PSI - Issue 18

Volume 18 • 201 9

ISSN 2452-3216

ELSEVIER

25th International Conference on Fracture and Structural Integrity

Guest Editors: Francesco I acoviello L uca Susmel

D onato Firrao Giuse pp e Ferro

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Procedia Structural Integrity 18 (2019) 1–2

25th International Conference on Fracture and Structural Integrity Editorial Francesco Iacoviello a *, Luca Susmel b , Donato Firrao c , Giuseppe Ferro c a Università di Cassino e del Lazio Meridionale, via G. Di Biasio 43, 03043 Cassino (FR) Italy b University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK c Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129, Torino, Italy 25th International Conference on Fracture and Structural Integrity Editorial Francesco Iacoviello a *, Luca Susmel b , Donato Firrao c , Giuseppe Ferro c a Università di Cassino e del Lazio Meridionale, via G. Di Biasio 43, 03043 Cassino (FR) Italy b University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK c Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129, Torino, Italy

© 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Gruppo Italiano Frattura (IGF) ExCo. © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Gruppo Italiano Frattura (IGF) ExCo. © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Gruppo Italiano Frattura (IGF) ExCo. Keywords: Editorial

Three years ago, June 2016, the Italian Group of Fracture (IGF) organized in Catania the ECF21, the 21 st European Conference on Fracture. The event was undoubtedly a great success with more than seven hundred participants. For that event, the proceedings were published in an issue of Procedia Engineering , as Procedia Structural Integrity , the open access product that publishes conference proceedings organized by the European Structural Integrity Society (ESIS), by its Technical Committees or by the National Groups that are affiliated with ESIS, was starting its first publications in that year. Now, after three years, IGF organizes again its biennial conference in Catania, as the 25th International Conference on Fracture and Structural Integrity . In these three years, Procedia Structural Integrity published 14 issues up to now and, thanks to the quality of the published papers and to the hard work of a great academic community, it obtained different indexations (Google Scholar, Scopus and ISI-WoS). Now Procedia Structural Integrity is able to support the events organized by the fracture and structural integrity community, offering a high quality publication platform for the proceedings. The event in Catania in 2019 was a great success, with about 140 presentations and this issue publishes more than one hundred papers offering a view of the research activities in the fracture and structural integrity fields both in Italy and in many different countries, with the 70 % of the participants coming from other countries like Russia, Germany, France, Greece, Morocco, Ukraine etc. During the conference, René de Borst, Reinhard Pippan and Sergio Reale were awarded with the IGF Honorary memberships. In addition, two new awards were established: “Manson-Coffin IGF medal” and “Paolo Lazzarin IGF Medal”. Keywords: Editorial Three years ago, June 2016, the Italian Group of Fracture (IGF) organized in Catania the ECF21, the 21 st European Conference on Fracture. The event was undoubtedly a great success with more than seven hundred participants. For that event, the proceedings were published in an issue of Procedia Engineering , as Procedia Structural Integrity , the open access product that publishes conference proceedings organized by the European Structural Integrity Society (ESIS), by its Technical Committees or by the National Groups that are affiliated with ESIS, was starting its first publications in that year. Now, after three years, IGF organizes again its biennial conference in Catania, as the 25th International Conference on Fracture and Structural Integrity . In these three years, Procedia Structural Integrity published 14 issues up to now and, thanks to the quality of the published papers and to the hard work of a great academic community, it obtained different indexations (Google Scholar, Scopus and ISI-WoS). Now Procedia Structural Integrity is able to support the events organized by the fracture and structural integrity community, offering a high quality publication platform for the proceedings. The event in Catania in 2019 was a great success, with about 140 presentations and this issue publishes more than one hundred papers offering a view of the research activities in the fracture and structural integrity fields both in Italy and in many different countries, with the 70 % of the participants coming from other countries like Russia, Germany, France, Greece, Morocco, Ukraine etc. During the conference, René de Borst, Reinhard Pippan and Sergio Reale were awarded with the IGF Honorary memberships. In addition, two new awards were established: “Manson-Coffin IGF medal” and “Paolo Lazzarin IGF Medal”.

2452-3216 © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Gruppo Italiano Frattura (IGF) ExCo. 2452-3216 © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Gruppo Italiano Frattura (IGF) ExCo. * Corresponding author. Tel.: +39.07762993681; fax: +39.07762993781. E-mail address: iacoviello@unicas.it * Corresponding author. Tel.: +39.07762993681; fax: +39.07762993781. E-mail address: iacoviello@unicas.it

2452-3216  2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Gruppo Italiano Frattura (IGF) ExCo. 10.1016/j.prostr.2019.08.133

