Optimization of peened-surface laser shock conditions by method of finite element and technique of design of experiments

Frija, Mounir; Ayeb, Manel; Seddik, Raoudha; Fathallah, Raouf; Sidhom, Habib

doi:10.1007/s00170-018-1849-5

Cited by 19 publications

(7 citation statements)

References 34 publications

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“…Moreover, it can be seen that the compressive residual stresses are very close to the surface treated by LSP, which can represent a major interest with the aim of delaying the initiation and propagation of cracks. Converse to the circular laser spots where the maximum compressive residual stress is located in the centre of the impact (stress hole), 6 the maximum compressive residual stress generated by the square laser spot (Figures 8(b) and 9(b)) is not in the centre of the laser impact, but at a distance of about 1.5 mm from the centre along the y axis. 14 In addition to the efficiency improvement in the LSP process, the square laser spot is more likely to produce a more uniform surface (particularly using multiple layers) compared to the circular laser spot that can hardly produce a uniform surface with the use of large overlapping ratio in order to avoid an un-peened surface zone.…”

Section: Resultsmentioning

confidence: 99%

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Influence of multiple laser impacts on thin leading edges of turbine blade

Ayeb

Frija

Fathallah

2019

Proceedings of the Institution of Mechanical Engineers, Part L:

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Laser shock peening is a mechanical surface improvement treatment used to enhance the fatigue life of critical components. This paper investigates the influence of multiple square laser impacts to study their special effect on the diverse mechanical behaviours of the thin leading edge surface of turbine blades. Most works existing in the literature have presented experimental investigations. The originality of our paper is to validate and numerically simulate the proposed model. Indeed, a 3D finite element method of a thin leading edge specimen, Ti–6Al–4V, of a turbine blade is numerically simulated using the ABAQUS software. The mechanical surface modifications (residual stresses, equivalent plastic strains and Johnson–Cook superficial damage) induced by the multiple square laser impact are examined in detail. The main purpose of this investigation is to determine the effects of single-sided and double-sided laser shock peening.

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Section: Resultsmentioning

confidence: 99%

“…Wei and Ling 5 used a 3D model to analyse the effects of LSP treatments on residual stresses. Frija et al, 6 Ayeb 7 and Ayeb et al 8 employed a 2D axisymmetric model in order to optimize and numerically simulate the LSP process. Later, these effects were studied with circular laser spots.…”

Section: Introductionmentioning

confidence: 99%

Influence of multiple laser impacts on thin leading edges of turbine blade

Ayeb

Frija

Fathallah

2019

Proceedings of the Institution of Mechanical Engineers, Part L:

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“…Such a hybrid modelling approach is new in the context of LSP, where the number of publications on the application of machine learning approaches for the LSP process is scarce, overall. Frija et al [ 30 ] optimized the LSP surface conditions by using an FE model exposed to the laser-induced pressure pulse as well as Design of Experiments (DoE) to infer related laser parameters. They extended the work by the use of an ANN to efficiently predict significant characteristics of numerical compressive residual stress profiles and approximated a simplified 1st-order linear slope of residual stresses [ 31 ].…”

Section: Methodsmentioning

confidence: 99%

Hybrid Modelling by Machine Learning Corrections of Analytical Model Predictions towards High-Fidelity Simulation Solutions

et al. 2021

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Within the fields of materials mechanics, the consideration of physical laws in machine learning predictions besides the use of data can enable low prediction errors and robustness as opposed to predictions only based on data. On the one hand, exclusive utilization of fundamental physical relationships might show significant deviations in their predictions compared to reality, due to simplifications and assumptions. On the other hand, using only data and neglecting well-established physical laws can create the need for unreasonably large data sets that are required to exhibit low bias and are usually expensive to collect. However, fundamental but simplified physics in combination with a corrective model that compensates for possible deviations, e.g., to experimental data, can lead to physics-based predictions with low prediction errors, also despite scarce data. In this article, it is demonstrated that a hybrid model approach consisting of a physics-based model that is corrected via an artificial neural network represents an efficient prediction tool as opposed to a purely data-driven model. In particular, a semi-analytical model serves as an efficient low-fidelity model with noticeable prediction errors outside its calibration domain. An artificial neural network is used to correct the semi-analytical solution towards a desired reference solution provided by high-fidelity finite element simulations, while the efficiency of the semi-analytical model is maintained and the applicability range enhanced. We utilize residual stresses that are induced by laser shock peening as a use-case example. In addition, it is shown that non-unique relationships between model inputs and outputs lead to high prediction errors and the identification of salient input features via dimensionality analysis is highly beneficial to achieve low prediction errors. In a generalization task, predictions are also outside the process parameter space of the training region while remaining in the trained range of corrections. The corrective model predictions show substantially smaller errors than purely data-driven model predictions, which illustrates one of the benefits of the hybrid modelling approach. Ultimately, when the amount of samples in the data set is reduced, the generalization of the physics-related corrective model outperforms the purely data-driven model, which also demonstrates efficient applicability of the proposed hybrid modelling approach to problems where data is scarce.

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“…Recently, LSP [1][2][3][4][5][6][7][8][9][10][11] is an innovative surface treatment that is based on the application of a laser pulse (nanosecond regime, power density in GW/cm 2 ) on the surface of the treated material. It involves a hardening of the superficial layers by the propagation of a shock wave.…”

Section: Introductionmentioning

confidence: 99%

Reliability prediction of high‐cycle fatigue behavior of parts treated by laser shock peening

Frija

Ayeb

Seddik

et al. 2023

Fatigue Fract Eng Mat Struct

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The purpose of this paper is to develop a global approach to predict the reliability of the high‐cycle fatigue behavior of mechanical components treated by laser shock peening (LSP). Most works that exist in the literature have introduced experimental and numerical studies. The originality of our investigation is to predict and validate the reliability of high‐cycle fatigue of the workpieces treated by LSP treatment by using the modified Crossland criterion. Based on previous works made by our team that shows the finite element simulation of the LSP process, this approach will be based on the integration of probabilistic fatigue criteria adapted to the superficially treated parts using ABAQUS software. After the validation of this approach, it will allow design engineers to optimize the operating conditions of the LSP process.

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Optimization of peened-surface laser shock conditions by method of finite element and technique of design of experiments

Cited by 19 publications

References 34 publications

Influence of multiple laser impacts on thin leading edges of turbine blade

Influence of multiple laser impacts on thin leading edges of turbine blade

Hybrid Modelling by Machine Learning Corrections of Analytical Model Predictions towards High-Fidelity Simulation Solutions

Reliability prediction of high‐cycle fatigue behavior of parts treated by laser shock peening

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