Fatigue Crack Growth Curves for Material Selection, Design and Service-Life Studies of Carbon-Fibre Reinforced-Plastic Composites: Effect of Test Temperature and R-ratio

Fatigue Crack Growth Curves for Material Selection, Design and Service-Life Studies of Carbon-Fibre Reinforced-Plastic Composites: Effect of Test Temperature and R-ratio | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Fatigue Crack Growth Curves for Material Selection, Design and Service-Life Studies of Carbon-Fibre Reinforced-Plastic Composites: Effect of Test Temperature and R-ratio A. J. Brunner, R. Jones, Anthony J. Kinloch This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8180128/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract The present paper addresses the problem of fatigue crack growth in composite structures, with special relevance to composite airframes. Assessment of their in-service life can be based on either a ‘no-growth’ or a ‘slow-growth’ design philosophy. Whilst the most widely used approach is to adopt a ‘no-growth’ approach, it is accepted that this can result in an overly conservative design. Furthermore, even for ‘no-growth’ designs, delamination growth under cyclic fatigue loads can still arise in-service. Thus, the immediate challenge facing the composites community is to extend the ‘no-growth’ design philosophy to allow for the nucleation and growth of small, naturally-occurring delaminations in composite structures. The main aim of the present paper is, therefore, to investigate the robustness of the Hartman-Schijve methodology to meet this challenge when the effects of test temperature, together with the R -ratio, on the fatigue behaviour are considered. CFRP composites fatigue fracture mechanics modelling service-life test temperature Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction The third volume of the Composite Materials Handbook [ 2 ] provides methodologies and lessons learned for the design, analysis, manufacture and field support of fibre-reinforced, polymeric-matrix composite structures. It notes that the assessment of cyclic fatigue crack growth in such composite structures, with special relevance to airframes, can be based on either a ‘no-growth’ or a ‘slow-growth’ design philosophy. Whilst the most widely used approach is to adopt a ‘no-growth’ approach, it is accepted that this can result in an overly conservative design. Furthermore, it is now known [‎3,4] that, even for such ‘no-growth’ designs, delamination growth arising from cyclic fatigue loads can still arise in-service. As such two of the immediate challenges facing the composites community are: To extend the ‘no-growth’ design philosophy to allow for the nucleation and growth of small naturally-occurring delaminations in composite structures. To develop the tools needed to provide a conservative estimate for the remaining life, and hence the inspection intervals, for delamination damage found in an operational airframe. (These the tools are needed for aircraft sustainment.) The principle of ‘slow-growth’ in fibre-reinforced polymeric-matrix composites is applicable to delamination growth under cyclic fatigue loads which is slow and stable. It is recommended [ 5 , 6 ] that this be analysed using linear-elastic fracture-mechanics (LEFM). This requires a knowledge of the da/dN versus \(\:\varDelta\:\sqrt{G}\:\) curves, where da/dN is the fatigue crack (i.e. delamination) growth (FCG) per cycle and \(\:\varDelta\:\sqrt{G}\) is the \(\:\:\) range of the applied energy release-rate in the fatigue cycle, as defined by \(\:\:\sqrt{{G}_{max}}-\:\sqrt{{G}_{min}}\) ; where \(\:{G}_{max}\:\) and \(\:{G}_{min}\:\) are the maximum and minimum values of the applied energy release-rate in the fatigue cycle, respectively [ 7 , 8 ]. Furthermore, the NASA Fracture Design Handbook NASA-HDBK-5010 [‎6] mandates the use of a ‘worst-case upper-bound’ FCG curve for material selection, design and service-life (sustainment) studies. This worst-case upper-bound FCG curve for a carbon-fibre reinforced-plastic (CFRP) should: Exhibit no, or only minimal, retardation, arising from fibre bridging. Since the fastest growing, i.e. lead, delaminations that arise under cyclic-fatigue loading of real composite structures or components from mis-drilled holes, ply drop-offs and impact damage show no, or only little, retardation from fibre-bridging developing across the faces of the delamination as the fatigue crack advances [ 8 ]. Allow for scatter in the test data [ 9 , 10 ]. Encompass, and bound, all the experimental results. Be representative of, and applicable to, small, naturally-occurring delaminations [ 11 , 12 ] However, the experimental determination of valid and relevant da/dN versus \(\:\varDelta\:\sqrt{G}\:\) FCG curves is where major problems have been encountered, which have only relatively recently been identified and resolved. These problems arise from the use of the double-cantilever beam (DCB) test specimen, see Fig. 1 , to experimentally measure the Mode I (i.e. opening tensile) da/dN versus \(\:\varDelta\:\sqrt{G}\:\) FCG curves [ 13 ]. This test specimen contains a through-thickness crack and has an inserted polymer film to act as a starter crack of length, a o , for the crack from which the FCG measurements will be subsequently taken. However, this polymer film, although typically only about 10 to 13 µm in thickness, acts as relatively blunt crack and must be extended to a length a p so as to give a naturally-sharp crack tip for the subsequent actual measurements. Unfortunately, this requirement for some pre-test crack extension leads to fibre bridging developing across the crack faces, prior to the start of the fatigue test, which subsequently retards the propagation of the delamination under the cyclic fatigue loads. Indeed, the experimental data reveals that such retardation effects cannot be avoided. The experimental data also reveals that the DCB fatigue test results may show a great deal of scatter [ 9 , 10 ], which in-part arises from fibre-bridging developing during the test. It is, therefore, very difficult to experimentally determine a worst-case upper-bound FCG curve. The same comments are true with respect to determining a valid value of the fatigue threshold, \(\:\varDelta\:\sqrt{{G}_{thr}}\) , below which no significant FCG occurs. Finally, in the FCG of metallic structures it is well established that small, embedded, naturally-occurring defects do not behave in the same manner as relatively large through-thickness cracks as used in a typical test specimen [ 11 , 12 ]. The problem being that such small, naturally-occurring, cracks grow faster than expected from observations of the latter type of through-thickness cracks. In the DCB test only though-thickness cracks can be employed. Consequently, the FCG curves developed using DCB tests are not applicable to a sustainment assessment. To examine in detail these problems of measuring the FCG rate curves from DCB tests using CFRPs, the European Structural Integrity Society (ESIS), Technical Committee (TC) 4 (on Polymers, Composites and Adhesives) has launched a round-robin study, using Mode I DCB tests, into (a) the effect of the pre-crack length, a p -a o , prior to the start of the measurements from the laboratory cyclic-fatigue test, on the measured FCG rate, da / dN , data and (b) the reproducibility of the FCG rate curves under cyclic-fatigue loads. The first paper from this study [ 14 ] confirmed that a key experimental parameter is the value of the pre-crack (i.e. pre-delamination) extension length, a p -a o , in the DCB test specimen prior to any cyclic-fatigue measurements being undertaken. As mentioned above, this phenomenon arises since varying the value of a p -a o typically leads to a varying degree of fibre bridging developing behind the tip of the fatigue delamination, prior to the start of the actual fatigue test. The presence of such fibre bridging may retard the FCG rate and so lead to an impression of enhanced fatigue behaviour that is not present when no, or very, little fibre bridging occurs, as is typically the case in a real composite component. In this study [ 14 ], the FCG results were measured by the two independent laboratories and the results were in good agreement. In a second paper [ 15 ] it was shown that the Hartman-Schijve equation [ 7 , 8 , 16 , 17 ], determined with test data generated [ 14 ] under an R -ratio of R = 0.1, could also be used to compute the FCG curves associated with tests performed at R = 0.3, 0.5 and 0.7, where R is the load ratio (= P min / P max ). More importantly, the Hartman-Schijve methodology (a) can be used to determine a worst-case upper-bound da/dN versus \(\:\varDelta\:\sqrt{G\:}\:\) FCG curve which exhibits no retardation, (b) takes into account the experimental scatter, (c) encompasses and bounds all the experimental results and (d) can represent the fatigue growth of small, naturally-occurring delaminations in the CFRP. (The latter curves are needed for a sustainment analysis, i.e. to determine the remaining life of the airframe and hence the inspection intervals.) The main aim of the present paper is to investigate the robustness of this Hartman-Schijve methodology from [ 14 , 15 ] when the effects of test temperature together with varying the R -ratio [ 18 , 19 ] are studied. 2. Theoretical Due to the inhomogeneity and anisotropy of continuous-fibre composites, the energy release-rate, G , approach, rather than the stress-intensity factor approach, is generally used to study delamination growth in such materials. Now, following the work of Paris et al. [ 20 – 22 ] on metals, where the range, \(\:\varDelta\:K\) , of the applied stress-intensity factor is invariably employed to analyse the data, then at first sight the most obvious and corresponding parameter against which to plot the measured rate of the FCG, da/dN , for composites is the range of the applied energy release-rate, \(\:\varDelta\:G\) , where \(\:\varDelta\:G={G}_{max}-{G}_{min}\) . However, theoretical work by Sih, Paris and Irwin [ 23 ] and recent work, for example by Jones et al. [ 7 , 8 , 24 ], Yao et al. [ 25 ] and Simon et al. [ 26 ], has revealed that the logical extension of the Paris FCG equation for metals to delamination growth in CFRPs is, in fact, to express da/dN as a function of \(\:\varDelta\:\sqrt{G}\) rather than ∆ G . Where \(\:\varDelta\:\sqrt{G}\:\) is given by: $$\:\varDelta\:\sqrt{G}=\sqrt{{G}_{max}}-\sqrt{{G}_{min}}$$ 1 Following these ideas, a novel empirical methodology based on using a variant of the Hartman-Schijve [ 15 , 16 ] equation has been proposed to access the ‘worst-case upper-bound’ FCG rate curve, which may be thought of as a material-allowable property. The form proposed [e.g. 7,8,14,15,25,26] for the Hartman-Schijve equation, which is a variant of the NASGRO equation, in terms of \(\:\varDelta\:\sqrt{G}\) is: $$\:\frac{da}{dN}=D.\:{}^{n}=\:D.