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Neutron texture analysis on GEM at ISIS
ARTICLE IN PRESS
Physica B 385–386 (2006) 639–643 www.elsevier.com/locate/physb
Neutron texture analysis on GEM at ISIS W. Kockelmann, L.C. Chapon, P.G. Radaelli ISIS facility, Rutherford Appleton Laboratory, Didcot, Chilton, Oxfordshire, OX11 0QX, UK
Abstract Texture analysis by time-of-flight neutron diffraction, carried out on a multidetector instrument, requires just a few sample orientations. Moreover, on a diffractometer like GEM at ISIS with sufficiently high detector coverage a quantitative bulk texture analysis can be performed even on a stationary sample in a matter of minutes. A ‘single-shot’, rapid texture measurement on a highcount-rate instrument can be of considerable benefit for studying materials at non-ambient conditions or for bulky samples that cannot be mounted on a goniometer. The capabilities of GEM for texture analysis are demonstrated with results on copper texture standards, which were studied to accompany diffraction analyses of archaeological objects. r 2006 Elsevier B.V. All rights reserved. PACS: 61.12.Ex; 81.40.Ef; 61.12. q Keywords: TOF neutron diffraction; Texture analysis; Archaeometry
1. Introduction Neutron diffraction is a well-established tool in engineering and geological sciences for quantitative analysis of the crystallographic texture of a material, which is related to geological and mechanical deformation processes. Low absorption of neutrons is of considerable advantage for studying big, coarse-grained geological samples but also for testing intact and unique museum objects for which sampling is unacceptable. Texture is measured by recording diffraction patterns as a function of the scattering angle, either by (i) rotating the sample on a goniometer and/or by (ii) using a multi-detector surrounding the sample. The amount of texture information is roughly given by the product of the number of pole figures (h k l) times the number of sample orientations [1]. Hence, by using a polychromatic beam and with many detectors at fixed scattering angles, time-of-flight (TOF) neutron diffraction has some considerable advantages for texture analysis since significant portions of both reciprocal space and orientation space are simultaneously covered in one
Corresponding author. Tel.: +44 1235 446731; fax: +44 1235 445720.
E-mail address: [email protected] (W. Kockelmann). 0921-4526/$ - see front matter r 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.physb.2006.06.091
measurement. The capabilities and benefits of extracting the orientation distribution function (ODF) of a material from TOF data is well documented both from the experimental and the data treatment point of view. TOF texture analysis with a multidetector set-up was previously demonstrated at the pulsed source IBR-2, Russia [2] and at the pulsed spallation source LANSCE, USA [3]. Moreover, in the past few years Rietveld codes such as MAUD [4] and GSAS [5] have been developed to determine texture coefficients from TOF data. Texture measurements increasingly take advantage of this development in crystallography and Rietveld analysis, allowing texture information from complex low symmetry and polyphase materials to be extracted. By increasing the detector coverage on a TOF diffractometer fewer sample rotations are required for quantitative texture analysis, as was demonstrated on HIPPO at LANCSE [3,6] with typically 4–8 sample orientations and total collection times in the order of 20 min. Here we show that on GEM at ISIS, quantitative texture information can be obtained even faster from a single measurement without any sample rotations. Results on copper reference samples that were measured to calibrate the diffraction results of complex archaeological metal objects are presented to assess the texture analysis capabilities of GEM.
