Non-destructive mechanical characterization of metal to metal bond interface for 3D-ICs

2011 ◽  
Author(s):  
Riko I Made ◽  
Chee Lip Gan
Keyword(s):  
Mechanical Characterization ◽  
Metal Bond ◽  
3D Ics ◽  
Bond Interface ◽  
Non Destructive

Mechanical Characterization of Building Stones through DT and NDT Tests: Research of Correlations for the In Situ Analysis of Ancient Masonry

2014 ◽  
Vol 628 ◽  
pp. 85-89 ◽  
Author(s):  
Emilia Vasanelli ◽  
Maria Sileo ◽  
Giovanni Leucci ◽  
Angela Calia ◽  
Maria Antonietta Aiello ◽  
...  
Keyword(s):  
Mechanical Characterization ◽  
Diagnostic Procedures ◽  
Ultrasonic Pulse Velocity ◽  
Mechanical Analysis ◽  
Ultrasonic Pulse ◽  
Pulse Velocity ◽  
Reliable Technique ◽  
Non Destructive

In this paper, the use of ultrasonic pulse velocity (UPV) testing as a reliable technique to determine the compressive strength of a calcarenitic stone typical of Salento (South of Italy), known as Lecce Stone (LS) has been investigated. The scope of the experimental research is to establish correlations between the results obtained by non-destructive and destructive tests, in order to reduce the use of destructive methods within the diagnostic procedures for the mechanical analysis and qualification of ancient masonries. Furthermore, the presence of water as a variable affecting the test was investigated. The results of the tests show that the UPV values are well correlated with the compressive strengths and this method showed to be efficient in predicting the strength of LS.

Mechanical characterization of clear wood from Portuguese poplar

BioResources ◽  
2019 ◽  
Vol 14 (4) ◽  
pp. 9677-9685
Author(s):  
Sandra R. S. Monteiro ◽  
Carlos E. J. Martins ◽  
Alfredo M. P. G. Dias ◽  
Helena Cruz
Keyword(s):  
Mechanical Characterization ◽  
Longitudinal Vibrations ◽  
Clear Wood ◽  
Hardwood Species ◽  
Structural Applications ◽  
Tension And Compression ◽  
Species Characteristics ◽  
Non Destructive ◽  
Moduli Of Elasticity

Poplar wood is a light, soft, and fast-growing timber from a hardwood species, characteristics that make it suitable for several applications. This study focused on the mechanical characterization of Portuguese poplar species, namely white poplar (Populus alba) and black poplar (P. nigra), aiming for its structural use. Therefore, a sample of lamellae was assessed to determine its density and dynamic modulus of elasticity, using a non-destructive device, based on longitudinal vibrations. Clear wood specimens were obtained from a set of lamellae to perform tension and compression parallel-to-grain tests. These tests were used to determine the moduli of elasticity in tension and compression and the tensile and compressive strengths and strains. Also, typical stress-strain curves were identified for the sample studied. The results stressed the potential for structural applications of Portuguese poplar.

scholarly journals Advances on the use of non-destructive techniques for mechanical characterization of stone masonry: GPR and sonic tests

2017 ◽  
Vol 5 ◽  
pp. 1108-1115 ◽  
Author(s):  
Rachel Martini ◽  
Jorge Carvalho ◽  
Nuno Barraca ◽  
António Arêde ◽  
Humberto Varum
Keyword(s):  
Mechanical Characterization ◽  
Stone Masonry ◽  
Non Destructive ◽  
Non Destructive Techniques

Physical and mechanical characterization of a commercial rendering mortar using destructive and non-destructive techniques

2019 ◽  
Vol 224 ◽  
pp. 835-849 ◽  
Author(s):  
Guillermo Aragón ◽  
Ángel Aragón ◽  
Amaia Santamaría ◽  
Alberto Esteban ◽  
Francisco Fiol
Keyword(s):  
Mechanical Characterization ◽  
Non Destructive ◽  
Non Destructive Techniques

scholarly journals Non-destructive Method of the Assessment of Stone Masonry by Artificial Neural Networks

2020 ◽  
Vol 14 (1) ◽  
pp. 84-97 ◽  
Author(s):  
Rachel Martini ◽  
Jorge Carvalho ◽  
António Arêde ◽  
Humberto Varum
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Elastic Modulus ◽  
Network Architecture ◽  
Mechanical Characterization ◽  
Stone Masonry ◽  
Historical Building ◽  
Artificial Neural ◽  
Non Destructive

Background: In this study , a methodology based on non-destructive tests was used to characterize historical masonry and later to obtain information regarding the mechanical parameters of these elements. Due to the historical and cultural value that these buildings represent, the maintenance and rehabilitation work are important to maintain the appreciation of history. The preservation of buildings classified as historical-cultural heritage is of social interest, since they are important to the history of society. Considering the research object as a historical building, it is not recommended to use destructive investigative techniques. Objective: This work contributes to the technical-scientific knowledge regarding the characterization of granite masonry based on geophysical, mechanical and neural networks techniques. Methods: The database was built using the GPR (Ground Penetrating Radar) method, sonic and dynamic tests, for the characterization of eight stone masonry walls constructed in a controlled environment. The mechanical characterization was performed with conventional tests of resistance to uniaxial compression, and the elastic modulus was the parameter used as output data of ANNs. Results: For the construction and selection of network architecture, some possible combinations of input data were defined, with variations in the number of hidden layer neurons (5, 10, 15, 20, 25 and 30 nodes), with 122 trained networks. Conclusion: A mechanical characterization tool was developed applying the Artificial Neural Networks (ANN), which may be used in historic granite walls. From all the trained ANNs, based on the errors attributed to the estimated elastic modulus, networks with acceptable errors were selected.

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