Integration of artificial neural network with finite element analysis for residual stress prediction of direct metal deposition process

Hajializadeh, Farshid; Ince, Ayhan

doi:10.1016/j.mtcomm.2021.102197

Cited by 15 publications

(8 citation statements)

References 35 publications

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“…The remarkable point is the reduction of the residual stress from 361 to 23 MPa after the stress relaxation, equivalent to a change of 338 MPa, while the yield strength variation was equal to 99 MPa. It stems from the non-uniformity of residual stress distribution along each layer and the unbalanced distribution of residual stress in different layers, which has been mentioned in various studies [ [48] , [49] , [50] , [51] , [52] , [53] ].…”

Section: Resultsmentioning

confidence: 99%

Investigating the relationship between mechanical properties and residual stress in the laser cladding process of Inconel 625 superalloy

Borhani,

Rajabi,

Shoja Razavi

et al. 2023

Heliyon

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Section: Resultsmentioning

confidence: 99%

Investigating the relationship between mechanical properties and residual stress in the laser cladding process of Inconel 625 superalloy

Borhani,

Rajabi,

Shoja Razavi

et al. 2023

Heliyon

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“…A well-tested FE model is a good source for generating data for ML models for a large number of process variables. PIML model (combined FE and ML) has been reported in the existing literature for analyzing different deposition attributes on additively manufactured components [10][11][12]. Hajializadeh et al [10] developed an ANN model integrated with the FE model for the computation of residual stresses in different deposited structures.…”

Section: Introductionmentioning

confidence: 99%

“…PIML model (combined FE and ML) has been reported in the existing literature for analyzing different deposition attributes on additively manufactured components [10][11][12]. Hajializadeh et al [10] developed an ANN model integrated with the FE model for the computation of residual stresses in different deposited structures. The transient temperature evolution for multi-layer deposition with varying heat input levels for a WAAM process is analysed by ANN [13].…”

Section: Introductionmentioning

confidence: 99%

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Physics-informed machine learning models for the prediction of transient temperature distribution of ferritic steel in directed energy deposition by cold metal transfer

Kumar

Sarma

Bag

et al. 2023

Science and Technology of Welding and Joining

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In-situ monitoring of the additive layer characteristics in the directed energy deposition (DED) process by any contact technology is cumbersome. A well-tested finite element (FE) model is often employed to extract transient temperature distribution during deposition. However, the numerical model pertaining to each deposition attribute is computationally expensive. In the present work, we have generated a dataset through an experimentally validated thermal model, and further multiple machine learning (ML) algorithms are applied to train datasets. Models with an accuracy of more than 99% are utilised for the prediction of transient temperature distribution. The validation of deposition attributes using experiments and numerical model suggests that the physics-informed machine learning models for cold metal transfer can be applied in the DED process.

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“…The researchers suggested that the structural and adaptive node technique was up to nine times faster than the Abaqus software. Hajializadeh et al [55] used the estimation method with artificial neural networks in addition to finite elements in the analysis of residual stresses in stainless steel products manufactured with AM and stated that the artificial intelligence integrated finite element technique was a very timesaving approach. Rouway et al [56] compared distortion and residual stresses in polyamide (PA) and PEEK-based composite turbine blades using finite elements.…”

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confidence: 99%

Simulation of Fused Deposition Modeling of Glass Fiber Reinforced ABS Impact Samples: The Effect of Fiber Ratio, Infill Rate, and Infill Pattern on Warpage and Residual Stresses

Ergene

Bolat

2023

Hittite Journal of Science and Engineering

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It is known that products made of polymer materials or especially polymer materials with glass fiber and carbon fiber are used in many different areas such as automotive, aerospace, and defense. At this point, studies in the literature have gained momentum due to the combination of fiber-reinforced polymer materials emerging as a result of technological developments and industrial demands, and the fused deposition modeling (FDM) method providing the production of parts in desired sizes and complexity. Residual stresses and distortions occurring in polymer-based composite parts produced with FDM are among the problems that should be minimized. In this study, the influences of fiber ratio (%10, %15, and %20), infill rate (%20, %50, and %80), and infill pattern (line, honeycomb, and triangle) on the residual stresses and warpages generating in impact test specimens produced from glass fiber reinforced ABS filaments by fused deposition modeling were tried to be determined with the Digimat 2021 program. As a result of the findings, it was determined that the distortion values decreased and the thermal residual stress values went up with the increase in fiber ratio and infill rate. In addition, it can be reported that the distortions that bring out as a result of the separation of the produced parts from the production platform are caused by the high deformations condensing at the lower corner points of the parts.

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Integration of artificial neural network with finite element analysis for residual stress prediction of direct metal deposition process

Cited by 15 publications

References 35 publications

Investigating the relationship between mechanical properties and residual stress in the laser cladding process of Inconel 625 superalloy

Investigating the relationship between mechanical properties and residual stress in the laser cladding process of Inconel 625 superalloy

Physics-informed machine learning models for the prediction of transient temperature distribution of ferritic steel in directed energy deposition by cold metal transfer

Simulation of Fused Deposition Modeling of Glass Fiber Reinforced ABS Impact Samples: The Effect of Fiber Ratio, Infill Rate, and Infill Pattern on Warpage and Residual Stresses

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