ARTICLE IN PRESS 640
W. Kockelmann et al. / Physica B 385–386 (2006) 639–643
2. Experimental details GEM is a high-count-rate materials diffractometer designed to study the structures of both crystalline and liquid and amorphous samples [7]. GEM is equipped with 6 detector banks, housing a total of about 7000 individual detector elements with banks 1 and 6 covering forward and backscattering angles, respectively (Fig. 1(a)). One detector element is typically about 5 200 mm2 in size, corresponding to an angular coverage of about 0.21 101 for a typical sample–detector distance at 901. The total detector coverage of GEM exceeds 4 sr, and hence considerable coverage in orientation space is provided. For the texture analysis 164 separate detector groups were generated, with each group covering approximately 101 101. The corresponding pole figure coverage for a single sample orientation (the
‘zero position’) is shown in Fig. 1(b), with the backscattering bank-6 detector groups in the pole figure centre. It has to be kept in mind that a symbol in the pole figure in Fig. 1 represents the angular location of a group of detectors. Hence one group records a range of sample orientations over which intensities are averaged, thus affecting the achievable angular texture resolution. Figs. 1(c) and (d) show the pole figure coverages for texture analyses with two sample orientations, with rotations about a vertical (601) and horizontal (901) axis, respectively. We have collected data from several copper and bronze specimens in order to assess the capabilities of GEM for texture analysis. Copper samples were made and mechanically treated, i.e. texturized, in different ways in order to mimic specific production and working processes of
Fig. 1. (a) GEM detector arrays and corresponding pole figure coverage, (b) for a single sample orientation, (c) for a second sample orientation with a 601 rotation about the vertical diffractometer axis, and (d) for a second sample orientatation with a 901 rotation about the horizontal incoming beam direction. The primary beam direction is in the centre of the pole figures. For each bank, the angular range and the product of texture groups times the accessible number of Cu (h k l) is given.
ARTICLE IN PRESS W. Kockelmann et al. / Physica B 385–386 (2006) 639–643
archaeological metals. Here we present data from two diskshaped copper samples (34 mm diameter, 2 mm thickness), a cold-rolled sample (sample 1), and a cold-rolled sample which was subsequently uniaxially compressed to 20% of its original thickness (sample 2). The beam size for the texture measurements was 20 20 mm2; hence the illuminated volume was 800 mm3. Data sets were collected for three separate sample orientations (as indicated in Fig. 1(b)–(d)), for the zero position, a 601 rotation about a vertical axis, and a 901 rotation about a horizontal axis, by remounting the aluminium sample holder on the sample stick. Data sets were collected as a function of time between 2 and 30 min. The data were normalized to the incident neutron flux distribution, corrected for detector efficiencies, and converted into 164 d-spacing patterns for each sample orientation corresponding to the texture detector grouping. 164 and 328 diffraction patterns were simultaneously Rietveld fitted in MAUD for one or two sample orientations, respectively, corresponding to pole figures coverages in Fig. 1(b)–(d). Values of the ODF cells were extracted using the extended WIMV (E-WIMV) algorithm [4] as implemented in MAUD, which can handle an incomplete and highly irregular pole figure coverage, as the one shown in Fig. 1(b). In order to account for sample absorption anisotropy due to the sample shape, one scale factors for
641
each diffraction pattern was refined. (1 1 1), (2 0 0), and (2 2 0) pole figures, which were reconstructed with MAUD from the ODF, are shown in equal area projection in Figs. 2 and 3. TOF texture analysis benefits from the simultaneous coverage of d-spacing range, i.e., number of pole figures, and of scattering angles. It is obvious that the forwardscattering banks have less weight in the quantitative texture analysis than the high-angle banks. The ‘texture information’ index, defined as a product of number of groups times number of observed (h k l), varies between banks as indicated in Fig. 1(a), ranging from 12 to 700 between banks 1 and 5, respectively, for the case of copper and a minimum d spacing of 0.5 A˚. It could be noted that an extension of GEM detector bank 5 which is just being commissioned will provide additional orientation coverage in the gap towards bank 6 (Fig. 1(b)). 3. Results and discussion Fig. 2 shows reconstructed (1 1 1), (2 0 0) and (2 2 0) pole figures of the cold-rolled sample 1 for the three setting of Fig. 1(b)–(d) with data collection times of 2 min per orientation. The pole figures showing the typical hallmarks of a rolling texture are very similar for the three types of coverage models. Maximum multiples of a random
Fig. 2. Pole figures of sample 1 for a single orientation according to Fig. 1(b) (top), 2 sample orientations accoring to Fig. 1(c) (middle) and 2 sample orientation according to Fig. 1(d) (bottom). Sample normal in the pole figure centre. Rolling direction in the vertical.