{\left[\frac{\varDelta\:\sqrt{G}-\:\varDelta\:\sqrt{{G}_{thr}}}{\surd\:\left\{1-\:\sqrt{{G}_{max}}/\sqrt{A}\right\}}\right]}^{n}\:$$ 2 where \(\:\:\) is the crack driving force (CDF) [ 17 ] in terms of energy release-rate, i.e. the ‘similitude parameter’, and where D and n are constants and A is the cyclic fracture toughness. The term \(\:\varDelta\:\sqrt{{G}_{thr}}\) is defined by: $$\:\:\varDelta\:\sqrt{{G}_{thr}}=\:\sqrt{{G}_{max.thr}}-\:\sqrt{{G}_{min.thr}}$$ 3 and the subscript ‘ thr ’ in Equations ( 2 ) and ( 3 ) refers to the values at threshold, below which no significant FCG occurs. As noted above, using the Hartman-Schjive methodology it has been found possible (a) to determine a worst-case upper-bound da/dN versus \(\:\varDelta\:\sqrt{G\:}\:\) FCG curve which exhibits no FCG retardation and (b) to take into account the experimental scatter. This worst-case curve (a) encompasses and bounds all the experimental results and (b) can be formulated to be representative of small naturally-occurring delaminations in the CFRP [ 15 ]. It should be noted that, since the DCB test cannot yield a directly measured ‘worst-case upper-bound’ FCG curve which is free from fibre-bridging, other methods of analysing the experimentally results have also been by proposed [e.g. 27]. However none of these have also considered the additional detrimental effect of small, naturally-occurring delaminations, as opposed to through-thickness delaminations, as might be found in an operational composite component or structure. This is a major aim of the present work. In the present study the cyclic fatigue test results for the unidirectional CFRP material are taken at test temperatures of -40 o C, 50 o C and 80 o C from Yao et al. [ 18 , 19 ] and are compared to those reported for room temperature tests at 22 o C reported by Michel et al. [ 14 , 15 ]. It will be shown that delamination growth in these tests can be captured using the same equation as given in Michel et al. [ 14 , 15 ] for room temperature tests at R -ratios of 0.1, 0.3. 0.5 and 0.7, viz: $$\:\frac{da}{dN}=1.23.{10}^{-10}\:{\left[\frac{\varDelta\:\sqrt{G}-\:\varDelta\:\sqrt{{G}_{thr}}}{\surd\:\left\{1-\sqrt{{G}_{max}}/\sqrt{A}\right\}}\right]}^{4.49}$$ 4 3. Experimental The CFRP was a continuous carbon-fibre epoxy-matrix polymer composite which was fabricated using a prepreg of unidirectional, continuous carbon fibres in a thermosetting epoxy polymer matrix, ‘M30SC/DT120′ supplied by Delta-Tech S.p.A., Altopascio, Italy. (A unidirectional, continuous fibre CFRP was chosen since this is the orientation of the fibres given in the ISO Standard test method [ 1 ] and this fibre orientation typically results in a severe degree of fibre-bridging occurring.) In all cases, a thin film of poly(tetrafluoroethylene), 12.7 µm in thickness, was inserted at one end of the DCB test specimen between the plies in the mid-plane of the CFRP panel during the hand lay-up process to act as an initial delamination, or ‘starter crack’, see Fig. 1 ; where the nominal length, a o , is about 50 mm. The laid-up panels were placed in an autoclave and cured for 90 minutes at a temperature of 120°C under a pressure of 6 bar. The glass transition temperature of the resulting thermosetting epoxy polymer matrix was 120°C. The cyclic-fatigue tests were undertaken according to the test protocol outlined in [ 13 ]. This protocol involved first growing the crack under quasi-static loading until a clearly visible stable crack extension has been observed, which should be a relatively short distance, i.e. less than 3.0 mm, from the initial delamination, a o . This new pre-crack extension length, was termed a p - a o . Fatigue cycling was then started, under displacement control, with a deformation range where the maximum deformation was equal to the value reached in the preceding quasi-static loading test. (It should be noted that, if a pre-crack extension length of a p -a o is not used in the fracture mechanics test, then optimistically high values of the quasi-static toughness and fatigue resistance will be measured. This is because the starter crack film of length a o represents a relatively blunt crack tip compared to that of a pre-crack of length a p , which is naturally-grown ahead of the starter film prior to the commencement of the test.) For any given DCB specimen, the test method that was employed involved growing the crack under cyclic-fatigue loading, at a relatively low frequency of 5 Hz, to avoid heating effects, for a relatively short distance from the initial value of the pre-crack extension length, a p -a o , whilst taking readings of the number of cycles, N , crack length, a , load, P , and displacement, δ . This fatigue test was then halted and the fatigue testing was repeated, but now with respect to the new, longer crack that was present in the DCB specimen, i.e. the crack length a p -a o that was now present was taken to be the relevant initial value for this repeated fatigue test. Thus, a key variable when testing a given DCB test is the value of the pre-crack extension length, a p -a o , prior to measurements being taken for any cyclic-fatigue test. For these fatigue tests [ 18 , 19 ], the test temperature was − 40 o , 50 o or 80 o C and the value of the \(\:R\) -ratio used was 0.1 or 0.5. The measured data were employed to calculate, for a given DCB test at a given value of the crack extension length, a p -a o , the FCG rate, da / dN , using the ‘incremental polynomial method’ [ 11 , 13 ]. Also, the values of the maximum and minimum energy release-rate, \(\:{G}_{max}\:\) and \(\:{G}_{min}\) , applied in a fatigue cycle were deduced from the maximum and minimum applied measured loads, \(\:\:{P}_{max}\) and \(\:{P}_{min},\) respectively, using the ‘modified compliance method’ as described in detail in the ISO Standard [ 1 ]. Finally, it should be noted that experimental results reveal that the DCB fatigue test may show a great deal of scatter [ 9 , 10 ], which in-part arises from fibre-bridging developing during the test, as noted above. Another source of possible scatter in the experimental data arises from the fact that changes in the fracture or fatigue delamination resistance of fibre-reinforced polymer-matrix composites may occur from the absorption of moisture from the respective ambient humidity [ 28 – 31 ]. Thus, tests results obtained outside of a controlled laboratory climate, e.g. 23 ± 2°C and 50 ± 5% relative humidity [ 32 ], at different temperatures without controlling the ambient humidity may therefore be affected by the level of moisture. In the present tests [ 18 , 19 ] the relative humidity in the temperature chamber was not reported. 4. Results 4.1 Overview In the present section, the experimental results from the tests conducted [ 18 , 19 ] at -40 o , 50 o and 80 o C and at the R -ratios of 0.1 and 0.5, and reinterpreted according to the theoretical scheme discussed above, for the CFRP are first considered, see Figs. 2 to 7 . The use of the Hartman-Schijve Eq. ( 2 ), to replot all these data to obtain a unique, linear, master relationship is then presented and these data are compared to the previous results [ 14 , 15 ] for the same type of CFRP obtained at 22 o C, see Fig. 8 . The parameters deduced from this approach are then used to compute the experimentally measured results that are shown in Figs. 2 to 7 . Finally, the ‘worst-case upper-bound FCG curves’, for a small, naturally-occurring delamination present in a structure or component using this type of CFRP, are calculated using the Hartman-Schijve methodology and are compared to the experimentally measured results, see Figs. 9 and 10 . 4.2 Experimental plots of the logarithmic da / dN versus the logarithmic \(\:\varDelta\:\sqrt{G}\) In Fig. 2 values of the logarithmic da / dN versus the logarithmic \(\:\varDelta\:\sqrt{G}\) calculated from the tests reported in [ 18 , 19 ] at -40 o C for an R -ratio of 0.5 for the CFRP composites are given as a function of the pre-crack extension length, a p -a o , prior to the start of measurements from the DCB fatigue test. As may be seen from the results shown in Fig. 2 , the FCG curves move steadily to the right as the pre-crack extension length, a p -a o , prior to measurements being taken for the test is increased. This implies that there is a retardation of the FCG rate as the value of a p -a o is increased. That is, as the value of a p -a o increases, and for a given value of \(\:\varDelta\:\sqrt{G}\) , the corresponding value of da / dN is lower; so, at a given value of \(\:\:\varDelta\:\sqrt{G}\) , the fatigue crack grows slower as the value of a p -a o is increased. These observations arise from the extent of fibre bridging in the DCB test specimen being more extensive as the pre-crack extension length, a p -a o , is increased, since this retards the rate of delamination growth at a given value of \(\:\:\varDelta\:\sqrt{G}\) . It is very important to note that such results as in Fig. 2 agree in general form with those from previous studies [e.g. 8,13–15,25,26] and clearly reveal that there is not one unique FCG rate curve. Instead, a number of such FCG rate curves may be obtained depending upon the chosen value of the pre-crack extension length, a p -a o , at the start of the test, prior to measurements being taken from that given cyclic-fatigue test. The results shown in Figs. 3 to 7 for the other test temperatures and R -ratios all reveal similar effects. Furthermore, by comparing Figs. 2 and 3 for − 40 o C, Figs. 4 and 5 for 50 o C and Figs. 6 and 7 for 80 o C, it may be seen that for the DCB tests, at a given value of the pre-crack extension length, a p -a o , then when subjected to the higher R -ratio of 0.5, as opposed to 0.1, a somewhat higher value of da/dN is recorded for a given value of the \(\:\varDelta\:\sqrt{G}\) . This is as expected [ 24 ], since an increase in the R- ratio increases the mean stress and therefore for a given value of \(\:\varDelta\:\sqrt{G}\) , at a given value of a p -a o , a higher R -ratio leads to a higher FCG rate, da/dN . (The effect of test temperature on the FCG behaviour is discussed below.) 4.3 Use of the Hartman-Schijve Equation To bring all the results shown in Figs. 2 to 7 together in one ‘master’ plot, the use of the Hartman-Schijve methodology, more specifically Eq. ( 2 ), is next examined to investigate whether the experimental results shown in Figs. 2 to 7 can be replotted to give a unique, linear, master curve so that the constants D and n may be ascertained. To achieve this, the values of A and \(\:\varDelta\:\sqrt{{G}_{thr}}\) are chosen so as to ensure that Eq. ( 2 ) best fits an experimental set of test data of logarithmic da / dN versus logarithmic \(\:\varDelta\:\sqrt{G}\:\) over the entire range of FCG rates. As in [ 14 , 15 ], the values of A and \(\:\varDelta\:\sqrt{{G}_{thr}}\:\) were determined using the ‘Total Least Squares’ methodology described in [ 33 ], so that, for a given set of test data at a given value of a p - a o , the logarithmic da / dN versus logarithmic \(\:\left[\frac{\varDelta\:\sqrt{G}-\:\varDelta\:\sqrt{{G}_{thr}}}{\surd\:\left\{1-\:\sqrt{{G}_{max}}/\sqrt{A}\right\}}\right]\:\) plots became virtually linear. After the individual linear relationship for a given set of test data points had been determined, a combined plot of each of the different tests was assembled, as shown in Fig. 8 . Here, it can be seen that, allowing for experimental error, all the resultant plots enable a single, linear, master relationship to be defined when using logarithmic axes. This unique, linear, master relationship, i.e. as in Eq. ( 2 ), holds for the over forty sets of data shown in Fig. 8 . The value of the linear coefficient of determination, R 2 , for this linear plot is 0.93. This is somewhat lower than found previously [ 14 , 15 ] due to the relatively high scatter observed in the test conducted at 80 o C at an R ratio of 0.1 with a p -a o being 90.6 mm, see Fig. 7 . Notwithstanding, the linear fit for the present set of test data is in very good agreement with that from previous work [ 14 , 15 ], as may be seen from Fig. 8 , and the values of D and n for the present and previous tests [ 14 , 15 ] are 1.23 x 10 − 10 and 4.49, respectively. These values are used below (a) to compute the logarithmic da / dN versus logarithmic \(\:\varDelta\:\sqrt{G}\) relationships as a function of a p -a o , together with the respective \(\:\varDelta\:{\sqrt{G}}_{thr}\) and \(\:\surd\:A\) values and (b) to predict the worst-case, upper-bound FCG rate curves for a small naturally-occurring delamination, together with the appropriate mean value of \(\:\varDelta\:{\sqrt{G}}_{thr}\) (taken to be zero) and the quasi-static value of the Mode I (tensile) interlaminar fracture energy, \(\:{G}_{co}\) , at the onset of crack growth, as described below. 