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The “ Manson-Coffin IGF medal ” is awarded to outstanding academics in recognition of their seminal contribution to the formalization, implementation, and validation of novel theories and methodologies specifically devised to model fatigue related phenomena. The medal is established for the first time in 2019, one hundred years after the birth of S. S. Manson and one hundred and two years after the birth of L. F. Coffin. These two researchers have played a pivotal role in our understanding of damage under time-variable loading by independently proposing to use cyclic elasto plastic strains to assess lifetime in the low/medium-cycle fatigue regime. The “ Paolo Lazzarin IGF Medal ” promoted by IGF in memory of Prof. Paolo Lazzarin who suddenly passed away in 2014. Prof. Paolo Lazzarin has strongly contributed with his research to educate students and young researchers in the field of fatigue design by means of innovative local approaches. He has been a pioneering researcher in different fields of materials design. The “ Paolo Lazzarin IGF Medal ” is given in recognition of distinguished work that has made or is making a notable contribution to the branches of theoretical, numerical and experimental studies of stress fields near notches and fracture and fatigue assessment of materials in the presence of defects and geometrical discontinuities. The award is established for the first time in 2019 at five years from the death of Prof. Lazzarin and it will be assigned every two years to people who have been contributing outstandingly to the above mentioned research fields in terms of scientific impact and education. For these two new IGF awards, the 2019 winners are Neil James ( Manson-Coffin IGF medal ) and Filippo Berto ( Paolo Lazzarin IGF Medal ). In 2007 IGF started to video-record the IGF events and to publish these video-recordings in its YouTube channel. According to the IGF tradition, the majority of the presentations given in Catania also in 2019 are available on the IGF YouTube channel at: https://www.youtube.com/c/IGFTube. We hope the readers of these Proceedings will enjoy both the technical articles and the recorded talks. To conclude, a big “thank you” goes to all the members of the IGF Ex-Co for supporting not only the publication of this Proceedings, but also all the different activities done by the IGF on a daily basis: the fantastic results that the IGF has obtained in recent years have been possible also, and above all, thanks to the hard work done by everyone… actually, probably, among the best Ex-Cos that the IGF has ever had, i.e.: Vittorio Di Cocco (Università di Cassino e del Lazio Meridionale) Giuseppe Ferro (Politecnico di Torino, Vice President) Angelo Finelli (retired from ENEA, Treasurer) Donato Firrao (Politecnico di Torino, Vice President) Francesco Iacoviello (Università di Cassino e del Lazio Meridionale, President)

Carmine Maletta (Università della Calabria) Giacomo Risitano (Università di Messina) Andrea Spagnoli (Università di Parma) Luca Susmel (University of Sheffield, Secretary)

These last lines are a “copy and past” of the Editorial published in 2018 in Procedia Structural Integrity for the IGF Workshop “Fracture and Structural Integrity” … all the ExCo members daily help the IGF in all its activities and it is a duty and a pleasure to remember their efforts and the time they spend for the association. We hope that the new ExCo elected in Catania will be able to keep the level of the IGF activities as high as the level of the last years.

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Procedia Structural Integrity 18 (2019) 858–865

25th International Conference on Fracture and Structural Integrity A constitutive model to predict the pseudo-elastic stress-strain behaviour of SMA Costanzo Bellini 1 * and Stefano Natali 2 1 Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, via G. Di Biasio 43, Cassino 03043, Italy 2 Department of Chemical Engineering, Materials and Environment, Sapienza University of Rome, via Eudossiana 18, Roma 00184, Italy Abstract Shape memory alloys (SMAs) are a wide class of materials characterized by the property to recover the initial shape also after high values of deformations. This is due to the ability of SMAs to change, in a reversible manner, their microstructure from an initial structure, often named austenite, to a final structure, named martensite. The transformations of microstructure can take place with or without one or more intermediate phases, but always without re-crystallization, implying a microstructure changing inside the crystals, without any new boundary creation. The stress-strain behaviour depends on the crystal structures. In this work, a simple model to predict the stress-strain behaviour of a PE SMA has been proposed. The results have been compared to an experimental tensile test carried out on a NiTi SMA alloy. 25th International Conference on Fracture and Structural Integrity A constitutive model to predict the pseudo-elastic stress-strain behaviour of SMA Costanzo Bellini 1 * and Stefano Natali 2 1 t t f ivil and Mechanical Engineering, University of Cassino and Southern Lazi , via G. Di Bia o 43, Cassino 3043, It l 2 Department of Chemical Engineering, Materials and Environment, Sapienza University of Rome, via Eudossiana 18, Roma 00184, Italy Abstract Shape memory alloys (SMAs) are a wide class of materials characterized by the property to recov r the initial shape also after high values of deform tions. This is due to the ability of SMAs to cha ge, in a reversible manner, their microstructure from an initial structure, often na ed austenite, to a final structure, named martensite. The transfor atio s of icrostructure can take place with or without one or more interme iate ph ses, but always without re-crystallization, implying a microstructure c anging inside th crystals, without any new boundary creation. The stress-strain behaviour depends on the crystal structures. In this work, a simple model to predict the stress-strain behaviour of a PE SMA has been proposed. The results have been compared to an experimental tensile test carried out on a NiTi SMA alloy.

© 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Gruppo Italiano Frattura (IGF) ExCo. © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Gruppo Italiano Frattura (IGF) ExCo. © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Gruppo Italiano Frattura (IGF) ExCo.

Keywords: Ductile cast iron; hot dip galvanizing; intermetallic phases. Keywords: Ductile cast iron; hot dip galvanizing; intermetallic phases.

