Flow monitoring in liquid composite molding based on linear direct current sensing technique

On‐line monitoring of the Liquid Composite Molding (LCM) process is a key element to improve part quality and to reduce manufacturing cost. In this context, direct current (DC) resistance measurement is a promising sensing technique allowing to track both flow front position and degree of curing of the resin during injection. An original measurement technique called Linear Direct Current (LDC) based on the same physical principle as DC was developed to record the flow front position continuously and not only at discrete nodes location. Preliminary measurements in a 1D‐flow channel have shown the large influence of the hardware configuration on the accuracy of the LDC system. Further experiments were conducted with the support of an embedded print plate in the flow channel as LDC sensor. These 2D measurements have demonstrated the relatively good spatial accuracy and stability of the LDC system. Permeability characterization in the 1D‐flow channel based on flow front position delivered by LDC, ultrasound monitoring techniques and visual recording is also presented in this paper. The flow front position determined by conventional methods based on video capture is taken as reference. Comparison of the permeability values (K_visual, K_LDC and K_US) shows a maximum deviation of 6.93% given by the LDC‐system. The ultrasound interface change measurement principle allows very accurate and reproducible punctual flow front tracking and shows a maximum deviation compared to the reference permeability (K_visual) of 3.32%. This study shows the potential of the investigated techniques for flow front monitoring of LCM injection processes.     

Learned Stochastic Mobility Prediction for Planning with Control Uncertainty on Unstructured Terrain

Motion planning for planetary rovers must consider control uncertainty in order to maintain the safety of the platform during navigation. Modeling such control uncertainty is difficult due to the complex interaction between the platform and its environment. In this paper, we propose a motion‐planning approach whereby the outcome of control actions is learned from experience and represented statistically using a Gaussian process regression model. This mobility prediction model is trained using sample executions of motion primitives on representative terrain, and it predicts the future outcome of control actions on similar terrain. Using Gaussian process regression allows us to exploit its inherent measure of prediction uncertainty in planning. We integrate mobility prediction into a Markov decision process framework and use dynamic programming to construct a control policy for navigation to a goal region in a terrain map built using an onboard depth sensor. We consider both rigid terrain, consisting of uneven ground, small rocks, and nontraversable rocks, and also deformable terrain. We introduce two methods for training the mobility prediction model from either proprioceptive or exteroceptive observations, and we report results from nearly 300 experimental trials using a planetary rover platform in a Mars‐analogue environment. Our results validate the approach and demonstrate the value of planning under uncertainty for safe and reliable navigation.     

Some approximate Godunov schemes to compute shallow-water equations with topography

We study here the computation of shallow-water equations with topography by Finite Volume methods, in a one-dimensional framework (though all methods introduced may be naturally extended in two dimensions). All methods are based on a discretisation of the topography by a piecewise function constant on each cell of the mesh, from an original idea of Le Roux et al. Whereas the Well-Balanced scheme of Le Roux is based on the exact resolution of each Riemann problem, we consider here approximate Riemann solvers. Several single step methods are derived from this formalism, and numerical results are compared to a fractional step method. Some test cases are presented: convergence towards steady states in subcritical and supercritical configurations, occurrence of dry area by a drain over a bump and occurrence of vacuum by a double rarefaction wave over a step. Numerical schemes, combined with an appropriate high-order extension, provide accurate and convergent approximations.     

Modelling and predicting partial orders from pairwise belief functions

In this paper, we introduce a generic way to represent and manipulate pairwise information about partial orders (representing rankings, preferences, ...) with belief functions. We provide generic and practical tools to make inferences from this pairwise information and illustrate their use on the machine learning problems that are label ranking and multi-label prediction. Our approach differs from most other quantitative approaches handling complete or partial orders, in the sense that partial orders are here considered as primary objects and not as incomplete specifications of ideal but unknown complete orders.     

Nonparametric Traversability Estimation in Partially Occluded and Deformable Terrain

Terrain traversability estimation is a fundamental requirement to ensure the safety of autonomous planetary rovers and their ability to conduct long‐term missions. This paper addresses two fundamental challenges for terrain traversability estimation techniques. First, representations of terrain data, which are typically built by the rover's onboard exteroceptive sensors, are often incomplete due to occlusions and sensor limitations. Second, during terrain traversal, the rover‐terrain interaction can cause terrain deformation, which may significantly alter the difficulty of traversal. We propose a novel approach built on Gaussian process (GP) regression to learn, and consequently to predict, the rover's attitude and chassis configuration on unstructured terrain using terrain geometry information only. First, given incomplete terrain data, we make an initial prediction under the assumption that the terrain is rigid, using a learnt kernel function. Then, we refine this initial estimate to account for the effects of potential terrain deformation, using a near‐to‐far learning approach based on multitask GP regression. We present an extensive experimental validation of the proposed approach on terrain that is mostly rocky and whose geometry changes as a result of loads from rover traversals. This demonstrates the ability of the proposed approach to accurately predict the rover's attitude and configuration in partially occluded and deformable terrain.     

Intergranular Oxidation of Nickel-Base Alloys: Potentialities of Focused Ion Beam Tomography

Oxidation of Metals - The present work focuses on the intergranular oxidation of Alloy 600 and its weld material Alloy 82 after exposure in simulated primary water at 340–360 °C...