the joining interface depends on this phenomenon.

A good and regular wetting is observed when the linear energy remains below ,500 J mm [START_REF] Yang | Welding of aluminum alloy to zinc coated steel by cold metal transfer[END_REF] . For an energy range between 530 and 650 J mm 21 , the deposit becomes less regular (Figs. 9-10). This could be due to the local vapourisation of the Zn layer, that prevents the wetting of liquid aluminium. Indeed, the direct wetting of liquid aluminium on uncoated steel is very poor compared to Zn coated steel. [START_REF] Yang | Welding of aluminum alloy to zinc coated steel by cold metal transfer[END_REF] For regular deposits (deposits 1 to 4 and 10 to 13), the width of the deposit is comprised between 4?7mm and 7?8mm, and globally increases with energy. Note that for an equivalent energy, the width of deposits 10 to 13 is higher than the width of deposits 1 to 4.

The thickness of the formed intermetallic layer is also a main point, because it determines the brittleness of the bond. It is generally admitted that due to kinetics considerations, the intermetallic formed at the Fe/Al interface is mainly Fe 2 Al 5 , [START_REF] Sierra | Steel to aluminium braze-welding by laser process with an Al-12Si filler wire[END_REF] while FeAl 3 can sometimes also be observed on the aluminium side (Fig. 11). When the aluminium alloy contains silicon, the formed Fe 2 Al 5 intermetallic generally contains a few per cent of Si, that decreases the growth kinetics of the intermetallic layer by reducing the diffusion rate of Fe and Al through Fe 2 Al 5 . [START_REF] Sierra | Steel to aluminium braze-welding by laser process with an Al-12Si filler wire[END_REF] It has been shown in previous studies that intermetallic thicknesses higher than 10 mms were generally cracked. [START_REF] Peyre | Generation of aluminium-steel joints with laser-induced reactive wetting[END_REF] For all the weld deposits, the maximum intermetallic thickness is ,8 mms, due to the low heat input associated to the CMT process. However, the intermetallic thickness can change significantly according to the waveform characteristics. In order to investigate the influence of the current waveform on the intermetallic growth, four welding conditions are compared on Fig. 12, corresponding to two different waveform types giving rather equivalent deposit rates and widths: waveforms having an arcing duration higher than short-circuit time (tests 4-5), and equivalent arcing and short-circuit durations (tests 10-11). One can observe on Fig. 12 that for the last waveform type, the intermetallic thickness is globally lower. This have to be correlated to the associated energy, which is higher for the first type of waveform, in spite of similar deposit rates.

Discussion

In GMAW process, it is generally admitted, after the demonstration of Lesnewich, [START_REF] Lesnewich | Control of melting rate and metal transfer in gas shielded metal arc welding. Part I -Control of electrode melting rate[END_REF] that the melting rate of the filler wire in direct current electrode positive (DCEP) is controlled by the combination of joule effect and condensation heat of electrons. The melting rate then increases with the welding current, generally following a polynomial law. However, has explained by Waszink, [START_REF] Waszink | Heat Generation and Heat Flow in the Filler Metal in GMA Welding[END_REF] the melting of the filler wire tip results of heat inputs, but also of heat transfer, specially from the drop surface to the solid part of the wire.

If we consider the evolution of the deposit rate with the mean current for our tests, one can see there is no clear relation (Fig. 13). One can correlate these results to the special metal transfer mechanism related to the CMT process. During the short-circuit phase, the condensation heat of electrons does not occur, and heat transfer from the liquid drop to the base metal reduces the heat transferred to the solid filler wire, and then its melting rate. The heating of the wire tip during the arcing phase by joule effect and condensation heat of electrons is then partially balanced by the heat transfer in the base metal during the short-circuit phase. The contribution of both phenomena to the melting rate seems to vary according 8 evolution of the drops weight computed from image analysis 9 relation between energy, width and regularity of the various deposits 10 general aspect of the deposits 11 Fe-Al phase diagram [START_REF] Massalski | Binary Alloy Phase Diagrams[END_REF] 12 comparison of intermetallic thickness distribution for various tests to the waveform, as suggests the variation of energy for a similar deposit rate when the short-circuit frequency increases (Figs. 56).