ARTICLE IN PRESS W. Kockelmann et al. / Physica B 385–386 (2006) 639–643
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Fig. 3. Pole figures of sample 2 for collection times of 2 (top), 8 (middle) and 30 min (bottom). Table 1 Details of the pole figure determination for copper samples 1 and 2, Number of orientations, collection time per orientation, maximum pole density, texture index F2 and texture agreement index RB Type of coverage
# sample angles
Sample 1, cold rolled Fig. 1(b) 1 Fig. 1(c) 2 Fig. 1(d) 2 Sample 2, cold rolled+uniaxially compressed Fig. 1(b) 1 Fig. 1(b) 1 Fig. 1(b) 1
Time min.
Max. m.r.d.
F2
RB %
2 2 2
2.14 2.39 2.18
2.63 2.55 2.31
9.5 9.8 10.4
2 8 30
3.41 3.57 3.52
3.53 3.55 3.56
10.0 9.5 9.5
distribution (m.r.d.) values and texture indices F2 [1] are in reasonable agreement (Table 1). It can be concluded that the texture can be determined from a single orientation in the present study. In other cases, for bulky and irregularly shaped copper objects, convergence of refinements was improved with an extra sample orientation, probably due to the presence of strong directional absorption. Coverage models as displayed in Fig. 1(c), (d) may also prove useful and necessary for low crystal symmetries. Fig. 3 compares copper pole figures of the rolled and subsequently compressed sample 2 for collections times of 2, 8 and 30 min. The pole figures clearly show the effect of the equiaxial compression in terms of the texture index (Table 1) and texture type. The pole densities follow, to a first approximation, a (1 1 0) fibre texture but poles are not equally distributed due to the initial rolling texture.
Textural features and texture indices do not change significantly after 2 min collection time. The results on the Cu reference samples suggest that pole figures can be reliably obtained on GEM in a ‘single shot’ in a matter of minutes without any sample reorientations. The angular coverage of 101 101 per element proved to be adequate for the slow varying textures of the samples studied here while for sharper textures the angular detector coverage per group can be reduced by software. Stationary and rapid texture analyses have a particular advantage, for example, for in situ growth studies at non-ambient conditions. Bulky engineering and archaeological objects can be non-destructively analysed in terms of phase composition, microstructure parameters and texture, whilst keeping the radio activation to a minimum.
ARTICLE IN PRESS W. Kockelmann et al. / Physica B 385–386 (2006) 639–643
Acknowledgements Support by L. Lutterotti, University of Trento, Italy, with the MAUD program is gratefully acknowledged. We would like to thank H. Phiesel, Bonn University, Germany, for providing the reference samples. References [1] H.-R. Wenk, P. Van Houtte, Rep. Prog. Phys. 67 (2004) 1367.
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[2] K. Feldmann, M. Betzl, W. Kleinsteuber, K. Walther, Textures Microstruct. 14–18 (1991) 59. [3] H.-R. Wenk, L. Lutterotti, S. Vogel, Nucl. Instr. and Meth. A 515 (2003) 575. [4] R.B. von Dreele, J. Appl. Crystallogr. 30 (1997) 517. [5] L. Lutterotti, S. Matthies, H.-R. Wenk, A.S. Schultz, J.W. Richardson Jr., J. Appl. Phys. 81 (1997) 594. [6] S. Matthies, J. Pehl, H.-R. Wenk, L. Lutterotti, S.C. Vogel, J. Appl. Crystallogr. 38 (2005) 462. [7] P. Day, J.E. Enderby, W.G. Williams, L.C. Chapon, A.C. Hannon, P.G. Radaelli, A.K. Soper, Neutron News 15 (2004) 19.
