Innovative approach to mass production of fiber metal laminate sheets

Sherkatghanad, Ehsan; Lang, Lihui; Shichen, Liu; Wang, Yao

doi:10.1080/10426914.2017.1364864

Cited by 42 publications

(22 citation statements)

References 30 publications

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“…FML was used in the upper fuselage and on the tail leading edges of the Airbus A380 due to its lightweight, high impact strength, good corrosion resistance, and relatively long fatigue life compared with conventional aluminum structure. [ 1–7 ] With the advances of the automation manufacturing of FML, [ 8,9 ] the interest in FML is growing again as aero companies are searching for robust fuselage material adaptable to their new narrow‐body commercial aircraft. [ 10 ] Also, there are increasing needs in the automobile industries to exert for lightweight, high stiffness material to enhance the weight efficiency of the vehicle structures.…”

Section: Introductionmentioning

confidence: 99%

Fatigue Life Prediction of GLARE Composites Using Regression Tree Ensemble‐Based Machine Learning Model

Sai

Chai

Srikanth

2020

Advcd Theory and Sims

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With the advancement of machine learning in leading technologies, it is perceived that machine learning is a new and effective alternative for the classic fatigue life prediction. This paper provides a regression tree ensemble‐based machine learning approach to predict the fatigue life of GLARE composites. In the model, mechanical, geometrical properties and fatigue loading stresses are selected as the training parameters (so‐called features), and the GLARE fatigue life is predicted as the output of the model. Experimental data of a total of 98 pieces of GLARE specimens with nine different layups are used for the training and validation of the machine learning model. Results show that the model can provide good fatigue life prediction accuracy and model stability. The most correlated, either positively or negatively, parameters to the fatigue life span are the stress developed in the aluminum layer, the maximum cyclic stress, alternating stress, and mean fatigue stress.

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Section: Introductionmentioning

confidence: 99%

Fatigue Life Prediction of GLARE Composites Using Regression Tree Ensemble‐Based Machine Learning Model

Sai

Chai

Srikanth

2020

Advcd Theory and Sims

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“…Moreover, Lang et al in their research introduce a special procedure using assisted radial pressure in HDD with Al6016-T4 reveals that because of the clamping force the BHG may be less than the initial thickness during the drawing of the blank, [7]. 2017, Sherkatghanad et al in their research present a new method using a semi-cured Glare blank as an alternative of a solidified blank in mechanical hydroforming of hemispherical parts, results shown an increase in the forming depth due to middle layer chance to move more freely between the Al sheets, but wrinkling and delamination has appeared and the depth target cannot be accomplished [8]. From the numerical simulations of Harith, no wrinkling can be observed when using BHG with a small value.…”

Section: Introductionmentioning

confidence: 99%

Process Control Improvement in Deep Drawing of Hemispherical Cups Made of GLARE Material

Blala

Lang

et al. 2020

MATEC Web Conf.

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Controlling the material flow under the blank holder in hydroforming is a delicate point that can be controlled by the blank holder force (BHF), cavity pressure (CP) and the blank holder gap (BHG). BHG is one of the effective parameters to control the material flow. A numerical and experimental approach studied the impact of the BHG on the Hydromechanical Deep Drawing (HDD) of a hemispherical cup made with 2/1 Glare sheets, including another two factors, CP and BHF. BHGs with different thickness was used in the experiments to form Glare Laminate. Simulation and experiment illustrate that an optimized value of these parameters has a good effect on the forming quality and depth of the final product. Finally, an appropriate value of the BHG is projected and the explanations of the failure modes are discussed.

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“…16,17 This hydroforming process has been researched in various ways through experiment and analysis. 18–20…”

Section: Introductionmentioning

confidence: 99%

“…16,17 This hydroforming process has been researched in various ways through experiment and analysis. [18][19][20] The type of fiber structure used to reinforce composites affects physical properties and formability of the structure. 11 Also, both in thermoset and thermoplastic composites, the microstructure of the interface plays an important role in controlling the optimal properties of the composite.…”

Section: Introductionmentioning

confidence: 99%

Mechanical analysis of thermo-hydroforming of a fiber-reinforced thermoplastic composite helmet using preferred fiber orientation model

Ahn

Kuuttila

Pourboghrat

2018

Journal of Composite Materials

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The composite thermo-hydroforming process, utilizing heated and pressurized fluid, was used to form an advanced helmet with the Spectra Shield. Experimental results obtained from the forming process show that thermo-hydroforming is a feasible process for manufacturing thermoplastic composite materials. Concurrent to the forming experiments, the forming process was numerically modeled using ABAQUS/CAE. The behavior of the fiber reinforced polymer composite was modeled using the Preferred Fiber Orientation model, which was implemented into the explicit finite element code ABAQUS by writing a User Material Subroutine. The preferred fiber orientation model was further adapted to work with a composite laminate consisting of multiple layers. Numerical results were compared with experimental data to validate the method in terms of predicting the deformed geometry of the multilayer composite, wrinkling, as well as the punch force–displacement curve. Overall, the deformed shape of the fiber-reinforced thermoplastic composite helmet, including the distribution of wrinkles were predicted accurately.

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Innovative approach to mass production of fiber metal laminate sheets

Cited by 42 publications

References 30 publications

Fatigue Life Prediction of GLARE Composites Using Regression Tree Ensemble‐Based Machine Learning Model

Fatigue Life Prediction of GLARE Composites Using Regression Tree Ensemble‐Based Machine Learning Model

Process Control Improvement in Deep Drawing of Hemispherical Cups Made of GLARE Material

Mechanical analysis of thermo-hydroforming of a fiber-reinforced thermoplastic composite helmet using preferred fiber orientation model

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