Hybrid Technique for Underbody Noise Transmission of Wind Noise

2011 ◽  
Author(s):  
Philippe Moron ◽  
Andreas Hazir ◽  
Bernd Crouse ◽  
Robert Powell ◽  
Barbara Neuhierl ◽  
...  
Keyword(s):  
Hybrid Technique ◽  
Wind Noise ◽  
Noise Transmission

Acoustic microscopy techniques for the inspection of integrated circuit devices and packages

1992 ◽  
Vol 50 (2) ◽  
pp. 966-967
Author(s):  
Thomas M. Moore
Keyword(s):  
Integrated Circuit ◽  
Acoustic Microscopy ◽  
Hybrid Technique ◽  
Frequency Scanning ◽  
Ic Packaging ◽  
Ic Package ◽  
Microscopy Techniques ◽  
Ic Package Inspection ◽  
Pulse Echo ◽  
Scanning Acoustic Microscope

In the last decade, a variety of characterization techniques based on acoustic phenomena have come into widespread use. Characteristics of matter waves such as their ability to penetrate optically opaque solids and produce image contrast based on acoustic impedance differences have made these techniques attractive to semiconductor and integrated circuit (IC) packaging researchers.These techniques can be divided into two groups. The first group includes techniques primarily applied to IC package inspection which take advantage of the ability of ultrasound to penetrate deeply and nondestructively through optically opaque solids. C-mode Acoustic Microscopy (C-AM) is a recently developed hybrid technique which combines the narrow-band pulse-echo piezotransducers of conventional C-scan recording with the precision scanning and sophisticated signal analysis capabilities normally associated with the high frequency Scanning Acoustic Microscope (SAM). A single piezotransducer is scanned over the sample and both transmits acoustic pulses into the sample and receives acoustic echo signals from the sample.

Hybrid Technique of Fast RCS Computation With Characteristic Modes and AWE

2007 ◽  
Vol 6 ◽  
pp. 464-467 ◽  
Author(s):  
You-Bao Wang ◽  
Y. M. Bo ◽  
G. Q. Ji ◽  
D. Ben ◽  
G. X. Jiang ◽  
...  
Keyword(s):  
Hybrid Technique ◽  
Characteristic Modes

A modified version of MATLAB application window for predicting the weld bead profile and stress–strain plot of AA5052 CMT weldment using ER4043

SIMULATION ◽  
2021 ◽  
pp. 003754972110315
Author(s):  
B Girinath ◽  
N Siva Shanmugam
Keyword(s):  
Strain Curve ◽  
Weld Bead ◽  
Welding Parameters ◽  
Bead Geometry ◽  
Hybrid Technique ◽  
Stress Strain Curve ◽  
Stress Strain ◽  
Weld Bead Geometry ◽  
Inference System ◽  
Engineering Stress

The present study deals with the extended version of our previous research work. In this article, for predicting the entire weld bead geometry and engineering stress–strain curve of the cold metal transfer (CMT) weldment, a MATLAB based application window (second version) is developed with certain modifications. In the first version, for predicting the entire weld bead geometry, apart from weld bead characteristics, x and y coordinates (24 from each) of the extracted points are considered. Finally, in the first version, 53 output values (five for weld bead characteristics and 48 for x and y coordinates) are predicted using both multiple regression analysis (MRA) and adaptive neuro fuzzy inference system (ANFIS) technique to get an idea related to the complete weld bead geometry without performing the actual welding process. The obtained weld bead shapes using both the techniques are compared with the experimentally obtained bead shapes. Based on the results obtained from the first version and the knowledge acquired from literature, the complete shape of weld bead obtained using ANFIS is in good agreement with the experimentally obtained weld bead shape. This motivated us to adopt a hybrid technique known as ANFIS (combined artificial neural network and fuzzy features) alone in this paper for predicting the weld bead shape and engineering stress–strain curve of the welded joint. In the present study, an attempt is made to evaluate the accuracy of the prediction when the number of trials is reduced to half and increasing the number of data points from the macrograph to twice. Complete weld bead geometry and the engineering stress–strain curves were predicted against the input welding parameters (welding current and welding speed), fed by the user in the MATLAB application window. Finally, the entire weld bead geometries were predicted by both the first and the second version are compared and validated with the experimentally obtained weld bead shapes. The similar procedure was followed for predicting the engineering stress–strain curve to compare with experimental outcomes.

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