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Micro-mechanical damage in tool steels analyzed by acoustic emission technique.

Introduction

Understanding the fracture events of tools in the manufacturing process is crucial to foresee tool lifetime and develop tool steels with improved mechanical performance [1]. Failure detection helps in predicting premature failure of the tool, which directly affects the price of the manufactured part. The interaction between the two main constituents of the tool steel microstructures: the primary carbides and the metallic matrix, determines their mechanical properties and the performance of tools.

Carbides play an import role in the mechanical response of these steels, since they act as hard particles and dictate the wear resistance. The origin of fracture and fatigue cracking of cast tool steels is usually associated with the primary carbides, which break under the applied stress and act as initiation sites [2]. However, there is a lack of knowledge about the mechanical behavior of carbides in tool steels, mainly due to the experimental difficulties associated with its measurement [3]. With conventional non-destructive testing methods, it is difficult to identify the moment when carbides begin to break and even more difficult to identify when the crack starts to propagate in the metal matrix because it is necessary to stop the test and inspect the trial.

The acoustic emission (AE) technique "listens" what is happening within the material during the test; because the AE is based on the phenomenon of transient elastic-waves generated due to a rapid release of strain energy caused by a structural alteration in the solid material. So, it serves as a powerful method to monitor progressive damage accumulation in different materials [4-7].

Only scarce data exist in the literature concerning the application of AE for the analysis of micro-damage of tool steels. Fukaura et al. [8, 9] and Yokoi et al. [10] are amongst the few authors, who employed this technique to determine the progression of internal damage on tool steels. These authors successfully detected AE signals from carbide cracking; the signals started at a certain applied load and the event rates continually increased until reaching the fracture stress. No continuous AE signals existed (related with plastic deformation processes), but that numerous burst emissions at close intervals were recorded instead. Yamada and Wakayama [11] observed a rapid increase in cumulative AE energy prior to the final fracture and attributed this phenomenon to the main crack formation. They also distinguished two types of AE signal: one was a burst-type signal with high frequency and the other was a low frequency and continuous-type signal. The former was considered to be emitted from micro-cracking while the latter was due to plastic deformation of the binder phase.

[FIGURE 1 OMITTED]

In a previous work, Martinez et al. [12] applied AE in a three-point bending test. Three different zones were distinguished during a bending test in a tool steel sample with regard to the AE events, and the stress level at which cracking carbides start was also found (Fig. 1). This work dealt with real time detection of elastic waves signals generated during a monotonic mechanical test, a three-point bending test, in case of a tool steel named DIN 1.2379. The main aim is to cluster the recorded signals during the test, based on the spectral characteristics, and correlate the classification with the failure mechanism that generates the reference waveform, identified by microstructural inspection of the specimen.

Experimental Procedure

The material studied was a conventional ledeburitic high-carbon, high-chromium tool steel named DIN 1.2379 (AISI D2) obtained by ingot metallurgy routes. The main alloying elements found in their chemical composition are shown in Table 1.

Prismatic samples were machined from forged and annealed commercial bars parallel to the forging direction. Heat treatment was applied to the sample material in order to get a hardness level of 60-62 HRC, as summarized in Table 2. The bending strength, [[sigma].sup.R] , was reported by Picas et al. [1], and the fracture toughness, [K.sub.IC], was determined as specified in the ASTM E 399-90 standard. These values are also shown in Table 2.

Fracture tests were performed by means of three-point bending tests with a constant span length of 40 mm. Samples dimensions were 8 mm x 6 mm x 120 mm. Samples were mechanically ground and their corners were rounded to avoid stress magnifications and remove any defect introduced during sample preparation. Faces subject to tensile stress during three-point bending tests were carefully polished to mirror-like finish using colloidal silica particles with approximately 40 nm sizes.

Microstructural inspection of the samples was carried out using a FE-SEM (Field Emission Scanning Electron Microscope) and the fracture tests were performed in a universal testing machine using an articulated fixture to minimize torsion effects. The applied displacement rate was 0.01 mm/min.

The test was monitored using sensors of a fixed resonance frequency of 700 kHz (VS700D, Vallen System GmbH). Three pre-amplifiers with a 34 dB gain of the same brand were also used (AEP4). Acoustic Emission (AE) signals were recorded and analyzed using the Vallen Systeme GmbH AMSY5 analyzer. The experimental set-up is schematized in Fig. 2.

[FIGURE 2 OMITTED]

During the measurements, digital filters of 95-850 kHz were applied. In order to avoid the multiple reflections due the small dimensions of the specimen, only the first counts of each hit were analyzed as purely representative of micro-damage phenomena.