Physica B 385–386 (2006) 639–643 www.elsevier.com/locate/physb
Neutron texture analysis on GEM at ISIS W. Kockelmann, L.C. Chapon, P.G. Radaelli ISIS facility, Rutherford Appleton Laboratory, Didcot, Chilton, Oxfordshire, OX11 0QX, UK
Abstract Texture analysis by time-of-flight neutron diffraction, carried out on a multidetector instrument, requires just a few sample orientations. Moreover, on a diffractometer like GEM at ISIS with sufficiently high detector coverage a quantitative bulk texture analysis can be performed even on a stationary sample in a matter of minutes. A ‘single-shot’, rapid texture measurement on a highcount-rate instrument can be of considerable benefit for studying materials at non-ambient conditions or for bulky samples that cannot be mounted on a goniometer. The capabilities of GEM for texture analysis are demonstrated with results on copper texture standards, which were studied to accompany diffraction analyses of archaeological objects. r 2006 Elsevier B.V. All rights reserved. PACS: 61.12.Ex; 81.40.Ef; 61.12. q Keywords: TOF neutron diffraction; Texture analysis; Archaeometry
1. Introduction Neutron diffraction is a well-established tool in engineering and geological sciences for quantitative analysis of the crystallographic texture of a material, which is related to geological and mechanical deformation processes. Low absorption of neutrons is of considerable advantage for studying big, coarse-grained geological samples but also for testing intact and unique museum objects for which sampling is unacceptable. Texture is measured by recording diffraction patterns as a function of the scattering angle, either by (i) rotating the sample on a goniometer and/or by (ii) using a multi-detector surrounding the sample. The amount of texture information is roughly given by the product of the number of pole figures (h k l) times the number of sample orientations [1]. Hence, by using a polychromatic beam and with many detectors at fixed scattering angles, time-of-flight (TOF) neutron diffraction has some considerable advantages for texture analysis since significant portions of both reciprocal space and orientation space are simultaneously covered in one
Corresponding author. Tel.: +44 1235 446731; fax: +44 1235 445720.
E-mail address: [email protected] (W. Kockelmann). 0921-4526/$ - see front matter r 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.physb.2006.06.091
measurement. The capabilities and benefits of extracting the orientation distribution function (ODF) of a material from TOF data is well documented both from the experimental and the data treatment point of view. TOF texture analysis with a multidetector set-up was previously demonstrated at the pulsed source IBR-2, Russia [2] and at the pulsed spallation source LANSCE, USA [3]. Moreover, in the past few years Rietveld codes such as MAUD [4] and GSAS [5] have been developed to determine texture coefficients from TOF data. Texture measurements increasingly take advantage of this development in crystallography and Rietveld analysis, allowing texture information from complex low symmetry and polyphase materials to be extracted. By increasing the detector coverage on a TOF diffractometer fewer sample rotations are required for quantitative texture analysis, as was demonstrated on HIPPO at LANCSE [3,6] with typically 4–8 sample orientations and total collection times in the order of 20 min. Here we show that on GEM at ISIS, quantitative texture information can be obtained even faster from a single measurement without any sample rotations. Results on copper reference samples that were measured to calibrate the diffraction results of complex archaeological metal objects are presented to assess the texture analysis capabilities of GEM.
