Fatigue damage analysis: Issues and challenges

Sadananda, K.; Iyyer, Nagaraja

doi:10.1016/j.ijfatigue.2015.08.026

Cited by 27 publications

(12 citation statements)

References 29 publications

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“…Fatigue crack growth simulation software packages such as NASGRO (NASGRO) are commonly used for estimation of fatigue crack parameters. However, fatigue life and crack length estimates have been found to be wrong by a factor of as much as tenfold (Haile, 2012 andVasudevan, 2016). The main contributing factor of such large errors is the use of equivalent initial flaw size (EIFS) (Newman, 2016).…”

Section: Background and Motivationmentioning

confidence: 99%

Machine Learning Based Prognostics of Fatigue Crack Growth in Notch Pre-cracked Aluminum 7075-T6 Rivet Hole

2018

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Constant stress amplitude fatigue tests were conducted on the notch pre-cracked Aluminum 7075-T6 rivet hole dog-bone coupons. Monitoring of visible surface crack length by special surface engraving using digital microscope images and by ultrasonic sensors signals was carried out to yield fatigue crack length measurements in relation to number of fatigue cycles applied. The experimental results provide ultrasonic sensor validation for fatigue crack length measurements. Fracto-graphic examination of failed fatigue surfaces has provided further confirmation of notch pre-crack length, crack initiation process, and crack growth marker bands. These experimental inputs were used in NASGRO and AFGROW software fatigue crack growth simulations. The simulation results did not match the crack initiation fatigue life measured by experiments. However, there was good agreement with crack growth simulations of larger cracks. Hence, we plan to develop a machine learning application that will learn the fatigue crack initiation and crack growth processes from data obtained from our own experiments and other fatigue data available from AFGROW databases. Nonlinear AutoRegressive models with eXogenous input (NARX) artificial neural network were used to predict crack growth longer than 5.0-mm. Particle filtering modeling with Bayesian updating was applied to these experimental data for prognostics of fatigue crack growth. A concept design and preliminary implementation results will be presented.

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Section: Background and Motivationmentioning

confidence: 99%

Machine Learning Based Prognostics of Fatigue Crack Growth in Notch Pre-cracked Aluminum 7075-T6 Rivet Hole

2018

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“…Dengan menggunakan persamaan garis miring dari kurva S-N yang diperoleh hasil pengujian dapat diprediksi umur dari material AISI 1045 dengan terlebih dahulu mengetahui tegangan yang terjadi pada material tersebut. Pada pembebanan fatik, retak dapat dipastikan berasal dari pembebanan atau tegangan yang diterima oleh material yang berawal dari permukaan yang kemudian bergerak perlahan-lahan menuju ke dalam dan terjadi pada suatu titik yang memiliki konsentrasi tegangan yang tinggi 9,10) .. Komponen yang tidak memiliki cacat yang mengalami pembebanan dinamis, tidak mengalami kerusakan pada keseluruhan penampang dari komponen tersebut tetapi kerusakan terjadi berawal dari permukaan, dengan berjalannya waktu dan pembebanan, retak tersebut merambat.…”

Section: Hasil Dan Pembahasanunclassified

Pengaruh Penghilangan Kekasaran Permukaan Terhadap Kekuatan Fatik = Effect of Surface Roughness Removal to Fatigue Strength

