Modified GTN parameters calibration in additive manufacturing of Ti-6Al-4 V alloy: a hybrid ANN-PSO approach

Shafaie, Majid; Khademi, Maziar; Sarparast, Mohsen; Zhang, Hongyan

doi:10.1007/s00170-022-10522-7

Cited by 7 publications

(2 citation statements)

References 55 publications

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“…Shafaie et al [25] set up an artificial neural network for a less time-consuming and optimized determination of the various model constants of the GTN model for Ti-6Al-4V parts manufactured by PBF-LB/M. Experimental validations showed a good agreement with the determined parameter values.…”

Section: Introductionmentioning

confidence: 99%

Towards a Simulation-Assisted Prediction of Residual Stress-Induced Failure during Powder Bed Fusion of Metals Using a Laser Beam: Suitable Fracture Mechanics Models and Calibration Methods

Panzer,

Wolf,

Bachmann

et al. 2023

JMMP

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In recent years, Additive Manufacturing (AM) has emerged as a transformative technology, with the process of Powder Bed Fusion of Metals using a Laser Beam (PBF-LB/M) gaining substantial attention for its precision and versatility in fabricating metal components. A major challenge in PBF-LB/M is the failure of the component or the support structure during the production process. In order to locate a possible residual stress-induced failure prior to the fabrication of the component, a suitable failure criterion has to be identified and implemented in process simulation software. In the work leading to this paper, failure criteria based on the Rice-Tracey (RT) and Johnson-Cook (JC) fracture models were identified as potential models to reach this goal. The models were calibrated for the nickel-based superalloy Inconel 718. For the calibration process, a conventional experimental, a combined experimental and simulative, and an AM-adapted approach were applied and compared. The latter was devised to account for the particular phenomena that occur during PBF-LB/M. It was found that the JC model was able to capture the calibration data points more precisely than the RT model due to its higher number of calibration parameters. Only the JC model calibrated by the experimental and AM-adapted approach showed an increased equivalent plastic failure strain at high triaxialities, predicting a higher cracking resistance. The presented results can be integrated into a simulation tool with which the potential fracture location as well as the cracking susceptibility during the manufacturing process of PBF-LB/M parts can be predicted.

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Section: Introductionmentioning

confidence: 99%

Towards a Simulation-Assisted Prediction of Residual Stress-Induced Failure during Powder Bed Fusion of Metals Using a Laser Beam: Suitable Fracture Mechanics Models and Calibration Methods

Panzer,

Wolf,

Bachmann

et al. 2023

JMMP

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“…Additive manufacturing (AM) is a layer-by-layer manufacturing process used to create 3-dimensional (3D) components. these processes are suitable for the fabrication of complex structures with various geometrical shapes [6][7][8][9]. The Selective Laser Melting process (SLM) is utilized to form complex metal structures and in this process, raw powder metal feedstock and a laser heat source are employed for fabrication [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Predictive modeling of porosity in AlSi10Mg alloy fabricated by laser powder bed fusion: A comparative study with RSM, ANN, FL, and ANFIS

Babakan,

Davoodi,

Shafaie

et al. 2023

Int J Adv Manuf Technol

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The laser selective melting (SLM) process is commonly used in additive manufacturing (AM) to produce integrated parts from metallic powder. However, this process can result in porosity in the fabricated components due to gas bubbles or lack of fusion. In a recent study, four different methods (response surface methodology (RSM), artificial neural network (ANN), fuzzy logic (FL), and adaptive-network-based fuzzy inference system (ANFIS)) were employed to predict the correlation between process parameters and porosity levels in AlSi10Mg alloy produced by the SLM process.The ANFIS method was used in this study, which utilizes fuzzy rules and artificial neural networks to predict the impact of process parameters, such as island size, hatch space, scan speed, and laser power on porosity levels. The results showed that the laser power and scan speed had a significant effect on the volume of porosity, while the influence of island size and hatch space was slighter in the SLM process of AlSi10Mg alloy.Furthermore, the ANFIS model demonstrated an excellent fitting parameter with an R 2 value of more than 0.99 for the total data and an RMSE of about 0.67 for the output. This result indicates that the ANFIS method is the best model among all the methods tested in accurately predicting the process target. The success of the ANFIS method in this study suggests that it is a robust and reliable technique for predicting porosity levels in the SLM process.

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Integrating data-driven system to predict temperature and distortion in multi-layer direct metal deposition processes

Shafaie,

Sarparast,

Zhang

2024

Int J Adv Manuf Technol

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This study proposed a framework to train an artificial neural network (ANN) by a data-driven system to predict the temperature and distortion in multi-layer direct metal deposition (DMD) of SS 304. By integrating thermomechanical variables, the research ensures the fidelity of finite element (FE) simulations, which are validated against existing data. Notably, the study achieves enhanced precision over prior work by varying the heat input sources and heat transfer equations. A novel aspect of this research is the use verified FE simulation to add data to data-driven system to train an efficient ANN for predicting temperature and distortion based on key parameters such as laser power and scanning speed. The iterative process involved multiple FE simulations with varying laser parameters to refine the ANN’s predictive capabilities. This methodology enabled the identification of relationships between manufacturing parameters, temperature, and distortion. The iterative training continued until the ANN’s predictions and subsequent FE simulation results converged within an acceptable margin. The findings confirm that the trained ANN can predict temperature and distortion both accurately and expediently, marking a significant advancement in the control of the DMD process. Graphical Abstract

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Modified GTN parameters calibration in additive manufacturing of Ti-6Al-4 V alloy: a hybrid ANN-PSO approach

Cited by 7 publications

References 55 publications

Towards a Simulation-Assisted Prediction of Residual Stress-Induced Failure during Powder Bed Fusion of Metals Using a Laser Beam: Suitable Fracture Mechanics Models and Calibration Methods

Towards a Simulation-Assisted Prediction of Residual Stress-Induced Failure during Powder Bed Fusion of Metals Using a Laser Beam: Suitable Fracture Mechanics Models and Calibration Methods

Predictive modeling of porosity in AlSi10Mg alloy fabricated by laser powder bed fusion: A comparative study with RSM, ANN, FL, and ANFIS

Integrating data-driven system to predict temperature and distortion in multi-layer direct metal deposition processes

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