1. Introduction Shape memory alloys (SMAs) are a wide class of materials characterized by the ability to remember the initial geometry also after high values of deformations as indicated by Otsuka et al. (2005) and by Eggeler et al. (2004). This is due to the ability of SMAs to change the initial microstructure to a final microstructure under load effect. Lagoudas et al. (2009), studying the influence of temperature on fatigue behaviour of SMA, highlighted that the microstructure 1. Introduction Shape memory alloys (SMAs) are a wide class of materials characterized by the ability to remember the initial geometry also after high values of deformations as indicated by Otsuka et al. (2005) and by Eggeler et al. (2004). This is due to the ability of SMAs to change the initial microstructure to a final microstructure under load effect. Lagoudas et al. (2009), studying the influence of temperature on fatigue behaviour of SMA, highlighted that the microstructure

2452-3216 © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Gruppo Italiano Frattura (IGF) ExCo. 2452-3216 © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Gruppo Italiano Frattura (IGF) ExCo. * Correspon ing author. Tel.: +39-0776-2993698; fax: +39-0776-2993886. E-mail address: costanzo.bellini@unicas.it * Corresponding author. Tel.: +39-0776-2993698; fax: +39-0776-2993886. E-mail address: costanzo.bellini@unicas.it

2452-3216  2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Gruppo Italiano Frattura (IGF) ExCo. 10.1016/j.prostr.2019.08.236

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transformations take place at low temperature without any new boundary origin. It means that the transformations occur inside the grains and no new grains are born during the transformation. Furthermore, these transformations are reversible, allowing to recover the initial shape just recovering the initial structure. For this, the SMAs are characterized by a typical reversible microstructure transformation without any recrystallizations; in one word, the SMAs are characterized by a transition of their microstructure.

Nomenclature SMAs Shape memory alloy PE Pseudo-elastic E A

Young’s modulus of austenite Young’s modulus of martensite Coefficient of transforming austenite Coefficient of transforming martensite

E M K A K M

In terms of mechanical behaviour, the SMAs are characterized by different stages (Fig. 1). The first one is the stage where the austenite is stable, and it is characterized by a linear elastic behaviour with Young’s modulus of the austenite. The first stage is followed by the second stage, where the microstructure changes from austenite to martensite (with or without intermediate microstructures). The second stage is often schematized as a plateau with a linear microstructure transformation, but many experimental evidences showed that at the beginning of the second stage the stress decreases, then increase with an increasing slope up to the slope of the third stage, where all the austenite transforms in the martensite. Over the third stage, the SMA shows the traditional plasticity stages up to the failure.

Fig. 1. Schematization of three stress-strain stages.

Some kinds of SMAs are the copper-based SMAs (Natali et al. 2014 and Brotzu et al. 2018) or many iron-based alloys. Due to its high performance, the most important SMA class is the NiTi alloys as indicate in Iacoviello et al. (2014). In scientific literature there are many mechanical approaches to describe the stress-strain response of NiTi SMAs as in Auricchio et al. (1997), and some approaches on fatigue damage prediction, as proposed by Furgiuele et al. (2012), Kollerov et al. (2013) and Maletta et al. (2017), but there are just a few models which take into account the role of the microstructure evolution on the mechanical behaviour. For instance, Miyazaki et al. (1989), Kang et al. (2012) and Sgambitterra et al. (2014), proposed an interesting energy approach to describe the cyclic behaviour of a NiTi SMAs, taking into account the ratcheting effect. In this work, a model proposed by Di Cocco et al. (2018), able to predict the microstructure evolution, has been used in order to calculate the microstructure evolution during a single tensile test in the first three stages. Then a simple stress-strain model, able to take into account the real contribution of austenite and martensite in the mechanical

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strength, has been proposed in order to predict the stress response of a NiTi SMA, including the second stage, that is the most critical to be simulated.

2. Material and methods In this work, a near-equiatomic NiTi SMA, characterized by a pseudo-elastic behaviour, has been used to realize mini flat dog-bone tensile specimens in order to evaluate the stress-strain behaviour by means of a patented tool, suitable for the use in a diffraction test devices (Fig. 2). This tool allows performing X-Ray diffraction tests on the calibrated length of the specimens under load conditions, in order to evaluate the real microstructure (quantification of austenite and martensite).

Fig. 2. Patented tool used to perform tensile tests and x-Ray diffractions.

Results of tensile tests have been analysed in terms of engineering stress and engineering strain. Furthermore, a commercial FEM code has been used to implement the proposed mechanical model in order to compare the predicted stress-strain curve to the test results. The tensile tests are simulated by the FEM code, including the evaluation of austenite/martensite ratio calculated by the structural model proposed by Di Cocco et al. (2018). 3. Experimental results The first three stages of the engineering stress-strain curve, taken by means of step by step procedure in order to perform the X-Ray diffractions, are shown in Fig. 3. The first stage is characterized by the presence of a fully austenitic phase, and the mechanical behaviour is similar to the elastic stage of traditional metallic alloys. The second stage is characterized by a sharp decrease of the slope, followed by an increase of slope up to the martensite Young’s modulus.

600

500

400

300

200  eng. [MPa]

100

0

0

2

4

6

8

10

12

14

 eng. [%]

Fig. 3. Engineering Stress-Strain curve.

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In terms of microstructure, the first stage is characterized by the spectrum shown in Fig. 4, where it is possible to observe the evaluated positions of the lattice atoms.

Fig. 4. Diffraction spectrum of austenite and schema of atoms position.

However, in Fig 5 the spectrum of martensite shows the different position of peaks angles due to different microstructure. The position of atoms in the lattice is shown in the martensite schema in Fig. 5.

Fig. 5. Diffraction spectrum of martensite and schema of atoms position.