ARTICLE IN PRESS 640
W. Kockelmann et al. / Physica B 385–386 (2006) 639–643
2. Experimental details GEM is a high-count-rate materials diffractometer designed to study the structures of both crystalline and liquid and amorphous samples [7]. GEM is equipped with 6 detector banks, housing a total of about 7000 individual detector elements with banks 1 and 6 covering forward and backscattering angles, respectively (Fig. 1(a)). One detector element is typically about 5 200 mm2 in size, corresponding to an angular coverage of about 0.21 101 for a typical sample–detector distance at 901. The total detector coverage of GEM exceeds 4 sr, and hence considerable coverage in orientation space is provided. For the texture analysis 164 separate detector groups were generated, with each group covering approximately 101 101. The corresponding pole figure coverage for a single sample orientation (the
‘zero position’) is shown in Fig. 1(b), with the backscattering bank-6 detector groups in the pole figure centre. It has to be kept in mind that a symbol in the pole figure in Fig. 1 represents the angular location of a group of detectors. Hence one group records a range of sample orientations over which intensities are averaged, thus affecting the achievable angular texture resolution. Figs. 1(c) and (d) show the pole figure coverages for texture analyses with two sample orientations, with rotations about a vertical (601) and horizontal (901) axis, respectively. We have collected data from several copper and bronze specimens in order to assess the capabilities of GEM for texture analysis. Copper samples were made and mechanically treated, i.e. texturized, in different ways in order to mimic specific production and working processes of
Fig. 1. (a) GEM detector arrays and corresponding pole figure coverage, (b) for a single sample orientation, (c) for a second sample orientation with a 601 rotation about the vertical diffractometer axis, and (d) for a second sample orientatation with a 901 rotation about the horizontal incoming beam direction. The primary beam direction is in the centre of the pole figures. For each bank, the angular range and the product of texture groups times the accessible number of Cu (h k l) is given.
ARTICLE IN PRESS W. Kockelmann et al. / Physica B 385–386 (2006) 639–643
archaeological metals. Here we present data from two diskshaped copper samples (34 mm diameter, 2 mm thickness), a cold-rolled sample (sample 1), and a cold-rolled sample which was subsequently uniaxially compressed to 20% of its original thickness (sample 2). The beam size for the texture measurements was 20 20 mm2; hence the illuminated volume was 800 mm3. Data sets were collected for three separate sample orientations (as indicated in Fig. 1(b)–(d)), for the zero position, a 601 rotation about a vertical axis, and a 901 rotation about a horizontal axis, by remounting the aluminium sample holder on the sample stick. Data sets were collected as a function of time between 2 and 30 min. The data were normalized to the incident neutron flux distribution, corrected for detector efficiencies, and converted into 164 d-spacing patterns for each sample orientation corresponding to the texture detector grouping. 164 and 328 diffraction patterns were simultaneously Rietveld fitted in MAUD for one or two sample orientations, respectively, corresponding to pole figures coverages in Fig. 1(b)–(d). Values of the ODF cells were extracted using the extended WIMV (E-WIMV) algorithm [4] as implemented in MAUD, which can handle an incomplete and highly irregular pole figure coverage, as the one shown in Fig. 1(b). In order to account for sample absorption anisotropy due to the sample shape, one scale factors for
641
each diffraction pattern was refined. (1 1 1), (2 0 0), and (2 2 0) pole figures, which were reconstructed with MAUD from the ODF, are shown in equal area projection in Figs. 2 and 3. TOF texture analysis benefits from the simultaneous coverage of d-spacing range, i.e., number of pole figures, and of scattering angles. It is obvious that the forwardscattering banks have less weight in the quantitative texture analysis than the high-angle banks. The ‘texture information’ index, defined as a product of number of groups times number of observed (h k l), varies between banks as indicated in Fig. 1(a), ranging from 12 to 700 between banks 1 and 5, respectively, for the case of copper and a minimum d spacing of 0.5 A˚. It could be noted that an extension of GEM detector bank 5 which is just being commissioned will provide additional orientation coverage in the gap towards bank 6 (Fig. 1(b)). 3. Results and discussion Fig. 2 shows reconstructed (1 1 1), (2 0 0) and (2 2 0) pole figures of the cold-rolled sample 1 for the three setting of Fig. 1(b)–(d) with data collection times of 2 min per orientation. The pole figures showing the typical hallmarks of a rolling texture are very similar for the three types of coverage models. Maximum multiples of a random
Fig. 2. Pole figures of sample 1 for a single orientation according to Fig. 1(b) (top), 2 sample orientations accoring to Fig. 1(c) (middle) and 2 sample orientation according to Fig. 1(d) (bottom). Sample normal in the pole figure centre. Rolling direction in the vertical.
