Search

Directed Energy Deposition of the commercial intermetallic Ti-48Al-2Cr-2Nb alloy was investigated. The CLAD® process is dependent on multiple parameters, which were successfully optimised through several experiments, including series of beads, small blocks, and massive blocks, under argon atmosphere. The use of adapted temperature management leads to massive blocks manufacturing that bear no apparent macroscopic defects, such as cracks, which are generally observed in this brittle material due to strong temperature cycling during the manufacturing. The microstructure and geometrical parameters were characterised by scanning electron microscopy (SEM). This process generates an ultra-fine and anisotropic microstructure, which is restored to a homogeneous duplex microstructure by a subsequent heat-treatment. Mechanical characterisation is in progress and will be used to validate the soundness of the materials produced in these conditions.

Titanium alloy powders, mostly the Ti-64 alloy with composition Ti-6Al-4V (wt.%), are extensively used for biomedical and high-demanding applications, e.g. in aeronautics. Advanced powder metallurgy processes like additive manufacturing are strong drivers for the production of high quality powders, insuring a good rheological behavior, a low porosity level in the parts and an adequate roughness. Ideally, fine spherical particles with no volume and surface defects are aimed at. Inert gas atomization is a process that is able to produce clean powders for such applications, especially for AM. To avoid the chemical reaction of titanium with the commonly used alumina crucibles, a crucible-free configuration has been designed in the EIGA process (Electrode induction melting Inert Gas Atomization). Two EIGA facilities are currently available in France for R&D purposes: a one-liter (nominal rod diameter/length (mm): 50/500) labscale facility at ONERA and an eight-liter capacity (100/1000) industrial-scale facility at the MetaFensch Institute. Using the smaller facility to screen and optimize atomization parameters and transfer them to the bigger facility is a tempting approach. This communication will show the characteristics of Ti-64 powders produced in both facilities, in terms of particle shape, particle size distribution, presence of satellites and porous particles.

Unexpected failures of industrial gearboxes may cause significant economic losses. It is therefore important to detect early fault symptoms. This paper introduces signal processing methods based on approximate entropy (ApEn) and Lempel-Ziv Complexity (LZC) for defect detection of gears. Both methods are statistical measurements exploring the regularity of a vibratory signal. Applied to gear signals, the parameter selection of ApEn and LZC calculation are first numerically investigated, and appropriate parameters are suggested. Finally, an experimental study is presented to investigate the effectiveness of these indicators. The results demonstrate that ApEn and LZC provide alternative features for signal processing. A new methodology is presented combining both Kurtosis and LZC for early detection of faults. The results show that this proposed method may be used as an effective tool for early detection of gear faults.

To detect rolling element bearing defects, many researches have been focused on Motor Current Signal Analysis (MCSA) using spectral analysis and wavelet transform. This paper presents a new approach for rolling element bearings diagnosis without slip estimation, based on the wavelet packet decomposition (WPD) and the Hilbert transform. Specifically, the Hilbert transform first extracts the envelope of the motor current signal, which contains bearings fault-related frequency information. Subsequently, the envelope signal is adaptively decomposed into a number of frequency bands by the WPD algorithm. Two criteria based on the energy and correlation analyses have been investigated to automate the frequency band selection. Experimental studies have confirmed that the proposed approach is effective in diagnosing rolling element bearing faults for improved induction motor condition monitoring and damage assessment.

This paper presents a method for constructing spectrum in non-stationary vibration using time series vector autoregressive model. A modal classification criterion and a modal amplification factor are introduced based on the eigen-decomposition of the block data matrix. The classical spectrum computation can be therefore modified to amplify only the physical modes and lighten the computation induced modes. In combination with a sliding window technique, it is shown that the method provides a clear, lightweight spectral representation of non stationary vibration. Application on the vibration signals measured from a moving flexible robot along a given trajectory shows the effectiveness and applicability of the method. The technique is integrated in the online modal surveillance software call STAR (Short Time AutoRegressive).