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The evaluation of cell parameters using the Bragg law is performed on austenite lattice at the end of the first stage (maximum elastic deformation of austenite) and on the martensite at the beginning of the third stage. The results are schematized in Fig. 6, where the presence of a deformation excess during the austenite-martensite transformation could be the reason of the sharp decrease of the stress-strain curve slope at the begin of the second stage.

3.072 �

Fig. 6. Evaluation of cell parameters of austenite and martensite under load.

Considering the model proposed by Di Cocco et al. (2014) to predict the microstructure evolution, it is possible to calculate the volume fraction of austenite and martensite respectively by the equations (1) and (2). � ��� ��� ����� ��� (1) � � � � � � ��� � �� ��� (2) where the parameters D=700 and C=1. In order to evaluate the first three stages of stress-strain curve, a simple model is proposed where the contributions of austenite and martensite are weighed by two different functions as shown in equation (3)

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�

��� � � � � � � � � ��� � � � � � � �

(3)

where A and M are obtained respectively by equations (1) and (2), and K A and K M are two measuring coefficients which consider the effect of transforming austenite (derivative of austenite function) and of transforming martensite (derivative of martensite function). E A and E M are Young’s modulus of the austenite and the martensite. In this case, the parameters are shown in table 1.

Table 1: Austenite and Martensite Parameters. Austenite

Martensite

E A

K A

E M

K M

60 [GPa]

-1250 [MPa -1 ]

28 [GPa]

60 [MPa -1 ]

The evaluation of the first three stress-strain curve stages is shown in Fig. 7, where the measures of austenite and martensite volume fraction are plotted for the same strain range.

100 150 200 250 300 350 400 450

Stage 1

σ eng. [MPa]

Stage 3

Stage 2

0 50

0

2

4

6

8

10

ε eng. [%]

0 0.2 0.4 0.6 0.8 1 1.2

Austenite Exp. Martensite Exp.

Aust. & Mart. vol. fraction

0

2

4

6

8

10

ε eng. [%]

Fig. 7. Evaluation first three stages of the engineering stress-strain curve and the measures of austenite and martensite volume fractions.

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It is possible to observe how at the middle of the second stage the volume fractions of austenite and of martensite are not 50%, as often assumed, but the volume fraction of austenite is close to 78%. The 50% of austenite and martensite is obtained near to the end of the second stage. Finally, in Fig 8, the results of FEM simulation of tensile stress are compared to the measured loads. It is possible to observe the good agreement between calculated loads and the experimental measures. In particular, in the first and in the third stages experimental and numerical curves are coincident, while in the second stage the difference is a bit larger; however, the discrepancy is undoubtedly acceptable, since it is less than 5%.

Fig. 8. Comparison between simulated tensile test and experimental results.

4. Conclusions In this work, a near-equiatomic NiTi SMA characterized by a pseudo-elastic effect is investigated in order to evaluate the tensile behaviour taking into account the microstructure evolutions. A simple constitutive model has been proposed in order to predict the first three stages starting from the austenite/martensite volume fractions. The main results can be summarized as follows: 1) The second stage of stress-strain curve it is not a perfect plateau, but presents a sharp decreasing of the curve slope, probably due to an excess of the cells dimension of martensite compared to the austenite cells. 2) The role of austenite/martensite phases transforming is important on the stress-strain behaviour during the tensile test; 3) A simple stress-strain model has been proposed, able to predict the first three stages of the stress-strain curve, taking in to account the volume fraction of austenite and martensite. 4) The austenite-martensite transformation in the second stage is not linear, reaching 50% over the middle strain range of the second stage.

References Auricchio, F., Sacco, E., 1997. A One-Dimensional Model for Superelastic Shape Memory Alloys with Different Elastic Properties Between Austenite and Martensite. International Journal of Non-Linear Mechanics 32, 1101.