ARTICLE IN PRESS W. Kockelmann et al. / Physica B 385–386 (2006) 639–643
642
Fig. 3. Pole figures of sample 2 for collection times of 2 (top), 8 (middle) and 30 min (bottom). Table 1 Details of the pole figure determination for copper samples 1 and 2, Number of orientations, collection time per orientation, maximum pole density, texture index F2 and texture agreement index RB Type of coverage
# sample angles
Sample 1, cold rolled Fig. 1(b) 1 Fig. 1(c) 2 Fig. 1(d) 2 Sample 2, cold rolled+uniaxially compressed Fig. 1(b) 1 Fig. 1(b) 1 Fig. 1(b) 1
Time min.
Max. m.r.d.
F2
RB %
2 2 2
2.14 2.39 2.18
2.63 2.55 2.31
9.5 9.8 10.4
2 8 30
3.41 3.57 3.52
3.53 3.55 3.56
10.0 9.5 9.5
distribution (m.r.d.) values and texture indices F2 [1] are in reasonable agreement (Table 1). It can be concluded that the texture can be determined from a single orientation in the present study. In other cases, for bulky and irregularly shaped copper objects, convergence of refinements was improved with an extra sample orientation, probably due to the presence of strong directional absorption. Coverage models as displayed in Fig. 1(c), (d) may also prove useful and necessary for low crystal symmetries. Fig. 3 compares copper pole figures of the rolled and subsequently compressed sample 2 for collections times of 2, 8 and 30 min. The pole figures clearly show the effect of the equiaxial compression in terms of the texture index (Table 1) and texture type. The pole densities follow, to a first approximation, a (1 1 0) fibre texture but poles are not equally distributed due to the initial rolling texture.
Textural features and texture indices do not change significantly after 2 min collection time. The results on the Cu reference samples suggest that pole figures can be reliably obtained on GEM in a ‘single shot’ in a matter of minutes without any sample reorientations. The angular coverage of 101 101 per element proved to be adequate for the slow varying textures of the samples studied here while for sharper textures the angular detector coverage per group can be reduced by software. Stationary and rapid texture analyses have a particular advantage, for example, for in situ growth studies at non-ambient conditions. Bulky engineering and archaeological objects can be non-destructively analysed in terms of phase composition, microstructure parameters and texture, whilst keeping the radio activation to a minimum.
ARTICLE IN PRESS W. Kockelmann et al. / Physica B 385–386 (2006) 639–643
Acknowledgements Support by L. Lutterotti, University of Trento, Italy, with the MAUD program is gratefully acknowledged. We would like to thank H. Phiesel, Bonn University, Germany, for providing the reference samples. References [1] H.-R. Wenk, P. Van Houtte, Rep. Prog. Phys. 67 (2004) 1367.
643
[2] K. Feldmann, M. Betzl, W. Kleinsteuber, K. Walther, Textures Microstruct. 14–18 (1991) 59. [3] H.-R. Wenk, L. Lutterotti, S. Vogel, Nucl. Instr. and Meth. A 515 (2003) 575. [4] R.B. von Dreele, J. Appl. Crystallogr. 30 (1997) 517. [5] L. Lutterotti, S. Matthies, H.-R. Wenk, A.S. Schultz, J.W. Richardson Jr., J. Appl. Phys. 81 (1997) 594. [6] S. Matthies, J. Pehl, H.-R. Wenk, L. Lutterotti, S.C. Vogel, J. Appl. Crystallogr. 38 (2005) 462. [7] P. Day, J.E. Enderby, W.G. Williams, L.C. Chapon, A.C. Hannon, P.G. Radaelli, A.K. Soper, Neutron News 15 (2004) 19.