4.4 Computed plots of the logarithmic da / dN versus the logarithmic \(\:\varDelta\:\sqrt{G}\) Equation ( 2 ) with the values of the parameters needed to fit the Hartman-Schijve relationship, see above, can now be used to compute the full experimental curve of logarithmic da / dN versus logarithmic \(\:\varDelta\:\sqrt{G}\) . The computed relationships are shown in Figs. 2 to 7 and, as may be seen, there is excellent agreement between the experimental data and the computed curves from the Hartman-Schijve methodology. Indeed, the values of the coefficients of determination, R 2 , associated with a comparison of the measured and computed curves shown in Figs. 2 and 3 were determined and have a mean of about 0.90 and a standard deviation of 0.08. Therefore, despite the obvious and expected scatter in the experimental measurements, the values of R 2 for the various computed curves are relatively high, thereby confirming the good agreement between the experimental and theoretical results. 4.5. The worst-case upper-bound FCG curve for sustainment Let us next address the question of how to determine the worst-case upper-bound FCG curve of da/dN versus \(\:\varDelta\:\sqrt{G}\) that is needed to assess the FCG rate for a small naturally-occurring delamination that may arise in an operational airframe, i.e. for a sustainment assessment of the airframe. To undertake this calculation we use Eq. ( 2 ) and take from Fig. 8 the values of the constants D and n to be 1.23 x 10 − 10 and 4.49, which are in agreement with previous work for this CFRP [ 15 ]. Next, we need the value \(\:\varDelta\:\sqrt{{G}_{thr}}\) , and following previous convention [ 15 ] we set the value of \(\:\varDelta\:\sqrt{{G}_{thr}}\) to be zero. This only leaves the question of what value to use as the worst-case value of the apparent cyclic fracture toughness, \(\:A\) . However, since we are dealing with small, naturally-occurring cracks, and since as shown in [ 18 , 19 ], the value of \(\:{G}_{co}\) appears to be essentially independent of the test temperature, the logical approach is to use the worst-case [ 8 , 15 , 34 , 35 ] of the ‘mean-3 σ ’ value of the quasi-static initiation term, termed \(\:{G}_{co,3\sigma\:}\) . Noting that the mean value of \(\:{G}_{co}\) given in [ 15 , 18 ] is 250 J/m 2 with a standard deviation, σ , of ± 45 J/m 2 , this yields a worst-case value of \(\:\sqrt{A}\:\) of 10.7 √(J/m 2 ). Now, in Figs. 9 and 10 the values of logarithmic da / dN versus logarithmic \(\:\varDelta\:\sqrt{G}\) are plotted again for the tests performed at -40 o , 50 o and 80 o C for the CFRP for the two R -ratios of 0.1 and 0.5, respectively. Values are given in the legend for the pre-crack extension length, a p - a o , prior to the start of measurements from the DCB fatigue test specimen. It may be seen that the experimental data reveals that the FCG rate can accelerate with elevated temperatures but decrease at sub-zero temperatures. However, the dependence of the FCG curve at a given value of \(\:\varDelta\:\sqrt{G}\) on the pre-crack extension length, a p - a o , greatly complicates the interpretation and assessment of the overall effect of test temperature. The resultant worse-case FCG curves for a small naturally-occurring delamination that may arise in an operational airframe were calculated as described above and are shown in Figs. 9 and 10 , where they are labelled the ‘Small delam curve’. Several interesting points arise concerning these worst-case curves. Firstly, these worst-case FCG curves shown in Figs. 9 and 10 are in very good agreement with that calculated for the previous tests conducted [ 15 ] at 22 o C at R -ratios of 0.1, 0.3, 0.5 and 0.7. Secondly, as required, these predicted FCG worst-case curves shown in Figs. 9 and 10 encompass and bound all the experimental results. Thirdly, these (sustainment) FCG curves are independent of the test temperature and the R -ratio. Fourthly, the vertical lines shown in Figs. 9 and 10 are for the case when the value of \(\:{G}_{max}\:\) is taken to be equal to \(\:{G}_{co,3\sigma\:}\) = \(\:A\) in Eq. ( 2 ) and they represent the asymptotic values for the worst-case FCG curves for a naturally-occurring delamination. Finally, these predicted FCG curves might be considered by some to be ‘overly-conservative’ but they are as determined in accordance with standard airframe economic life-assessment procedures, as first delineated by Lincoln et al. [ 36 ]. (The paper by Lincoln [ 36 ] was the first to highlight that a sustainment analysis should use the crack growth curve associated with small naturally-occurring cracks.) Indeed, one reason, of course, for their apparent conservative appearance is that we have chosen to use the ‘mean-3 σ ’ value of the quasi-static initiation term, termed \(\:{G}_{co,3\sigma\:}\) , which yields a worst-case value of \(\:A\) . This is termed the ‘A’ basis approach [ 8 , 34 , 35 ]. In this case, the mechanical property value indicated is the value above which at least 99% of the population of values is expected to fall with a confidence of 95%. This value is used to design and predict the service-life of a member where the loading is such that its failure would result in a loss of structural integrity. This approach to ensuring structural integrity is mandated in the NASA Fracture Control Handbook NASA-HDBK-15-10 [6‎]. If a lower number of standard deviations, σ , is taken, e.g. taking minus two standard deviations as in the ‘B’ basis approach [ 8 , 34 , 35 ], then the statistical probability of failure increases and the design becomes less conservative. 5. Conclusions The present paper addresses the problem of fatigue crack growth in composite structures, with special relevance to airframes. Assessment of their in-service life can be based on either a ‘no-growth’ or a ‘slow-growth’ design philosophy. Whilst the most widely used approach is to adopt a ‘no-growth’ approach, it is accepted that this can result in an overly conservative design. Furthermore, even for ‘no-growth’ designs, delamination growth arising from cyclic fatigue loads can still arise in-service. Thus, two of the immediate challenges facing the composites community are to extend the ‘no-growth’ design philosophy to allow for the nucleation and growth of small naturally-occurring delaminations and to develop the tools needed to determine the remaining life of a damaged airframe, and hence the necessary inspection intervals. The main aim of the present paper has been therefore to investigate the robustness of the Hartman-Schijve methodology to meet this challenge when the effects of test temperature, together with the R -ratio, on the fatigue behaviour are considered. Studies have been published in the literature [ 18 , 19 ] on the cyclic fatigue crack growth (FCG) behaviour, as a function of both test temperature and R -ratio, of a continuous carbon-fibre epoxy-matrix plastic (CFRP) composite which was fabricated using a prepreg of unidirectional, continuous carbon fibres in a thermosetting epoxy polymer matrix. We have re-interpreted these data such that a Hartmann-Schjive approach [ 7 , 8 , 15 , 16 ] may be adopted. Hence, worse-case FCG curves for a small naturally-occurring delamination that may arise in an operational airframe have been deduced. These curves, which are obtained by following an approach that reflects the USAF-Boeing approach [ 36 ] and that mandated in NASA-HDBK-1510 [ 6 ], are in good agreement with previous results [ 15 ] for tests conducted at 22 o C at R -ratios of 0.1, 0.3, 0.5 and 0.7. Further, as required, these predicted worst-case, small naturally-occurring delamination FCG curves encompass and bound all the experimental results; and they are independent of the test temperature and the R -ratio. Such curves provide the data needed for a ‘slow-growth’ design philosophy to be adopted and for the remaining life and inspection intervals to be determined. Otherwise the detection of delamination damage in an operational aircraft will lead to the need to either immediately repair or to replace the component. Nomenclature Declarations Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding: This work did not receive external funding. Author Contribution Andreas J. Brunner : Review & Editing. Anthony J. Kinloch : Validation, Writing, Review & Editing. Rhys Jones : Analysis and Methodology, Review & Editing. Acknowledgements The inputted experimental data has been previously published [18,19] and the authors would wish to thank Dr. L.Yao for making some of it available to Professor Jones in a spreadsheet format. Data Availability Data will be made available on request. References ISO Standard 15024: Fibre-reinforced plastic composites - Determination of mode I interlaminar fracture toughness, G Ic , for unidirectionally reinforced materials. 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 18 Feb, 2026 Reviews received at journal 18 Feb, 2026 Reviewers agreed at journal 02 Feb, 2026 Reviewers agreed at journal 15 Dec, 2025 Reviews received at journal 10 Dec, 2025 Reviewers agreed at journal 02 Dec, 2025 Reviewers agreed at journal 24 Nov, 2025 Reviewers invited by journal 24 Nov, 2025 Editor assigned by journal 24 Nov, 2025 Submission checks completed at journal 24 Nov, 2025 First submitted to journal 22 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":44340,"visible":true,"origin":"","legend":"\u003cp\u003eSketch of the double cantilever beam (DCB) CFRP composite test specimen. Showing the initial, starter-crack, delamination of length, \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e, in the DCB specimen, which was introduced to give a pre-crack length of \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e before measurements are taken for the cyclic-fatigue test [13].\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8180128/v1/25ae208e9bd634e227801106.jpg"},{"id":97137192,"identity":"d07168db-009a-42da-bba6-1f8dd4c1b65c","added_by":"auto","created_at":"2025-12-01 09:57:27","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":212971,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8180128/v1/b838d89a0b8ccd0072eb5d1d.jpg"},{"id":96991151,"identity":"47f0078f-ba17-4139-9160-1b80fb9340a7","added_by":"auto","created_at":"2025-11-28 11:22:23","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":188632,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8180128/v1/8876dd58993c48bdd38abcc0.jpg"},{"id":97139060,"identity":"73172b7a-0fb3-41ce-9527-149b1834c4d9","added_by":"auto","created_at":"2025-12-01 09:59:34","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":207498,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8180128/v1/f14ba120666b25ba31e97c35.jpg"},{"id":96991155,"identity":"d9e0bf7a-6b9d-4ddd-b3e0-30d6f451f1e7","added_by":"auto","created_at":"2025-11-28 11:22:23","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":200478,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8180128/v1/1b2eafcd419cd762d171ef6c.jpg"},{"id":97138848,"identity":"05793c7a-cfec-4925-9662-c03de551330b","added_by":"auto","created_at":"2025-12-01 09:59:23","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":199477,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8180128/v1/3a9f209922edf95efda7b0b4.jpg"},{"id":97139028,"identity":"3fdde3fb-2205-4804-9f08-b57bea849519","added_by":"auto","created_at":"2025-12-01 09:59:33","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":156686,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8180128/v1/9ef2d2ca3f77a176b8dae70c.jpg"},{"id":97138679,"identity":"acbe74ea-11d9-475f-9658-10e41b878333","added_by":"auto","created_at":"2025-12-01 09:59:11","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":143525,"visible":true,"origin":"","legend":"\u003cp\u003eThe linear, master relationship obtained for all the tests conducted at -40\u003csup\u003eo\u003c/sup\u003e, 50\u003csup\u003eo\u003c/sup\u003e and 80\u003csup\u003eo\u003c/sup\u003eC and at the \u003cem\u003eR\u003c/em\u003e-ratios of 0.1 and 0.5 for the CFRP composite as calculated using the Hartman-Schijve methodology, i.e. Equation (2). The value of the coefficient of determination, R\u003csup\u003e2\u003c/sup\u003e, is 0.93. The values of \u003cem\u003eD\u003c/em\u003e and \u003cem\u003en\u003c/em\u003e for the linear fit are 1.23 x 10\u003csup\u003e-10\u003c/sup\u003e and 4.49, respectively and are taken from previous work [15], see Equation (4).