Suhartono¹

2023

Majalah Ilmiah Pengkajian Industri

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AbstrakTujuan penelitian ini adalah untuk menyelidiki dan membuktikan bahwa kegagalan kelelahan baja dimulai dari permukaan. Oleh karena itu tindakan pencegahan dengan memperhalus permukaan sangat penting untuk mencegah beban kelelahan baja. Spesimen dari AISI 1045 Steel dimuat dengan cara uji kelelahan lentur putar. Kelelahan pemuatan akan disela sebagai umur kelelahan mencapai 50% dari umur kelelahan dan 75% dari umur kelelahan. Selama gangguan lainnya yang spesimen akan digiling dan dipoles, sebelum diuji benarbenar sampai patah akibat kelelahan yang terjadi. Umur kelelahan dari setiap kelompok specimen diuji berdasarkan beban akan dibandingkan dengan spesimen oleh kelelahan bongkar tanpa aturan.The Miner rule digunakan untuk mengevaluasi hasil tes. Pengaruh gangguan lainnya dan perlakuan permukaan dievaluasi dan dianalisis. Kata kunci : Fatik, kurva S-N, Kualitas permukaan Abstract PENDAHULUANKetahanan fatik suatu komponen dipengaruhi oleh kualitas permukaan. Kegagalan fatik umumnya bermula dari retak awal yang terjadi pada permukaan komponen. Kualitas permukaan yang baik memiliki pengaruh yang positif terhadap ketahanan fatik. Umur kerja suatu komponen akan berkurang dengan timbulnya retak.Awal retakan dapat berasal dari cacat yang terjadi pada proses fabrikasi, transportasi, perakitan dan pemasangan mesin, maupun akibat beban operasi.Proses kegagalan fatik dimulai dari nukleasi retak (awal keretakan) sampai pada kondisi akhir yaitu patah (final fracture). Pada komponen yang mengalami beban fatik (beban berulang), maka nukleasi retak lelah dapat saja terjadi diawali dengan skala

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“…1, such as length (a) and width (w), length of shielding zone (l s ), characteristic microstructural size (m s ) and the plastic zone radius (r p ) [23,[28][29][30][31]. A physically small crack (hereafter referred as small crack) is defined as a crack of length in the range between 20 µm and 1 mm [24] and is generally smaller than the typical non-destructive testing (NDT) detection limit (< 1 mm) [29,[32][33][34]. For long cracks, the concept of crack closure was introduced by Elber in 1970 [35].…”

Section: Small Crack Growth Behaviour and Modified Nasgro Equationmentioning

confidence: 99%

Simulation of fatigue small crack growth in additive manufactured Ti–6Al–4V material

Gupta

Sun

Bennett

2020

Continuum Mech. Thermodyn.

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Additive manufacturing (AM) offers design freedom and ability to fabricate parts of complex shapes which are not often possible with the conventional methods of manufacturing. In an AM part, even with optimum build parameters, a complete elimination of defects is not possible and this makes it hard to fully deploy the AM technology to build load bearing parts operating under cyclic loading conditions. Many of these defects are < 1 mm in size and are categorised as ‘small cracks’. Local interaction of cracks with microstructural features and closure effects at the wake of the crack tip are some of the factors which make the growth behaviour of small and long cracks different. A crack growth life prediction method, which effectively considers the small crack growth behaviour, has been discussed in this paper. This proposed method includes a detailed finite element-based crack growth simulation using the ANSYS SMART fracture technology. The lifing calculations utilise the modified NASGRO equation and small crack growth data which was obtained from the published long crack growth data, corrected for closure effects. The predicted stress versus number of cycles curves were compared against the fatigue test results for the AM specimens in Ti–6Al–4V material. A good correlation between the predictions and test results suggests that the proposed method can be used to assess the small crack growth life of AM parts where the fatigue effects of cyclic loading can be quite significant.

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Fatigue damage analysis: Issues and challenges

Cited by 27 publications

References 29 publications

Machine Learning Based Prognostics of Fatigue Crack Growth in Notch Pre-cracked Aluminum 7075-T6 Rivet Hole

Machine Learning Based Prognostics of Fatigue Crack Growth in Notch Pre-cracked Aluminum 7075-T6 Rivet Hole

Pengaruh Penghilangan Kekasaran Permukaan Terhadap Kekuatan Fatik = Effect of Surface Roughness Removal to Fatigue Strength

Simulation of fatigue small crack growth in additive manufactured Ti–6Al–4V material

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