Using this set-up configuration, 3 to 5 samples of each material were monitored until final fracture. Later, 2 to 3 samples were analyzed by means of stepwise loading (Fig. 3a)) in order to relate each type of AE characteristic signal pattern to the generated damage in the microstructure. Surface inspection of samples was carried out after each load increment in a Confocal Microscope (CM) (Fig. 3b)).

[FIGURE 3 OMITTED]

Results and Discussion

A. Microstructural Analysis.

In Fig. 4 the microstructure of the studied steel can be observed. The microstructure was markedly anisotropic, with large carbide stringers forming bands in the metallic matrix. The primary carbides of this steel were rather large and had irregular morphologies.

[FIGURE 4 OMITTED]

B.1. Identification of Characteristic AE Signal Patterns in Bending Tests of 1.2379 under Monotonic Loading.

Figure 5a) shows the results of the AE signals registered in bending tests under monotonic loading for 1.2379. This diagram plots the cumulative number of hits as a function of the stress applied and the location of each signal on the sample surface (with respect to the center of the sample). As it can be observed, the highest amount of signals was generated at the center of the sample (X-Loc. = 0 in Fig. 5a), where the applied stress was the highest during the three-point bending test, and the quantity of emitted signal continuously increased with the applied stress. The Y axis refers to the applied stress and the Z axis to the cumulated number of hits registered).

[FIGURE 5 OMITTED]

A closer look to the AE signals obtained allowed us to classify them into two categories depending on the frequency spectrum. As shown in Fig. 5b), at the beginning of the test, no AE signals were detected. At a certain applied stress level, a first type of AE signal started to be recorded (green line in Fig. 5b)). These signals were not continuous but they were emitted in a burst-like manner, and the quantity of hits registered increased along with the applied stress. Later as the stress increased, a second type of signal was distinguished (red line in Fig. 5b)). This signal also increased in number of hits together with the applied stress, but at the moment of final fracture it attained lower hit values than the first signal type.

These two signals identified not only differed because of the number of hits, but also they had very different characteristic frequencies and waveforms. As shown in Fig. 5c), the first type of signal had a main frequency of 280 kHz, while the frequency of the second type was around 650 kHz. These different frequency ranges of the two signals indicated that the responsible mechanisms for emitting them took place at different velocities in the microstructure, i.e. the second mechanism would be much faster than the first one.

B.2. Relationship between AE Signals and Micro-Damage during Bending Tests of 1.2379 a under Monotonic Loading.

Stepwise bending tests permitted to inspect the tensile surface of the samples at different increasing stress levels, and correlate the registered AE data (namely the two different identified signal types) to the micro-damage observed in the microstructure.

In Fig. 6a) the cumulative number of hits as a function of the stress applied at the first load step can be observed. This test was stopped at 800 MPa, when the first signals were detected. These signals indicated the same pattern as those of type 1 identified before. However, no damage could be observed at the sample surface, as shown in Fig. 7a); it is likely that something happened at the microstructure but it could not be detected yet by microscopy.

The next test was stopped at 2200 MPa, when a higher quantity of AE signal was detected. Practically all signals responded to the characteristics of the type 1 identified before, and a small number of hits of characteristic type 2 signals were first detected (Fig. 6b)). In this case, the first cracks were observed in the microstructure and they were located at primary carbides (Fig. 6b) and c)). However, despite many carbides were broken, none of the cracks nucleated from them were observed to have started propagating through the metallic matrix surrounding the broken carbide. The last load step at 2600 MPa revealed a notable increase of the type 2 signal, even though the number of hits of the type 1 had not ceased to increase (Fig. 6c)), as well as the number of broken carbides in the sample. The inspection of the surface permitted to observe that some cracks had now propagated through the metallic matrix (Fig. 7d)).

[FIGURE 6 OMITTED]

[FIGURE 7 OMITTED]

As it followed from the obtained results, the first and the second AE signal types were related to different damage mechanisms occurring in 1.2379 samples as the applied stress increased. The first type of signal corresponded to the breakage of carbides in the microstructure, i.e. the nucleation of cracks, while the second type was emitted by the subsequent propagation of these cracks through the metallic matrix.

The data shown above demonstrate that the AE technique, coupled to the bending tests under monotonic conditions, was able to provide accurate information regarding the acting micromechanical and damaging mechanisms of 1.2379. In this case, the nucleation and propagation of cracks in the microstructure was well identified by means of two different types of AE signals reporting respectively, the breakage of carbides in the microstructure, i.e. the nucleation of cracks, and the moment when these cracks left the carbide and grew through the metallic matrix, i.e., the propagation of cracks. Therefore, this technique provided a unique and very accurate tool to determine the threshold stresses, at which carbides started to break and the stresses at which the first cracks were nucleated.