\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8180128/v1/e386b2e72e2d81f85da1476d.jpg"},{"id":96991166,"identity":"df383c12-1f5b-401d-b5a2-02b1aa2b130f","added_by":"auto","created_at":"2025-11-28 11:22:23","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":227019,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8180128/v1/7816f3d5c6d74e1220b5cfef.jpg"},{"id":96991173,"identity":"68365f76-354d-46b9-b8de-3130a78ccd8f","added_by":"auto","created_at":"2025-11-28 11:22:23","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":223951,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8180128/v1/98ee792dc5d44e6dfc3b6677.jpg"},{"id":97248384,"identity":"8966fee3-f27d-4a15-9921-1178df1eab9e","added_by":"auto","created_at":"2025-12-02 12:56:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2761582,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8180128/v1/7e8c3a49-8c72-4fa9-ac81-c8f7148150c7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Fatigue Crack Growth Curves for Material Selection, Design and Service-Life Studies of Carbon-Fibre Reinforced-Plastic Composites: Effect of Test Temperature and R-ratio","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe third volume of the Composite Materials Handbook [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] provides methodologies and lessons learned for the design, analysis, manufacture and field support of fibre-reinforced, polymeric-matrix composite structures. It notes that the assessment of cyclic fatigue crack growth in such composite structures, with special relevance to airframes, can be based on either a \u0026lsquo;no-growth\u0026rsquo; or a \u0026lsquo;slow-growth\u0026rsquo; design philosophy. Whilst the most widely used approach is to adopt a \u0026lsquo;no-growth\u0026rsquo; approach, it is accepted that this can result in an overly conservative design. Furthermore, it is now known [\u0026lrm;3,4] that, even for such \u0026lsquo;no-growth\u0026rsquo; designs, delamination growth arising from cyclic fatigue loads can still arise in-service. As such two of the immediate challenges facing the composites community are:\u003c/p\u003e\u003cp\u003e\u003col style=\"list-style-type:lower-roman;\"\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo extend the \u0026lsquo;no-growth\u0026rsquo; design philosophy to allow for the nucleation and growth of small naturally-occurring delaminations in composite structures.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo develop the tools needed to provide a conservative estimate for the remaining life, and hence the inspection intervals, for delamination damage found in an operational airframe. (These the tools are needed for aircraft sustainment.)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThe principle of \u0026lsquo;slow-growth\u0026rsquo; in fibre-reinforced polymeric-matrix composites is applicable to delamination growth under cyclic fatigue loads which is slow and stable. It is recommended [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] that this be analysed using linear-elastic fracture-mechanics (LEFM). This requires a knowledge of the \u003cem\u003eda/dN\u003c/em\u003e versus \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\sqrt{G}\\:\\)\u003c/span\u003e\u003c/span\u003ecurves, where \u003cem\u003eda/dN\u003c/em\u003e is the fatigue crack (i.e. delamination) growth (FCG) per cycle and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\sqrt{G}\\)\u003c/span\u003e\u003c/span\u003e is the\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\)\u003c/span\u003e\u003c/span\u003erange of the applied energy release-rate in the fatigue cycle, as defined by\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\sqrt{{G}_{max}}-\\:\\sqrt{{G}_{min}}\\)\u003c/span\u003e\u003c/span\u003e; where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{G}_{max}\\:\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{G}_{min}\\:\\)\u003c/span\u003e\u003c/span\u003eare the maximum and minimum values of the applied energy release-rate in the fatigue cycle, respectively [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Furthermore, the NASA Fracture Design Handbook NASA-HDBK-5010 [\u0026lrm;6] mandates the use of a \u0026lsquo;worst-case upper-bound\u0026rsquo; FCG curve for material selection, design and service-life (sustainment) studies.\u003c/p\u003e\u003cp\u003eThis worst-case upper-bound FCG curve for a carbon-fibre reinforced-plastic (CFRP) should:\u003c/p\u003e\u003cp\u003e\u003col style=\"list-style-type:lower-alpha;\"\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eExhibit no, or only minimal, retardation, arising from fibre bridging. Since the fastest growing, i.e. lead, delaminations that arise under cyclic-fatigue loading of real composite structures or components from mis-drilled holes, ply drop-offs and impact damage show no, or only little, retardation from fibre-bridging developing across the faces of the delamination as the fatigue crack advances [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eAllow for scatter in the test data [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eEncompass, and bound, all the experimental results.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eBe representative of, and applicable to, small, naturally-occurring delaminations [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eHowever, the experimental determination of valid and relevant \u003cem\u003eda/dN\u003c/em\u003e versus \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\sqrt{G}\\:\\)\u003c/span\u003e\u003c/span\u003eFCG curves is where major problems have been encountered, which have only relatively recently been identified and resolved. These problems arise from the use of the double-cantilever beam (DCB) test specimen, see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, to experimentally measure the Mode I (i.e. opening tensile) \u003cem\u003eda/dN\u003c/em\u003e versus \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\sqrt{G}\\:\\)\u003c/span\u003e\u003c/span\u003eFCG curves [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This test specimen contains a through-thickness crack and has an inserted polymer film to act as a starter crack of length, \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e, for the crack from which the FCG measurements will be subsequently taken. However, this polymer film, although typically only about 10 to 13 \u0026micro;m in thickness, acts as relatively blunt crack and must be extended to a length \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e so as to give a naturally-sharp crack tip for the subsequent actual measurements. Unfortunately, this requirement for some pre-test crack extension leads to fibre bridging developing across the crack faces, prior to the start of the fatigue test, which subsequently retards the propagation of the delamination under the cyclic fatigue loads. Indeed, the experimental data reveals that such retardation effects cannot be avoided. The experimental data also reveals that the DCB fatigue test results may show a great deal of scatter [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], which in-part arises from fibre-bridging developing during the test. It is, therefore, very difficult to experimentally determine a worst-case upper-bound FCG curve. The same comments are true with respect to determining a valid value of the fatigue threshold, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\sqrt{{G}_{thr}}\\)\u003c/span\u003e\u003c/span\u003e, below which no significant FCG occurs. Finally, in the FCG of metallic structures it is well established that small, embedded, naturally-occurring defects do not behave in the same manner as relatively large through-thickness cracks as used in a typical test specimen [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The problem being that such small, naturally-occurring, cracks grow faster than expected from observations of the latter type of through-thickness cracks. In the DCB test only though-thickness cracks can be employed. Consequently, the FCG curves developed using DCB tests are not applicable to a sustainment assessment.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo examine in detail these problems of measuring the FCG rate curves from DCB tests using CFRPs, the European Structural Integrity Society (ESIS), Technical Committee (TC) 4 (on Polymers, Composites and Adhesives) has launched a round-robin study, using Mode I DCB tests, into (a) the effect of the pre-crack length, \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-a\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e, prior to the start of the measurements from the laboratory cyclic-fatigue test, on the measured FCG rate, \u003cem\u003eda\u003c/em\u003e/\u003cem\u003edN\u003c/em\u003e, data and (b) the reproducibility of the FCG rate curves under cyclic-fatigue loads. The first paper from this study [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] confirmed that a key experimental parameter is the value of the pre-crack (i.e. pre-delamination) extension length, \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-a\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e, in the DCB test specimen prior to any cyclic-fatigue measurements being undertaken. As mentioned above, this phenomenon arises since varying the value of \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-a\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e typically leads to a varying degree of fibre bridging developing behind the tip of the fatigue delamination, prior to the start of the actual fatigue test. The presence of such fibre bridging may retard the FCG rate and so lead to an impression of enhanced fatigue behaviour that is not present when no, or very, little fibre bridging occurs, as is typically the case in a real composite component. In this study [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], the FCG results were measured by the two independent laboratories and the results were in good agreement. In a second paper [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] it was shown that the Hartman-Schijve equation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], determined with test data generated [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] under an \u003cem\u003eR\u003c/em\u003e-ratio of \u003cem\u003eR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1, could also be used to compute the FCG curves associated with tests performed at \u003cem\u003eR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3, 0.5 and 0.7, where \u003cem\u003eR\u003c/em\u003e is the load ratio (=\u0026thinsp;\u003cem\u003eP\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e/\u003cem\u003eP\u003c/em\u003e\u003csub\u003emax\u003c/sub\u003e). More importantly, the Hartman-Schijve methodology (a) can be used to determine a worst-case upper-bound \u003cem\u003eda/dN\u003c/em\u003e versus \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\sqrt{G\\:}\\:\\)\u003c/span\u003e\u003c/span\u003eFCG curve which exhibits no retardation, (b) takes into account the experimental scatter, (c) encompasses and bounds all the experimental results and (d) can represent the fatigue growth of small, naturally-occurring delaminations in the CFRP. (The latter curves are needed for a sustainment analysis, i.e. to determine the remaining life of the airframe and hence the inspection intervals.) The main aim of the present paper is to investigate the robustness of this Hartman-Schijve methodology from [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] when the effects of test temperature together with varying the \u003cem\u003eR\u003c/em\u003e-ratio [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] are studied.