Buried Corrosion Detection in Multi-layer Airframe Structures
Substantial enhancements in Pulsed Eddy Current instrumentation and array probes provide a unique capability for the detection and imaging of sub-layer corrosion. The use of giant magnetoresistive (GMR) elements in an inductive array provides increased depth of sensitivity over conventional eddy-curr ent sensing coils. This paper provides an overview of newly developed instrumentation, whic h provides a portable, battery-operated platform for multi-layer corrosion dete ction in flight line or repair facilities. Demonstrated capability includes rapid inspec tion and detection of material loss in lap joints with air gaps and complex geometries. Advanced imaging capability and post-processing allows multi-layer analysis, with distinct C-scan images of up to four (4) 1-mm layers.
Studies of laser brazing with regard to the quality influencing parameters
Laser joining processes, such as brazing and welding, are a common application in industry,
especially in the automotive industry. These processes are the key to lightweight
and efficient design with regard to the automotive industry. There, laser brazing is used
mainly for visible joints due to the superior paint adhesion and surface roughness of
brazed joints compared to welds. As laser brazing is applied in the automotive industry
without using any fluxes or shielding gas, this leads to a difficulty in maintaining and
ensuring the quality of brazed joints.
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Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.