Summary

The method developed in this study by which bending fracture tests were coupled to AE techniques provided helpful results to understand in great detail the failure mechanisms of tool steels under such applied loading, as well as the interaction of their microstructural constituents. In the 1.2379 steel studied, two different AE signal wave patterns were identified during a fracture test. These signals were respectively assigned to crack nucleation in primary carbides, and the propagation of these through the metallic matrix.

Acknowledgements

Authors from Fundacio CTM Centre Tecnologic acknowledge the Catalan government for partially funding this work under grant TECCTA11-1-0006.

References

[1] Picas, I., Hernandez, R., Casellas, D., Valls, I.: 2010; Strategies to increase the tool performance in punching operations of UHSS; 50th IDDRG Conference (50th International DeepDrawing Research Group), Graz, Austria; June 2010; p.325.

[2] Picas I., Cuadrado N., Casellas D., Goez A., Llanes L.; 2010; Microstructural effects on the fatigue crack nucleation in cold work tool steels. Proc Eng 2:1777-1785.

[3] Casellas D., Caro J., Molas S., Prado J.M., Valls I.; 2007; Fracture toughness of carbides in tool steels evaluated by nanoindentation. Acta Mater 55:4277-4286

[4] Waller J.M., Saulsberry R.L., Andrade E.; 2009; Use of acoustic emission to monitor progressive damage accumulation in Kevlar 49 composites. AIP Conf Proc 1211:1111-1118.

[5] Lugo M., Jordon J.B., Horstemeyer M.F., Tschopp M.A., Harris J., Gokhalen A.M.: 2011; Quantification of damage evolution in a 7075 aluminium alloy using an acoustic emission technique. Mat Sci A 528:6708-6714

[6] Pollock A.A.; 2007; Some observations on acoustic emission-stress--time relationships. In: Carpinteri A, Lacidogna RG (ed) Acoustic emission and critical phenomena: from structural mechanics to geophysics. Boca Raton, London: CRC Press/Taylor&Francis, ISBN:9780415450829

[7] Maillet E., Godin N., R'Mili M., Reynaud P., Lamon J., Fantozzi G.; 2012; Analysis of acoustic emission energy release during static fatigue test at intermediate temperatures on ceramic matrix composites: towards rupture time prediction. Compos Sci Technol 72:1001-1007.

[8] Fukaura, K., Yokoyama, Y., Yokoi, D., Tsujii, N., Ono, K.; 2004; Fatigue of cold-work tool steels: effect of heat treatment and carbide morphology on fatigue life formation, life and fracture surface observations; Metallurgical and materials transactions A; vol.35A, April 2004, pp. 1289-1300.

[9] Fukaura K. and Ono, K.; 2001; Acoustic emission analysis of carbide cracking in tool steels, Journal of Acoustic Emission (JAE); vol.19, pp. 91-99.

[10] Yokoi D., Tsujii N., Fukaura F.; 2003; Effects of tempering temperatures and stress amplitude on low-cycle fatigue behavior of a cold work tool steel. Mater Sci Res Int. 9: 216-222.

[11] Yamada, K. and Wakayama, S.; 2009; AE Monitoring of microdamage during flexural fracture of cermets; In Proceedings International Congress and Exhibition EURO PM2009, Copenhagen, Denmark; October 2009; p. 247-252.

[12] Martinez-Gonzalez, E.; Picas, I.; Casellas, D.; Romeu, J.; 2010; Analysis of fracture resistance of tool steels by means of acoustic emission; Journal of acoustic emission (JAE); vol.28; pp.163-169.

Ingrid Picas (1), Eva Martinez-Gonzalez (2), Daniel Casellas (1) and Jordi Romeu (2)

(1) Fundacio CTM Centre Tecnologic, Av. Bases de Manresa 1, 08242 Manresa, Spain

(2) Laboratori d'Enginyeria Acustica i Mecanica (LEAM), Departament d'Enginyeria Mecanica (DEM)m Universitat Politecnica de Catalunya (UPC), EUETIB, Urgell 187, 08036 Barcelona, Spain
Table 1. Main alloying elements in the chemical composition of DIN
1.2379 (in wt %).

Steel       C         Cr         Mo      W        V

1.2379   1.5-1.6   11.0-12.0   0.6-0.8   --   0.9-1.0

Table 2. Heat treatment and obtained hardness and bending strength.

Steel         Austenitizing                 Tempering
               (oil quench)

1.2379   1050[degrees]C for 30 mm   550[degrees]C for 2 h (x2)

Steel     HRC    [[sigma].sup.R]        [K.sub.IC]
                    [MPa] [1]       [Mpa x [m.sup.1/2]]

1.2379   60-62   2847 [+ or -] 96            28
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Author:Picas, Ingrid; Martinez-Gonzalez, Eva; Casellas, Daniel; Romeu, Jordi
Publication:Journal of Acoustic Emission
Date:Jan 1, 2012
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