\u003c/p\u003e"},{"header":"2. Theoretical","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eDue to the inhomogeneity and anisotropy of continuous-fibre composites, the energy release-rate, \u003cem\u003eG\u003c/em\u003e, approach, rather than the stress-intensity factor approach, is generally used to study delamination growth in such materials. Now, following the work of Paris et al. [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] on metals, where the range, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:K\\)\u003c/span\u003e\u003c/span\u003e, of the applied stress-intensity factor is invariably employed to analyse the data, then at first sight the most obvious and corresponding parameter against which to plot the measured rate of the FCG, \u003cem\u003eda/dN\u003c/em\u003e, for composites is the range of the applied energy release-rate, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:G\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:G={G}_{max}-{G}_{min}\\)\u003c/span\u003e\u003c/span\u003e. However, theoretical work by Sih, Paris and Irwin [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and recent work, for example by Jones et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], Yao et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and Simon et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], has revealed that the logical extension of the Paris FCG equation for metals to delamination growth in CFRPs is, in fact, to express \u003cem\u003eda/dN\u003c/em\u003e as a function of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\sqrt{G}\\)\u003c/span\u003e\u003c/span\u003e rather than ∆\u003cem\u003eG\u003c/em\u003e. Where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\sqrt{G}\\:\\)\u003c/span\u003e\u003c/span\u003eis given by:\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\varDelta\\:\\sqrt{G}=\\sqrt{{G}_{max}}-\\sqrt{{G}_{min}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFollowing these ideas, a novel empirical methodology based on using a variant of the Hartman-Schijve [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] equation has been proposed to access the \u0026lsquo;worst-case upper-bound\u0026rsquo; FCG rate curve, which may be thought of as a material-allowable property. The form proposed [e.g. 7,8,14,15,25,26] for the Hartman-Schijve equation, which is a variant of the NASGRO equation, in terms of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\sqrt{G}\\)\u003c/span\u003e\u003c/span\u003e is:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\frac{da}{dN}=D.\\:{}^{n}=\\:D.{\\left[\\frac{\\varDelta\\:\\sqrt{G}-\\:\\varDelta\\:\\sqrt{{G}_{thr}}}{\\surd\\:\\left\\{1-\\:\\sqrt{{G}_{max}}/\\sqrt{A}\\right\\}}\\right]}^{n}\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\)\u003c/span\u003e\u003c/span\u003e is the crack driving force (CDF) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] in terms of energy release-rate, i.e. the \u0026lsquo;similitude parameter\u0026rsquo;, and where \u003cem\u003eD\u003c/em\u003e and \u003cem\u003en\u003c/em\u003e are constants and \u003cem\u003eA\u003c/em\u003e is the cyclic fracture toughness. The term \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\sqrt{{G}_{thr}}\\)\u003c/span\u003e\u003c/span\u003e is defined by:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:\\:\\varDelta\\:\\sqrt{{G}_{thr}}=\\:\\sqrt{{G}_{max.thr}}-\\:\\sqrt{{G}_{min.thr}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eand the subscript \u0026lsquo;\u003cem\u003ethr\u003c/em\u003e\u0026rsquo; in Equations (\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and (\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) refers to the values at threshold, below which no significant FCG occurs. As noted above, using the Hartman-Schjive methodology it has been found possible (a) to determine a worst-case upper-bound \u003cem\u003eda/dN\u003c/em\u003e versus \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\sqrt{G\\:}\\:\\)\u003c/span\u003e\u003c/span\u003eFCG curve which exhibits no FCG retardation and (b) to take into account the experimental scatter. This worst-case curve (a) encompasses and bounds all the experimental results and (b) can be formulated to be representative of small naturally-occurring delaminations in the CFRP [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. It should be noted that, since the DCB test cannot yield a directly measured \u0026lsquo;worst-case upper-bound\u0026rsquo; FCG curve which is free from fibre-bridging, other methods of analysing the experimentally results have also been by proposed [e.g. 27]. However none of these have also considered the additional detrimental effect of small, naturally-occurring delaminations, as opposed to through-thickness delaminations, as might be found in an operational composite component or structure. This is a major aim of the present work.\u003c/p\u003e\u003cp\u003eIn the present study the cyclic fatigue test results for the unidirectional CFRP material are taken at test temperatures of -40\u003csup\u003eo\u003c/sup\u003eC, 50\u003csup\u003eo\u003c/sup\u003eC and 80\u003csup\u003eo\u003c/sup\u003eC from Yao et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and are compared to those reported for room temperature tests at 22\u003csup\u003eo\u003c/sup\u003eC reported by Michel et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. It will be shown that delamination growth in these tests can be captured using the same equation as given in Michel et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] for room temperature tests at \u003cem\u003eR\u003c/em\u003e-ratios of 0.1, 0.3. 0.5 and 0.7, viz:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:\\frac{da}{dN}=1.23.{10}^{-10}\\:{\\left[\\frac{\\varDelta\\:\\sqrt{G}-\\:\\varDelta\\:\\sqrt{{G}_{thr}}}{\\surd\\:\\left\\{1-\\sqrt{{G}_{max}}/\\sqrt{A}\\right\\}}\\right]}^{4.49}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"3. Experimental","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe CFRP was a continuous carbon-fibre epoxy-matrix polymer composite which was fabricated using a prepreg of unidirectional, continuous carbon fibres in a thermosetting epoxy polymer matrix, \u0026lsquo;M30SC/DT120\u0026prime; supplied by Delta-Tech S.p.A., Altopascio, Italy. (A unidirectional, continuous fibre CFRP was chosen since this is the orientation of the fibres given in the ISO Standard test method [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and this fibre orientation typically results in a severe degree of fibre-bridging occurring.) In all cases, a thin film of poly(tetrafluoroethylene), 12.7 \u0026micro;m in thickness, was inserted at one end of the DCB test specimen between the plies in the mid-plane of the CFRP panel during the hand lay-up process to act as an initial delamination, or \u0026lsquo;starter crack\u0026rsquo;, see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; where the nominal length, \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e, is about 50 mm. The laid-up panels were placed in an autoclave and cured for 90 minutes at a temperature of 120\u0026deg;C under a pressure of 6 bar. The glass transition temperature of the resulting thermosetting epoxy polymer matrix was 120\u0026deg;C.\u003c/p\u003e\u003cp\u003eThe cyclic-fatigue tests were undertaken according to the test protocol outlined in [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This protocol involved first growing the crack under quasi-static loading until a clearly visible stable crack extension has been observed, which should be a relatively short distance, i.e. less than 3.0 mm, from the initial delamination, \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e. This new pre-crack extension length, was termed \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e-\u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e. Fatigue cycling was then started, under displacement control, with a deformation range where the maximum deformation was equal to the value reached in the preceding quasi-static loading test. (It should be noted that, if a pre-crack extension length of \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-a\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e is not used in the fracture mechanics test, then optimistically high values of the quasi-static toughness and fatigue resistance will be measured. This is because the starter crack film of length \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e represents a relatively blunt crack tip compared to that of a pre-crack of length \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e, which is naturally-grown ahead of the starter film prior to the commencement of the test.) For any given DCB specimen, the test method that was employed involved growing the crack under cyclic-fatigue loading, at a relatively low frequency of 5 Hz, to avoid heating effects, for a relatively short distance from the initial value of the pre-crack extension length, \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-a\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e, whilst taking readings of the number of cycles, \u003cem\u003eN\u003c/em\u003e, crack length, \u003cem\u003ea\u003c/em\u003e, load, \u003cem\u003eP\u003c/em\u003e, and displacement, \u003cem\u003eδ\u003c/em\u003e. This fatigue test was then halted and the fatigue testing was repeated, but now with respect to the new, longer crack that was present in the DCB specimen, i.e. the crack length \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-a\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e that was now present was taken to be the relevant initial value for this repeated fatigue test. Thus, a key variable when testing a given DCB test is the value of the pre-crack extension length, \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-a\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e, prior to measurements being taken for any cyclic-fatigue test. For these fatigue tests [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], the test temperature was \u0026minus;\u0026thinsp;40\u003csup\u003eo\u003c/sup\u003e, 50\u003csup\u003eo\u003c/sup\u003e or 80\u003csup\u003eo\u003c/sup\u003e C and the value of the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:R\\)\u003c/span\u003e\u003c/span\u003e-ratio used was 0.1 or 0.5. The measured data were employed to calculate, for a given DCB test at a given value of the crack extension