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Brotzu, A., Iacoviello, F., Di Cocco, V., Natali, S., 2018. Grain Size and Loading Conditions Influence on Fatigue Crack Propagation in a Cu-Zn Al Shape Memory Alloy. International Journal of Fatigue 115, 27. DOI: 10.1016/j.ijfatigue.2018.06.039. Di Cocco, V., Natali, S., 2018. A Simple Model to Calculate the Microstructure Evolution in a NiTi SMA. Frattura ed Integrita Strutturale 44, 173. DOI: 10.3221/IGF-ESIS.44.14 Eggeler, G., Hornbogen, E., Yawny, A., Heckmann, A., Wagner, M., 2004. Structural and Functional Fatigue of NiTi Shape Memory Alloys. Materials Science and Engineering A 378, 24. DOI: 10.1016/j.msea.2003.10.327 Furgiuele, F., Maletta, C., Sgambitterra, E., Casati, R., Tuissi, A., 2012. Fatigue of Pseudoelastic NiTi Within the Stress-Induced Transformation Regime: a Modified Coffin–Manson Approach. Smart Materials and Structures 21, 112001. DOI: 10.1088/0964-1726/21/11/112001 Iacoviello, F., Di Cocco, V., Maletta, C., Natali, S., 2014. Cyclic microstructural transitions and fracture micromechanisms in a near equiatomic NiTi alloy, International Journal of Fatigue 58, 136. DOI: 10.1016/j.ijfatigue.2013.03.009. Kang, G., Kan, Q., Yu, C., Song, D., Liu, Y., 2012. Whole-Life Transformation Ratchetting and Fatigue of Super-Elastic NiTi Alloy under Uniaxial Stress-Controlled Cyclic Loading. Materials Science and Engineering A 535, 228. DOI: 10.1016/j.msea.2011.12.071 Kollerov, M., Lukina, E., Gusev, D., Mason, P., Wagstaff, P., 2013. Impact of Material Structure on the Fatigue Behaviour of NiTi Leading to a Modified Coffin-Manson Equation. Materials Science and Engineering A 585, 356. DOI: 10.1016/j.msea.2013.07.072 Lagoudas, D.C., Miller, D.A., Rong, L., Kumar, P.K., 2009. Thermomechanical Fatigue of Shape Memory Alloys. Smart Materials and Structures 18, 085021. DOI: 10.1088/0964-1726/18/8/085021 Maletta, C., Niccoli, F., Sgambitterra, E., Furgiuele, F, 2017. Analysis of Fatigue Damage in Shape Memory Alloys by Nanoindentation, Materials Science & Engineering A 684, 335. DOI: 10.1016/j.msea.2016.12.003 Miyazaki, S., Imai, T., Igo, Y., Otsuka, K., 1986. Effect of Cycling Deformation on the Pseudoelasticity Characterisitc of Ni–Ti Alloys. Metallurgical Transactions A 17, 115. DOI: 10.1007/BF02644447 Natali, S., Di Cocco, V., Iacoviello, F., Volpe, V., 2014. Fatigue Crack Behavior on a Cu-Zn-Al SMA, Frattura ed Integrita Strutturale 8, 454. DOI: 10.3221/IGF-ESIS.30.55 Otsuka, K., Ren, X., 2005. Physical Metallurgy of TiNi-Based Shape Memory Alloys. Progress in Material Science 50, 511. DOI: 10.1016/j.pmatsci.2004.10.001 Sgambitterra, E., Maletta, C., Furgiuele, F., Casati, R., Tuissi, A, 2014. Fatigue Properties of a Pseudoelastic NiTi Alloy: Strain Ratcheting and Hysteresis under Cyclic Tensile Loading. International Journal of Fatigue 66, 78. DOI: 10.1016/j.ijfatigue.2014.03.011 Vantadori, S., Carpinteri, A., Di Cocco, V., Iacoviello, F., Natali, S., 2018. Fatigue Analysis of a Near-Equiatomic Pseudo-Elastic NiTi SMA. Theoretical and Applied Fracture Mechanics 94, 110. DOI: 10.1016/j.tafmec.2018.01.012

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25th International Conference on Fracture and Structural Integrity Acoustic Emission Entropy as a fracture-sensitive feature for real time assessment of metal plates under fatigue loading Danilo D’Angela * and Marianna Ercolino University of Greenwich, School of Engineering, Central Avenue, Chatham ME4 4TB, United Kingdom Abstract The paper presents the results of Acoustic Emission (AE) testing of fatigue fracture initiation and propagation in metal plates. The information Entropy of the AE data (i.e., AE Entropy ) is assessed as a fracture-sensitive feature for real-time assessment of metal plates under fatigue loading. Both Shannon and Kullback-Leibler formulations are found to be reliable for (a) detection of crack initiation/propagation, and (b) prediction of fracture failure. The reliability of the AE Entropy is also confirmed considering periodic monitoring (i.e., time-discontinuous detection/analysis of AE data), which is representative of structural health monitoring processes. The presented approach is promising for the application to real-time monitoring of metal structures undergoing fatigue loading such as bridges. 25th International Conference on Fracture and Structural Integrity Acoustic Emission Entropy as a fracture-sensitive feature for real time assessment of metal plates under fatigue loading Danilo D’Angela * and Marianna Ercolino University of Greenwich, School of Engineering, Central Avenue, Chatham ME4 4TB, United Kingdom Abstract The paper presents the results of Acoustic Emission (AE) testing of fatigue fracture initiation and propagation in metal plates. The information Entropy of the AE data (i.e., AE Entropy ) is assessed as a fracture-sensitive feature for real-time assessment of metal plates under fatigue loading. Both Shannon and Kullback-Leibler formulations are found to be reliable for (a) detection of crack initiation/propagation, and (b) prediction of fracture failure. The reliability of the AE Entropy is also confirmed considering periodic monitoring (i.e., time-discontinuous detection/analysis of AE data), which is representative of structural health monitoring processes. The presented approach is promising for the application to real-time monitoring of metal structures undergoing fatigue loading such as bridges.

