International audienceThe multi-scale description of dilute or semi-dilute suspensions involving rods has been successfully accomplished and applied in many scenarios of industrial interest. Many processes involve, however, the flow of rod suspensions in very narrow gaps whose thickness is much smaller than the rod length. In these conditions, the evolution of rod orientation is expected to be affected by confinement effects. In the present work, we propose a multi-scale description of rod orientation in confined conditions and simple shear flows

Multi-material stacks comprising composite and metallic layers are widely used in aerospace components. For the assembly of structural part, high-quality holes are required to ensure the performance of the mechanical fastening (rivet/bolt). On the final assembly lines of aircraft structures, these machining operations are often performed using an electric automated drilling unit (eADU). When drilling hybrid stacks, the difference in machinability of the materials (CFRP, titanium alloy, and aluminum alloy) makes it difficult to avoid delamination, fibre pullout, matrix degradation, burrs, roughness, and size defects. Therefore, each material must be drilled with suitable machining conditions to meet the demanding quality requirements. To this end, automated material detection would allow for the adjustment of appropriate cutting parameters for each material. Due to the numerous stack configurations (material, thickness) and the variability of process parameters (tool geometry, cutting conditions, lubrication, etc.), automated material detection is not an easy task. To address this issue in eADU applications, this paper presents a novel approach to identify during the process the drilled material being manufactured using a random forest (RF) machine learning model and multi-sensor data fusion. Cutting forces, vibration, micro-lubrication conditions (flow rate and pressure), and eADU spindle and feed motor currents are monitored on a dedicated drilling test rig. Numerous tests were performed on Al7175/CFRP stacks with different cutting conditions to validate the proposed methodology. Advanced signal processing and analysis in the time and frequency domains are used for feature extraction to identify Al7175 from the CFRP. The input features of the RF model were selected using the feature importance measure embedded in the RF model learning process. The knowledge process of drilling multiple material stacks is also considered. The results showed that the features extracted from the frequency domain are more effective in identifying the CFRP Al7175 than those extracted from the time domain. This optimal subset was then used to build the RF model. The proposed methodology resulted in a highly accurate classification allowing the implementation of an adaptive machining process on the eADU for hybrid stack drilling.

Non-destructive testing (NDT) of materials and structures is a very important industrial issue in the fields of transport, aeronautics and space as well as in the medical domain. Active infrared thermography is an NDT method that consists of providing an external excitation to cause an elevation of the temperature field in the material, consequently allowing evaluation of the resulting temperature field at the surface. However, thermal exciters that are used (flash lamps, halogen, lasers) act only on the surface of the sample. On the other hand, several energy conversion systems can lead to the generation of volumetric sources; the phenomena of thermo-acoustics, thermo-induction, thermomechanics or thermochemistry can be cited. For instance, ultrasonic waves can generate volumetric heat sources if the material is viscoelastic or if there is a defect. The reconstruction of these sources is the initial process for the quantification of parameters responsible for the heating. Characterizing a heat source means reconstructing its geometry and the supplied power. Identification of volumetric heat sources from surface temperature fields is a mathematically ill-posed problem. The main cause of the issue is the diffusive nature of the temperature. In this work, 3D reconstruction of the volumetric heat sources from the resulting surface temperature field, measured by infrared thermography, is studied. An analysis of the physical problem enables specifying the limits of the reconstruction. In particular, a criterion on the achievable spatial resolution is defined, and a reconstruction limitation for in-depth sources is highlighted.

Knowledge of material emissivity maps and their true temperatures is of great interest for contactless process monitoring and control with infrared cameras when strong heat transfer and temperature change are involved. This approach is always followed by emissivity or reflections issues. In this work, we describe the development of a contactless infrared imaging technique based on the pyro-reflectometry approach and a specular model of the material reflection in order to overcome emissivities and reflections problems. This approach enables in situ and real-time identification of emissivity fields and autocalibration of the radiative intensity leaving the sample by using a black body equivalent ratio. This is done to obtain the absolute temperature field of any specular material using the infrared wavelength. The presented set up works for both camera and pyrometer regardless of the spectral range. The proposed method is evaluated at room temperature with several heterogeneous samples covering a large range of emissivity values. From these emissivity fields, raw and heterogeneous measured radiative fluxes are transformed into complete absolute temperature fields.

In this paper, the problem of the quantitative characterization of thermal resistance fields in a multilayer sample is addressed by using the classical front face flash method as the thermal excitation and infrared thermography (IRT) as the monitoring sensor. In this challenging problem, the complete inverse processing of a multilayer analytical model is difficult due to the lack of sensitivity of some parameters (layer thickness, depth of thermal resistance, etc.) and the expansive computational iterative processing. For these reasons, the proposed strategy is to use a simple multilayer problem where only one resistive layer is estimated. Moreover, to simplify the inverse processing often based on iterative methods, an asymptotic development method is proposed here. Regarding the thermal signal reconstruction (TSR) methods, the drawback of these methods is the inability to be quantitative. To overcome this problem, the method incorporates a calibration process originating from the complete analytical quadrupole solution to the thermal problem. This analytical knowledge allows self-calibration of the asymptotic method. From this calibration, the quantitative thermal resistance field of a sample can be retrieved with a reasonable accuracy lower than 5%.