length, \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-a\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e, the FCG rate, \u003cem\u003eda\u003c/em\u003e/\u003cem\u003edN\u003c/em\u003e, using the \u0026lsquo;incremental polynomial method\u0026rsquo; [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Also, the values of the maximum and minimum energy release-rate, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{G}_{max}\\:\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{G}_{min}\\)\u003c/span\u003e\u003c/span\u003e, applied in a fatigue cycle were deduced from the maximum and minimum applied measured loads,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{P}_{max}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{min},\\)\u003c/span\u003e\u003c/span\u003e respectively, using the \u0026lsquo;modified compliance method\u0026rsquo; as described in detail in the ISO Standard [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFinally, it should be noted that experimental results reveal that the DCB fatigue test may show a great deal of scatter [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], which in-part arises from fibre-bridging developing during the test, as noted above. Another source of possible scatter in the experimental data arises from the fact that changes in the fracture or fatigue delamination resistance of fibre-reinforced polymer-matrix composites may occur from the absorption of moisture from the respective ambient humidity [\u003cspan additionalcitationids=\"CR29 CR30\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Thus, tests results obtained outside of a controlled laboratory climate, e.g. 23\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C and 50\u0026thinsp;\u0026plusmn;\u0026thinsp;5% relative humidity [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], at different temperatures without controlling the ambient humidity may therefore be affected by the level of moisture. In the present tests [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] the relative humidity in the temperature chamber was not reported.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Overview\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn the present section, the experimental results from the tests conducted [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] at -40\u003csup\u003eo\u003c/sup\u003e, 50\u003csup\u003eo\u003c/sup\u003e and 80\u003csup\u003eo\u003c/sup\u003eC and at the \u003cem\u003eR\u003c/em\u003e-ratios of 0.1 and 0.5, and reinterpreted according to the theoretical scheme discussed above, for the CFRP are first considered, see Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e to \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The use of the Hartman-Schijve Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), to replot all these data to obtain a unique, linear, master relationship is then presented and these data are compared to the previous results [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] for the same type of CFRP obtained at 22\u003csup\u003eo\u003c/sup\u003eC, see Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. The parameters deduced from this approach are then used to compute the experimentally measured results that are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e to \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Finally, the \u0026lsquo;worst-case upper-bound FCG curves\u0026rsquo;, for a small, naturally-occurring delamination present in a structure or component using this type of CFRP, are calculated using the Hartman-Schijve methodology and are compared to the experimentally measured results, see Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Experimental plots of the logarithmic \u003cem\u003eda\u003c/em\u003e/\u003cem\u003edN\u003c/em\u003e versus the logarithmic \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\sqrt{G}\\)\u003c/span\u003e\u003c/span\u003e\u003c/h2\u003e\u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e values of the logarithmic \u003cem\u003eda\u003c/em\u003e/\u003cem\u003edN\u003c/em\u003e versus the logarithmic \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\sqrt{G}\\)\u003c/span\u003e\u003c/span\u003e calculated from the tests reported in [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] at -40\u003csup\u003eo\u003c/sup\u003eC for an \u003cem\u003eR\u003c/em\u003e-ratio of 0.5 for the CFRP composites are given as a function of the pre-crack extension length, \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-a\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e, prior to the start of measurements from the DCB fatigue test.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAs may be seen from the results shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the FCG curves move steadily to the right as the pre-crack extension length, \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-a\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e, prior to measurements being taken for the test is increased. This implies that there is a retardation of the FCG rate as the value of \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-a\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e is increased. That is, as the value of \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-a\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e increases, and for a given value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\sqrt{G}\\)\u003c/span\u003e\u003c/span\u003e, the corresponding value of \u003cem\u003eda\u003c/em\u003e/\u003cem\u003edN\u003c/em\u003e is lower; so, at a given value of\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\varDelta\\:\\sqrt{G}\\)\u003c/span\u003e\u003c/span\u003e, the fatigue crack grows slower as the value of \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-a\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e is increased. These observations arise from the extent of fibre bridging in the DCB test specimen being more extensive as the pre-crack extension length, \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-a\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e, is increased, since this retards the rate of delamination growth at a given value of\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\varDelta\\:\\sqrt{G}\\)\u003c/span\u003e\u003c/span\u003e. It is very important to note that such results as in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e agree in general form with those from previous studies [e.g. 8,13\u0026ndash;15,25,26] and clearly reveal that there is not one unique FCG rate curve. Instead, a number of such FCG rate curves may be obtained depending upon the chosen value of the pre-crack extension length, \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-a\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e, at the start of the test, prior to measurements being taken from that given cyclic-fatigue test. The results shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e to \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e for the other test temperatures and \u003cem\u003eR\u003c/em\u003e-ratios all reveal similar effects. Furthermore, by comparing Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for \u0026minus;\u0026thinsp;40\u003csup\u003eo\u003c/sup\u003eC, Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e for 50\u003csup\u003eo\u003c/sup\u003eC and Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e for 80\u003csup\u003eo\u003c/sup\u003eC, it may be seen that for the DCB tests, at a given value of the pre-crack extension length, \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-a\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e, then when subjected to the higher \u003cem\u003eR\u003c/em\u003e-ratio of 0.5, as opposed to 0.1, a somewhat higher value of \u003cem\u003eda/dN\u003c/em\u003e is recorded for a given value of the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\sqrt{G}\\)\u003c/span\u003e\u003c/span\u003e. This is as expected [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], since an increase in the \u003cem\u003eR-\u003c/em\u003eratio increases the mean stress and therefore for a given value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\sqrt{G}\\)\u003c/span\u003e\u003c/span\u003e, at a given value of \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-a\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e, a higher \u003cem\u003eR\u003c/em\u003e-ratio leads to a higher FCG rate, \u003cem\u003eda/dN\u003c/em\u003e. (The effect of test temperature on the FCG behaviour is discussed below.)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Use of the Hartman-Schijve Equation\u003c/h2\u003e\u003cp\u003eTo bring all the results shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e to \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e together in one \u0026lsquo;master\u0026rsquo; plot, the use of the Hartman-Schijve methodology, more specifically Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), is next examined to investigate whether the experimental results shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e to \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e can be replotted to give a unique, linear, master curve so that the constants \u003cem\u003eD\u003c/em\u003e and \u003cem\u003en\u003c/em\u003e may be ascertained. To achieve this, the values of \u003cem\u003eA\u003c/em\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\sqrt{{G}_{thr}}\\)\u003c/span\u003e\u003c/span\u003e are chosen so as to ensure that Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) best fits an experimental set of test data of logarithmic \u003cem\u003eda\u003c/em\u003e/\u003cem\u003edN\u003c/em\u003e versus logarithmic \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\sqrt{G}\\:\\)\u003c/span\u003e\u003c/span\u003eover the entire range of FCG rates. As in [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], the values of \u003cem\u003eA\u003c/em\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\sqrt{{G}_{thr}}\\:\\)\u003c/span\u003e\u003c/span\u003ewere determined using the \u0026lsquo;Total Least Squares\u0026rsquo; methodology described in [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], so that, for a given set of test data at a given value of \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e-\u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e, the logarithmic \u003cem\u003eda\u003c/em\u003e/\u003cem\u003edN\u003c/em\u003e versus logarithmic \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left[\\frac{\\varDelta\\:\\sqrt{G}-\\:\\varDelta\\:\\sqrt{{G}_{thr}}}{\\surd\\:\\left\\{1-\\:\\sqrt{{G}_{max}}/\\sqrt{A}\\right\\}}\\right]\\:\\)\u003c/span\u003e\u003c/span\u003eplots became virtually linear. After the individual linear relationship for a given set of test data points had been determined, a combined plot of each of the different tests was assembled, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. Here, it can be seen that, allowing for experimental error, all the resultant plots enable a single, linear, master relationship to be defined when using logarithmic axes. This unique, linear, master relationship, i.e. as in Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), holds for the over forty sets of data shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. The value of the linear coefficient of determination, R\u003csup\u003e2\u003c/sup\u003e, for this linear plot is 0.93. This is somewhat lower than found previously [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] due to the relatively high scatter observed in the test conducted at 80\u003csup\u003eo\u003c/sup\u003eC