© 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Gruppo Italiano Frattura (IGF) ExCo. © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Gruppo Italiano Frattura (IGF) ExCo. © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Gruppo Italiano Frattura (IGF) ExCo. Keywords: Acoustic Emission testing; Fatigue fracture; Information Entropy; metal plates; Keywords: Acoustic Emission testing; Fatigue fracture; Information Entropy; metal plates;

1. Introduction Acoustic Emission (AE) testing (Grosse and Ohtsu, 2008) is among the most innovative techniques for structural health monitoring by means of non-destructive evaluation and passive testing. AEs are elastic waves spontaneously generated by localised structural damage within solids. The acoustic waves propagate within the bodies interacting with the structural discontinuities. AEs are detected by sensors coupled to the boundaries of the monitored components (Fig. 1.a). According to the most common approach, i.e., parameter-based approach, the direct/indirect analysis of the features of the AE waveform (i.e., AE features, Fig. 1.b) allows identifying the damage evolution. 1. Introduction Acoustic Emission (AE) testing (Grosse and Ohtsu, 2008) is among the most innovative techniques for structural health monitoring by means of non-destructive evaluation and passive testing. AEs are elastic waves spontaneously generated by localised structural damage within solids. The acoustic waves propagate within the bodies interacting with the structural discontinuities. AEs are detected by sensors coupled to the boundaries of the monitored components (Fig. 1.a). According to the most common approach, i.e., parameter-based approach, the direct/indirect analysis of the features of the AE waveform (i.e., AE features, Fig. 1.b) allows identifying the damage evolution.

* Corresponding author. Tel.: +44 (0) 7447156365 ; E-mail address: d.dangela@greenwich.ac.uk * Correspon ing author. Tel.: +44 (0) 7447156365 ; E-mail address: d.dangela@greenwich.ac.uk

2452-3216 © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Gruppo Italiano Frattura (IGF) ExCo. 2452-3216 © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Gruppo Italiano Frattura (IGF) ExCo.

2452-3216  2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Gruppo Italiano Frattura (IGF) ExCo. 10.1016/j.prostr.2019.08.201

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Fig. 1. AE testing: (a) technique application scheme (MISTRAS Limited), and (b) main AE features (Ercolino et al., 2015).

AE testing has been widely applied in recent years, and several techniques have been developed for the analysis of the AE data. The data analysis techniques range from basic historical / comparison plots (Aggelis et al., 2011; Gostautas et al., 2005) and correlation analysis (Al-jumaili, 2016; Carpinteri et al., 2009), to multidimensional analysis and clustering (Al-jumaili, 2016; Ercolino et al., 2015). AE testing has been shown to be reliable for structural damage assessment in the case of laboratory testing and time-continuous monitoring (Al-jumaili, 2016; Carpinteri et al., 2009). However, when the testing conditions are not well-controlled (e.g., monitoring of in-service bridges) or in the case of time-discontinuous data detection, AE testing is not necessarily reliable and valid (Chai et al., 2018; Chen et al., 2017; Nair and Cai, 2010). Even if novel processing techniques have been recently developed for noise disturbance reduction, their features depend on the specific application, and field monitoring is often needed (Schultz, 2015). Furthermore, the intrinsic chaotic nature of the AE waves makes even more difficult the analysis and the interpretation of AE data (Kahirdeh et al., 2017a). Recent studies directly addressed the chaotic nature of the AE phenomena in order to improve the damage assessment by means of AE testing. This was motivated by the known phenomenological correlation between the structural damage and the systemic disorder , which can be quantified by the evaluation of the Entropy of the system (Amiri and Khonsari, 2011; Moreno-Gomez et al., 2018). Entropy is an extensive property of a thermodynamic system, and its concept is used in statistical mechanics and information theory to assess the evolution of systems having a large number of degrees of freedom. One of the first formulations of the information Entropy is due to Shannon (1948); Shannon Entropy defines the measure of uncertainty contained within a random variable, or equivalently, the amount of information that a variable contains. Shannon Entropy and thermodynamic Entropy are not theoretically correlated; however, their applied formulations are equivalent. Shannon Entropy of the AE waves (i.e., AE Entropy or acoustic Entropy ) was found to be promising for structural damage assessment, but it is still far from the application to (a) real-time structural health monitoring, and (b) time-discontinuous data detection/analysis (Kahirdeh et al., 2017a, 2017b; Stavrakas et al., 2016). The paper presents AE testing of fatigue fracture in metal plates performed according to the parameter-based approach (Grosse and Ohtsu, 2008). The detected AE data were post-processed and filtered using the latest literature techniques (Ercolino et al., 2015; Yu et al., 2011). The information Entropy of the AE data ( AE Entropy ) was evaluated according to both Shannon (1948) and Kullback-Leibler (1951) formulations. The experimental crack initiation, crack propagation, and fracture failure were correlated to the AE Entropy evolution. Damage criteria based on AE Entropy were also identified for time-discontinuous data detection, simulating realistic structural health monitoring of fracture critical components of bridges. 2. Experimental testing and AE analysis Acoustic Emission (AE) testing of fatigue crack initiation and propagation was performed on Compact Tension (CT) specimens (D’Angela and Ercolino, 2018) made of structural metals. The testing set-up is shown in Fig. 2.a, and the geometry of the testes samples is shown in Fig. 2.b/c.

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Fig. 2. Testing features: (a) testing set-up, (b) CT specimen geometry (Table 1), and (c) notch detail. The dimensions are in mm.

The mechanical testing was carried out using a servo-hydraulic digitally controlled actuator; AE testing was performed using a single-channel USB AE system (software AEwin™ ); a 300 kHz-resonant ultra-low noise pre amplified sensor ( PKI30 ) was used to detect the AE data. The AE equipment was produced by MISTRAS Limited. The superior end of the fatigue machine was fixed, and the inferior one was moved by the actuator (Fig. 2.a); the AE sensor was coupled to the fixed end of the fatigue machine by a thin layer of silicone glue. The testing plan is reported in Table 1. Four CT samples were tested (Fig 2.b and Table 1): the samples A 1 and A 2 ( S 1 and S 2 ) were made of Aluminum alloy 7075-T651 (Steel S355J0). Two mock tests were carried out prior to the definite tests using similar samples; these were aimed to (a) check the reliability of the whole testing equipment, and (b) calibrate the AE testing parameters. Accordingly, the AE amplitude threshold was set equal to 45dB. The crack initiation was detected during the testing by means of dye penetrant inspection (European Committee for Standardization, 2000); the failure was defined by the complete fracture of the sample (clearly observable).