Figure 14 represents the relation between the mean current during the arcing phase and the melting rate (considering the melting of the wire tip occurs only during the arcing phase). One can observe for an equivalent mean current of arcing phase, that the melting rate is higher for the last tests, where the short-circuit frequency is high, than for the first ones.

The heat transferred to the base metal during the short circuit phase mainly depends on the short-circuit duration, and on the heat contained in the wire tip, which is related to its temperature and to the volume of heated filler metal. In the various tests performed in this study, the short-circuit time is about the same, so the heat transferred to the base metal depends on the last factor. For tests from nu10, the arcing time is short (,6ms), and the mass of the formed drops is rather low. In contrast, for the first welding tests, the arcing time is longer than the short-circuit duration, and the drops mass is higher, indicating a higher global heat contained in the drop, and probably a higher mean temperature, as suggested by the higher ratio between energy and deposited weight for the first tests (Fig. 15). The heat transferred from the wire tip to the base metal should then be higher, despite of the lower short-circuit frequency compared to the last tests.

That explains the higher thickness of intermetallic layer formed during the first tests (Fig. 12), with a similar deposit rate. Indeed, in GMAW-DCEP, it is generally admitted that the heating of the base metal is mainly due to the heat transfer from the deposited filler metal. The heat transferred to the steel surface during the short-circuit phases, higher for the first waveform type, thus induces the formation and growth of a thicker intermetallic layer.

Conclusions

The CMT process allows to deposit regular aluminium welds on Zn coated steel sheets, except for high heat input, where the vapourisation of the Zn layer prevents the wetting of aluminium. The decrease in the 'boost' time coupled to the increase in the boost current of the CMT waveform produces an increase in the short-circuit frequency and a decrease in the drop weight deposited during each short-circuit. This kind of waveform produces the best results in steel-aluminium joining because the heat transferred to the base metal is reduced for an equivalent deposited weight, and the thickness of the formed Fe-Al intermetallic layer is lower.

Figure 2

 2 Figure2shows the evolution of the current of the three phases of the CMT cycle measured during the various tests. These values correspond to the mean currents numerically computed from the recordings performed during the total welding time. The current values measured are rather close to the setpoint values (Table1), indicating a good current control of the CMT process. One can see the boost current increases slowly on the three first tests, then faster up to the 10th test and finally remains constant

  14 evolution of the melting rate versus the mean arc current considering only the arcing phase

Table 1

 1 Composition of the 4043 (AlSi5) filler wire

	Element	Si	Mn	Mg	Cu	Fe	Zn	Ti	Al
	Weight -%	5	,0,05	,0,05	,0,05	,0,40	,0,10	,0,20	Solde

Table 2

 2 Welding parameters for the various tests

	Test Nu I b (A)	t b (ms) I w (A) I sc (A) V f (m min 21 )
	1	2 4	2 9	1 5	4 0	2 0
	2	2 7	2 2	1 5	4 0	2 0
	3	3 0	1 5	1 5	4 0	2 0
	4	4 7 ?1 13?01	16?4	40	20
	5	6 4 ?3 11?01	17?8	40	20
	6	8 1 ?4	9?01	19?2	40	20
	7	9 8 ?5	7?01	20?7	40	20
	8	115?7	5?01	22?1	40	20
	9	132?8	3?01	23?5	40	20
	10	150	1	25	40	20
	11	150	1?15	28?8	40	20
	12	150	1?30	32?6	40	20
	13	150	1?38	34?6	40	20
	14	150	1?53	38?4	40	20
	15	150	1?61	40?3	40	20
	16	150	1?69	42?3	40	20
	17	150	1?76	44?2	40	20

example of CMT electrical and wire speed cycle (a-b) and corresponding high speed video recording (c)

mean power for the various tests

evolution of the deposit rate for the various tests

Acknowledgements

The authors gratefully acknowledge the programme Averroes 4 of Erasmus Mundus for its financial support of this work.