at an \u003cem\u003eR\u003c/em\u003e ratio of 0.1 with \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-a\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e being 90.6 mm, see Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Notwithstanding, the linear fit for the present set of test data is in very good agreement with that from previous work [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], as may be seen from Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, and the values of \u003cem\u003eD\u003c/em\u003e and \u003cem\u003en\u003c/em\u003e for the present and previous tests [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] are 1.23 x 10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e and 4.49, respectively. These values are used below (a) to compute the logarithmic \u003cem\u003eda\u003c/em\u003e/\u003cem\u003edN\u003c/em\u003e versus logarithmic \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\sqrt{G}\\)\u003c/span\u003e\u003c/span\u003e relationships as a function of \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-a\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e, together with the respective \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:{\\sqrt{G}}_{thr}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\surd\\:A\\)\u003c/span\u003e\u003c/span\u003e values and (b) to predict the worst-case, upper-bound FCG rate curves for a small naturally-occurring delamination, together with the appropriate mean value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:{\\sqrt{G}}_{thr}\\)\u003c/span\u003e\u003c/span\u003e (taken to be zero) and the quasi-static value of the Mode I (tensile) interlaminar fracture energy, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{G}_{co}\\)\u003c/span\u003e\u003c/span\u003e, at the onset of crack growth, as described below.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Computed plots of the logarithmic \u003cem\u003eda\u003c/em\u003e/\u003cem\u003edN\u003c/em\u003e versus the logarithmic \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\sqrt{G}\\)\u003c/span\u003e\u003c/span\u003e\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eEquation (\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) with the values of the parameters needed to fit the Hartman-Schijve relationship, see above, can now be used to compute the full experimental curve of logarithmic \u003cem\u003eda\u003c/em\u003e/\u003cem\u003edN\u003c/em\u003e versus logarithmic \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\sqrt{G}\\)\u003c/span\u003e\u003c/span\u003e. The computed relationships are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e to \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and, as may be seen, there is excellent agreement between the experimental data and the computed curves from the Hartman-Schijve methodology. Indeed, the values of the coefficients of determination, R\u003csup\u003e2\u003c/sup\u003e, associated with a comparison of the measured and computed curves shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e were determined and have a mean of about 0.90 and a standard deviation of 0.08. Therefore, despite the obvious and expected scatter in the experimental measurements, the values of R\u003csup\u003e2\u003c/sup\u003e for the various computed curves are relatively high, thereby confirming the good agreement between the experimental and theoretical results.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.5. The worst-case upper-bound FCG curve for sustainment\u003c/h2\u003e\u003cp\u003eLet us next address the question of how to determine the worst-case upper-bound FCG curve of \u003cem\u003eda/dN\u003c/em\u003e versus \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\sqrt{G}\\)\u003c/span\u003e\u003c/span\u003e that is needed to assess the FCG rate for a small naturally-occurring delamination that may arise in an operational airframe, i.e. for a sustainment assessment of the airframe. To undertake this calculation we use Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and take from Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e the values of the constants \u003cem\u003eD\u003c/em\u003e and \u003cem\u003en\u003c/em\u003e to be 1.23 x 10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e and 4.49, which are in agreement with previous work for this CFRP [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Next, we need the value \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\sqrt{{G}_{thr}}\\)\u003c/span\u003e\u003c/span\u003e, and following previous convention [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] we set the value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\sqrt{{G}_{thr}}\\)\u003c/span\u003e\u003c/span\u003e to be zero.\u003c/p\u003e\u003cp\u003eThis only leaves the question of what value to use as the worst-case value of the apparent cyclic fracture toughness, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:A\\)\u003c/span\u003e\u003c/span\u003e. However, since we are dealing with small, naturally-occurring cracks, and since as shown in [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], the value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{G}_{co}\\)\u003c/span\u003e\u003c/span\u003eappears to be essentially independent of the test temperature, the logical approach is to use the worst-case [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] of the \u0026lsquo;mean-3\u003cem\u003eσ\u003c/em\u003e\u0026rsquo; value of the quasi-static initiation term, termed \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{G}_{co,3\\sigma\\:}\\)\u003c/span\u003e\u003c/span\u003e. Noting that the mean value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{G}_{co}\\)\u003c/span\u003e\u003c/span\u003e given in [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] is 250 J/m\u003csup\u003e2\u003c/sup\u003e with a standard deviation, \u003cem\u003eσ\u003c/em\u003e, of \u0026plusmn;\u0026thinsp;45 J/m\u003csup\u003e2\u003c/sup\u003e, this yields a worst-case value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sqrt{A}\\:\\)\u003c/span\u003e\u003c/span\u003eof 10.7 \u0026radic;(J/m\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eNow, in Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e the values of logarithmic \u003cem\u003eda\u003c/em\u003e/\u003cem\u003edN\u003c/em\u003e versus logarithmic \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\sqrt{G}\\)\u003c/span\u003e\u003c/span\u003e are plotted again for the tests performed at -40\u003csup\u003eo\u003c/sup\u003e, 50\u003csup\u003eo\u003c/sup\u003e and 80\u003csup\u003eo\u003c/sup\u003eC for the CFRP for the two \u003cem\u003eR\u003c/em\u003e-ratios of 0.1 and 0.5, respectively. Values are given in the legend for the pre-crack extension length, \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e-\u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e, prior to the start of measurements from the DCB fatigue test specimen. It may be seen that the experimental data reveals that the FCG rate can accelerate with elevated temperatures but decrease at sub-zero temperatures. However, the dependence of the FCG curve at a given value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\sqrt{G}\\)\u003c/span\u003e\u003c/span\u003e on the pre-crack extension length, \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e-\u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e, greatly complicates the interpretation and assessment of the overall effect of test temperature.\u003c/p\u003e\u003cp\u003eThe resultant worse-case FCG curves for a small naturally-occurring delamination that may arise in an operational airframe were calculated as described above and are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, where they are labelled the \u0026lsquo;Small delam curve\u0026rsquo;. Several interesting points arise concerning these worst-case curves. Firstly, these worst-case FCG curves shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e are in very good agreement with that calculated for the previous tests conducted [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] at 22\u003csup\u003eo\u003c/sup\u003eC at \u003cem\u003eR\u003c/em\u003e-ratios of 0.1, 0.3, 0.5 and 0.7. Secondly, as required, these predicted FCG worst-case curves shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e encompass and bound all the experimental results. Thirdly, these (sustainment) FCG curves are independent of the test temperature and the \u003cem\u003eR\u003c/em\u003e-ratio. Fourthly, the vertical lines shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e are for the case when the value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{G}_{max}\\:\\)\u003c/span\u003e\u003c/span\u003eis taken to be equal to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{G}_{co,3\\sigma\\:}\\)\u003c/span\u003e\u003c/span\u003e= \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:A\\)\u003c/span\u003e\u003c/span\u003e in Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and they represent the asymptotic values for the worst-case FCG curves for a naturally-occurring delamination. Finally, these predicted FCG curves might be considered by some to be \u0026lsquo;overly-conservative\u0026rsquo; but they are as determined in accordance with standard airframe economic life-assessment procedures, as first delineated by Lincoln et al. [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. (The paper by Lincoln [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] was the first to highlight that a sustainment analysis should use the crack growth curve associated with small naturally-occurring cracks.) Indeed, one reason, of course, for their apparent conservative appearance is that we have chosen to use the \u0026lsquo;mean-3\u003cem\u003eσ\u003c/em\u003e\u0026rsquo; value of the quasi-static initiation term, termed \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{G}_{co,3\\sigma\\:}\\)\u003c/span\u003e\u003c/span\u003e, which yields a worst-case value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:A\\)\u003c/span\u003e\u003c/span\u003e. This is termed the \u0026lsquo;A\u0026rsquo; basis approach [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In this case, the mechanical property value indicated is the value above which at least 99% of the population of values is expected to fall with a confidence of 95%. This value is used to design and predict the service-life of a member where the loading is such that its failure would result in a loss of structural integrity. This approach to ensuring structural integrity is mandated in the NASA Fracture Control Handbook NASA-HDBK-15-10 [6\u0026lrm;]. If a lower number of standard deviations, \u003cem\u003eσ\u003c/em\u003e, is taken, e.g. taking minus two standard deviations as in the \u0026lsquo;B\u0026rsquo; basis approach [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], then the statistical probability of failure increases and the design becomes less conservative.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe present paper addresses the problem of fatigue crack growth in composite structures, with special relevance to airframes. Assessment of their in-service life can be based on either a \u0026lsquo;no-growth\u0026rsquo; or a \u0026lsquo;slow-growth\u0026rsquo; design philosophy. Whilst the most widely used approach is to adopt a \u0026lsquo;no-growth\u0026rsquo; approach, it is accepted that this can result in an overly conservative design. Furthermore, even for \u0026lsquo;no-growth\u0026rsquo; designs, delamination growth arising from cyclic fatigue loads can still arise in-service. Thus, two of the immediate challenges facing the composites community are to extend the \u0026lsquo;no-growth\u0026rsquo; design philosophy to allow for the nucleation and growth of small naturally-occurring delaminations and to develop the tools needed to determine the remaining life of a damaged airframe, and hence the necessary inspection intervals. The main aim of the present paper has been therefore to investigate the robustness of the Hartman-Schijve methodology to meet this challenge when the effects of test temperature, together with the \u003cem\u003eR\u003c/em\u003e-ratio, on the fatigue behaviour are considered.