Table 1. Testing plan. ID test Material

W [mm] H [mm] N [mm] B [mm] P [kN] R [-] f [Hz]

Al 7075-T6 71 Al 7075-T6 63

86 77 77 77

32 29 29 29

4

2.5

0.05 0.05 0.05

10 10 10

A 1 A 2 S 1 S 2

10 10 10

5 5 6

S355 S355

63 63

0.05 10

The AE signals were filtered using the modified Swansong II filtering technique (Ercolino et al., 2015) in order to remove (a) long-duration Hits with low Amplitude, and (b) short-duration Hits with high Amplitude, which are typical features of non-genuine AE signals. The filtering limit values were derived by Yu et al. (2011), who performed similar tests. The traditional AE analysis was preliminary performed considering both historical/comparison plots and correlation analysis (i.e., b-value analysis ). The information Entropy of the AE data was performed according to both Shannon (1948), and Kullback- Leibler (1951) formulations. The former Entropy is referred as Shannon Entropy H S (Equation 1), and the latter is referred as relative Entropy H R (Equation 2). Both Entropies are defined by the probability mass distribution vector p i (Equation 3), which can be evaluated considering the ratio between the number of Counts n i and the cumulative number of Counts c i . � � �∑ � � � � � � ��� (1) � � �∑ � � � � � � ��� � � ��� (2) � � � � � � � ; � � � � ; … ; � � � � � (3)

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3. Results and discussion The results related to the traditional AE analysis are not reported in the paper for the sake of brevity. However, the traditional analysis is confirmed as qualitative and inefficient for reliable fracture damage assessment. Historical and comparison plots exhibit qualitative damage correlations in some cases; they are not robust enough for real-time monitoring. The b-value analysis does not show clear and univocal damage correlations; in only two cases out of four, b-value is correlated to the fracture damage (i.e., it decreases along with the cracking process). The traditional AE analysis methods do not address the chaotic nature of the AE phenomena, as well as the mechanical noise disturbance. Therefore, the evaluation of acoustic Entropy is even more motivated. Both logarithmic cumulative Shannon Entropy ( H S ) and relative Entropy ( H R ) are plotted in Fig. 3 for all considered cases; the times related to crack initiation/propagation and failure are also shown. The use of the logarithmic scale was already found to be effective for the representation of the cumulative AE Entropy: this allows to identify better the potential damage correlations (Kahirdeh et al., 2017b). Fig. 3 shows that Shannon Entropy curves related to the different cases have a very similar pattern: (1) short-duration sub-vertical-tangent branch, (2) smooth knee , (3) decreasing-tangent branch, and (4) long-duration sub-horizontal-tangent branch ( plateau ). The relative Entropy curves also have a similar trend (1) short-duration sub-vertical-tangent branch, (2) abrupt knee, and (3) long-duration sub horizontal-tangent branch (plateau); the A 1 case also presents a more irregular increasing branch just before the plateau occurring, as well as it shows an increasing stage after the plateau. Overall, the Shannon and relative Entropy curves present a very similar trend.

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Fig. 3. Logarithmic cumulative Shannon Entropy (log Σ H S ) and relative Entropy (log Σ H R ) for (a) A 1 , (b) A 2 , (c) S 1 , and (d) S 2 cases. The crack initiation and fracture failure times are shown, as well as the crack propagation range is shaded.