\u003c/p\u003e\u003cp\u003eStudies have been published in the literature [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] on the cyclic fatigue crack growth (FCG) behaviour, as a function of both test temperature and \u003cem\u003eR\u003c/em\u003e-ratio, of a continuous carbon-fibre epoxy-matrix plastic (CFRP) composite which was fabricated using a prepreg of unidirectional, continuous carbon fibres in a thermosetting epoxy polymer matrix. We have re-interpreted these data such that a Hartmann-Schjive approach [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] may be adopted. Hence, worse-case FCG curves for a small naturally-occurring delamination that may arise in an operational airframe have been deduced. These curves, which are obtained by following an approach that reflects the USAF-Boeing approach [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and that mandated in NASA-HDBK-1510 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], are in good agreement with previous results [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] for tests conducted at 22\u003csup\u003eo\u003c/sup\u003eC at \u003cem\u003eR\u003c/em\u003e-ratios of 0.1, 0.3, 0.5 and 0.7. Further, as required, these predicted worst-case, small naturally-occurring delamination FCG curves encompass and bound all the experimental results; and they are independent of the test temperature and the \u003cem\u003eR\u003c/em\u003e-ratio. Such curves provide the data needed for a \u0026lsquo;slow-growth\u0026rsquo; design philosophy to be adopted and for the remaining life and inspection intervals to be determined. Otherwise the detection of delamination damage in an operational aircraft will lead to the need to either immediately repair or to replace the component.\u003c/p\u003e"},{"header":"Nomenclature","content":"\u003cp\u003e\u003cimg 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\" width=\"541\" height=\"740\"\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis work did not receive external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAndreas J. Brunner : Review \u0026amp; Editing. Anthony J. Kinloch : Validation, Writing, Review \u0026amp; Editing. Rhys Jones : Analysis and Methodology, Review \u0026amp; Editing.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThe inputted experimental data has been previously published [18,19] and the authors would wish to thank Dr. L.Yao for making some of it available to Professor Jones in a spreadsheet format.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eISO Standard 15024: Fibre-reinforced plastic composites - Determination of mode I interlaminar fracture toughness, G\u003csub\u003eIc\u003c/sub\u003e, for unidirectionally reinforced materials. 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(2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\[email protected]\u003c/span\u003e\u003cspan address=\"http://[email protected]\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMichel, S., Murphy, N., Kinloch, A.J., Jones, R.: On cyclic-fatigue crack growth in carbon-fibre-reinforced epoxy-polymer composites. Polymers. \u003cb\u003e16\u003c/b\u003e, 435 (2024)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMichel, S., Kinloch, A.J., Jones, R.: Cyclic-fatigue crack growth in polymer composites: Data interpretation via the Hartman-Schijve Methodology. Eng. Fract. Mech. \u003cb\u003e314\u003c/b\u003e, 110743 (2025)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHartman, A., Schijve, J.: The effects of environment and load frequency on the crack propagation law for macro FCG in aluminum alloys. Eng. Fract. Mech. \u003cb\u003e1\u003c/b\u003e, 615\u0026ndash;631 (1970)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchwalbe, K.H.: On the beauty of analytical models for fatigue crack propagation and fracture. A personal historical review. J. ASTM Int. \u003cb\u003e7\u003c/b\u003e, 3\u0026ndash;73 (2010)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYao, L., Chuai, M., Liu, J., Guo, L., Chen, X., Alderliesten, R.C., Beyens, M.: Fatigue delamination behavior in composite laminates at different stress ratios and temperatures. Int. J. Fatigue. \u003cb\u003e175\u003c/b\u003e, 107830 (2023)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYao, L., Chuai, M., Li, H., Chen, X., Quan, D., Alderliesten, R.C., Beyens, M.: Temperature effects on fatigue delamination behavior in thermoset composite laminates. Eng. Fract. 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Mech. \u003cb\u003e1\u003c/b\u003e, 189\u0026ndash;203 (1965)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJones, R., Kinloch, A.J., Hu, W.: Cyclic-FCG in composite and adhesively-bonded structures: The FAA slow crack growth approach to certification and the problem of similitude. Int. J. Fatigue. \u003cb\u003e88\u003c/b\u003e, 10\u0026ndash;18 (2016)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYao, L., Alderliesten, R.C., Jones, R., Kinloch, A.J.: Delamination fatigue growth in polymer-matrix fibre composites: a methodology for determining the design and lifing allowables. Compos. Struct. \u003cb\u003e96\u003c/b\u003e, 8\u0026ndash;20 (2018)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSimon, I., Banks-Sills, L., Fourman, V.: Mode I delamination propagation and R-ratio effects in woven DCB specimens for a multi-directional layup. Int. J. Fatigue. \u003cb\u003e96\u003c/b\u003e, 237\u0026ndash;251 (2017)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlderliesten, R.C.: Fatigue delamination of composite materials - approach to exclude large scale fibre bridging. IOP Conf. Series: Mater. Sci. Eng. \u003cb\u003e388\u003c/b\u003e, 012002 (2018)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePing, J., De Wang, X.T.: Review of hygrothermal effects on carbon fiber-reinforced composite materials reinforced composite materials. Adv. Mater. Res., 490\u0026ndash;495, 3611\u0026ndash;3615. (2012)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShettar, M., Chaudhary, A., Hussain, Z., Achutha Kini, U., Sharma, U.: S., Hygrothermal studies on GFRP Composites: A Review. MATEC Web of Conferences, 144, 02026. 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International Standards Organisation, Geneva, Switzerland, (1997)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIliopoulos, A.P., Michopoulos, J.G., Jones, R., Kinloch, A.J., Peng, D.: A framework for automating the parameter determination of crack growth models. Int. J. Fatigue. \u003cb\u003e169\u003c/b\u003e, 107490 (2023)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNiu, M.C.Y.: Composite Airframe Structures: Practical Design Information and Data. Conmilit, Hong Kong (1992)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRouchon, J., Fatigue, and Damage Tolerance Evaluation of Structures: : The Composite Materials Response. 22nd Plantema Memorial Lecture, 25th ICAF Symposium, Hamburg, Germany, Rotterdam, The Netherlands, National Aerospace Laboratory NLR, NLR-TP-2009-221, (2009)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLincoln, J.W., Melliere, R.A.: Economic life determination for a military aircraft. AIAA J. Aircr. \u003cb\u003e36\u003c/b\u003e, 737\u0026ndash;742 (1999)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"applied-composite-materials","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"acma","sideBox":"Learn more about [Applied Composite Materials](http://link.springer.com/journal/10443)","snPcode":"10443","submissionUrl":"https://submission.nature.com/new-submission/10443/3","title":"Applied Composite Materials","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"CFRP, composites, fatigue, fracture mechanics, modelling, service-life, test temperature","lastPublishedDoi":"10.21203/rs.3.rs-8180128/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8180128/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe present paper addresses the problem of fatigue crack growth in composite structures, with special relevance to composite airframes. Assessment of their in-service life can be based on either a \u0026lsquo;no-growth\u0026rsquo; or a \u0026lsquo;slow-growth\u0026rsquo; design philosophy. Whilst the most widely used approach is to adopt a \u0026lsquo;no-growth\u0026rsquo; approach, it is accepted that this can result in an overly conservative design. Furthermore, even for \u0026lsquo;no-growth\u0026rsquo; designs, delamination growth under cyclic fatigue loads can still arise in-service. Thus, the immediate challenge facing the composites community is to extend the \u0026lsquo;no-growth\u0026rsquo; design philosophy to allow for the nucleation and growth of small, naturally-occurring delaminations in composite structures. The main aim of the present paper is, therefore, to investigate the robustness of the Hartman-Schijve methodology to meet this challenge when the effects of test temperature, together with the \u003cem\u003eR\u003c/em\u003e-ratio, on the fatigue behaviour are considered.\u003c/p\u003e","manuscriptTitle":"Fatigue Crack Growth Curves for Material Selection, Design and Service-Life Studies of Carbon-Fibre Reinforced-Plastic Composites: Effect of Test Temperature and R-ratio","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-28 11:22:18","doi":"10.21203/rs.3.rs-8180128/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-18T15:17:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-18T10:27:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"81526130752309299799540040743434680806","date":"2026-02-02T10:23:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313740876569337428592605906167079005396","date":"2025-12-15T23:26:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-10T07:26:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"166778531956682877932924468126374751108","date":"2025-12-02T11:30:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17211094196389052172049101194910839586","date":"2025-11-25T00:25:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-24T12:48:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-24T12:22:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-24T12:20:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Applied Composite Materials","date":"2025-11-22T11:23:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"applied-composite-materials","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"acma","sideBox":"Learn more about [Applied Composite Materials](http://link.springer.com/journal/10443)","snPcode":"10443","submissionUrl":"https://submission.nature.com/new-submission/10443/3","title":"Applied Composite Materials","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"42c3359f-11aa-41be-81b8-0e86c57bf2c6","owner":[],"postedDate":"November 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-21T09:54:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-28 11:22:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8180128","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8180128","identity":"rs-8180128","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}