574 Danilo D’ Angela et al. / Procedia Structural Integrity 18 (2019) 570–576 Danilo D’Angela et al. / Structural Integrity Procedia 00 (2019) 000–000 5 Fig. 4 shows the boxplots of the Entropy values corresponding to the failure for both Shannon and relative Entropy formulations: (a) cumulative Entropy at the failure ( ) over the median value ( � ), and (b) logarithmic cumulative Entropy at the failure ( ). The dispersion of the relative Entropy values at the failure is significantly smaller than the Shannon Entropy one, considering both over � and . Damage correlations are identified considering both Shannon and relative Entropy curves. Crack initiation is correlated to the transition between stage (2) and (3) for the Shannon Entropy, and to the initiation of the stage (3) for the relative Entropy (Fig. 3). Crack propagation is associated with the stage (3) for both Shannon Entropy and relative Entropy (Fig. 3). Fracture failure corresponds to the occurring of the stage (4) for the Shannon Entropy (Fig. 3), and to a threshold value for the relative Entropy (Fig. 4). The damage correlations related to the Shannon Entropy are more qualitative than the ones related to the relative Entropy; however, the former can be reasonably considered as more robust and consistent. As a matter of fact, the trend of the Shannon Entropy curves is more regular and clearly sub staged, as well as it is less affected by the testing/sample conditions. Furthermore, damage criteria based on more gradual response (e.g., smooth knee) are easier to be assessed by real-time monitoring. The cumulative Shannon Entropy is evaluated considering several sets of random monitoring processes , which are more representative of real structural health monitoring processes. A random monitoring process consists of randomly selected sequential detection windows , during which AE data are recorded and the Entropy is assessed. A set of random processes can be defined by (a) the number of detection windows ( N DW ) over the structural lifetime ( L t ), (b) the time duration of the detection windows ( T DW ), and (c) a set of rules for the random selection of the detection windows. T DW should be significantly smaller than L t in order to be consistent with realistic monitoring. The minimum time interval between the end of the previous detection windows and the beginning of the following ( Δ T ) is the main rule for the random selection of the detection windows. Fig. 5 shows the logarithmic cumulative Shannon Entropy (log Σ H S ) evaluated according to the abovementioned procedure applied to the A 2 case. A number of 500 curves is considered for each set of values of N DW . In particular, five sets of N DW are considered (ranging from 10 to 100), T DW is assumed equal to 0.005 L t , and Δ T is assumed equal to 10 T DW . The global Entropy curve is also shown in Fig. 5. The trend of the Entropy curves is proven to not be affected by (a) time-discontinuous detection, (b) random selection of the detection windows, and (b) variation of N DW . The increase of N DW (with the consequent increase of total monitored time) translates the curves towards the global Entropy curve without affecting the relative shape and trend of the curves. The sets of random curves have the same trend of the global curve, even if a much-reduced total monitored time is considered (e.g., smaller than 0.1 L t ). The damage criteria verified for the time-continuous global curve (e.g., curve knee and plateau in Fig. 3) are also valid in the considered conditions (Fig. 4), which simulate more realistic structural health monitoring processes.

(a) (b) Fig. 4. Boxplots of failure Entropy values for both Shannon and relative Entropy formulations: (a) cumulative Entropy at the failure ( ���� ) over the median value ( � ���� ), and (b) logarithmic cumulative Entropy at the failure ( ���� ).

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Fig. 5. Logarithmic cumulative Shannon Entropy (log Σ H S ) over time-discontinuous data detection related to the A 2 case: five sets of 500 random process curves varying the number of detection windows ( N DW ) and global Entropy curve. 4. Conclusions The results of AE testing of fatigue fracture in metal plates were presented in the paper. The information Entropy of the AE data (AE Entropy) was experimentally investigated as a fracture-sensitive feature for real-time monitoring of metal plates under fatigue fracture. Both Shannon and Kullback-Leibler (i.e., relative Entropy) formulations were considered for the computation of the AE Entropy. Crack initiation and fracture propagation were found to be clearly correlated to the evolution of both Shannon and relative cumulative Entropy. The analysis of the AE Entropy allowed the prediction of the fracture failure. The reliability of the AE Entropy was also checked considering time discontinuous data detection, which is typical of realistic monitoring of structures. Both time-discontinuous detection and reduced total monitored time did not affect the trend of the Entropy evolution. The findings strengthened the robustness of the presented approach. Even if the evaluation of the AE Entropy resulted promising for the application to real monitoring of structures, further studies are necessary to strengthen the experimental damage criteria as well as to define innovative monitoring protocols. Acknowledgements The project was supported by REF funding (2016/2017 and 2017/2018) awarded by Dr Marianna Ercolino. References Aggelis, D.G., Kordatos, E.Z., Matikas, T.E., 2011. Acoustic emission for fatigue damage characterization in metal plates. Mechanics Research Communications 38, 106–110. https://doi.org/10.1016/j.mechrescom.2011.01.011 Al-jumaili, S.K.J., 2016. Damage Assessment In Complex Structures Using Acoustic Emission. Amiri, M., Khonsari, M.M., 2011. On the Role of Entropy Generation in Processes Involving Fatigue. Entropy 14, 24–31. https://doi.org/10.3390/e14010024 Carpinteri, A., Lacidogna, G., Puzzi, S., 2009. From criticality to final collapse: Evolution of the “b-value” from 1.5 to 1.0. Chaos, Solitons & Fractals 41, 843–853. https://doi.org/10.1016/j.chaos.2008.04.010 Chai, M., Zhang, Z., Duan, Q., 2018. A new qualitative acoustic emission parameter based on Shannon’s entropy for damage monitoring. Mechanical Systems and Signal Processing 100, 617–629. https://doi.org/10.1016/j.ymssp.2017.08.007 Chen, Z., Zhou, X., Wang, X., Dong, L., Qian, Y., 2017. Deployment of a Smart Structural Health Monitoring System for Long-Span Arch Bridges: A Review and a Case Study. Sensors 17, 2151. https://doi.org/10.3390/s17092151 D’Angela, D., Ercolino, M., 2018. Finite Element Analysis of Fatigue Response of Nickel Steel Compact Tension Samples using ABAQUS. Procedia Structural Integrity 13, 939–946. https://doi.org/10.1016/j.prostr.2018.12.176 Ercolino, M., Farhidzadeh, A., Salamone, S., Magliulo, G., 2015. Detection of initiation of failure in prestressed strands by cluster analysis of acoustic emissions. Structural Monitoring and Maintenance 2, 339–355. https://doi.org/10.12989/smm.2015.2.4.339 European Committee for Standardization, 2000. EN 2002-16. Aerospace series - Metallic materials; test methods - Part 16: Non-destructive testing, penetrant testing.