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

Babakan, Ahmad Memaran; Davoodi, Mohammad; Shafaie, Majid; Sarparast, Mohsen; Zhang, Hongyan

doi:10.1007/s00170-023-12333-w

Int J Adv Manuf Technol

2023

DOI: 10.1007/s00170-023-12333-w

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

Ahmad Memaran Babakan,

Mohammad Davoodi,

Majid Shafaie

et al.

Abstract: 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 po… Show more

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2024

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Cited by 3 publications

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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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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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Predicting the fatigue life of additively manufactured AlSi10Mg alloy using a physics-informed neural network incorporating continuous damage mechanics

Wu,

Wang,

2024

Proceedings of the Institution of Mechanical Engineers, Part B:

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The material characteristics of additively manufactured AlSi10Mg alloy, including the random distribution of process-induced micro defects, microstructual anisotropy, and grain morphologies with complex diversity, poses a significant challenge in accurately predicting its fatigue life, limiting its application in the aircraft field. This work herein introduces a novel approach incorporating a physics-informed neural network (PINN), in which an artificial neural network (ANN) is embedded with a damage mechanics model (CDM) and the critical process parameters of laser powder bed fusion (L-PBF) additive manufacturing. The partial differential equations of the updated CDM model are introduced into the training procedures of the PINN to building a loss function, effectively “teaching” the PINN to learn the physical knowledge. A comparison of the fatigue life prediction results of ANN and the proposed PINN models shows that the PINN model outperforms its counterparts with a 38.71% higher prediction accuracy. The effects of L-PBF process parameters on the fatigue life of as-built AlSi10Mg is examined using both ANN and PINN, proving the better predictive performance and data-physics consistency of the PINN model. The scheme of this work can inform the efficient prediction of fatigue life of additively manufactured alloys and also reverse prediction or fast finding of optimized process parameters for additive manufacturing.

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Laser Powder Bed Fusion of Pure Titanium: Optimization of Processing Parameters by Means of Efficient Volumetric Energy Density Approach

Badini,

Santero,

Rosito

et al. 2024

Metals

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This paper focuses on optimizing the process parameters for manufacturing commercially pure titanium grade 2 using Laser Powder Bed Fusion (L-PBF) technology. The most common approach involves trial-and-error builds with varying parameter combinations, followed by characterizing the bulk samples for defects and the microstructure. This method, typically based on Volumetric Energy Density (VED), is time-consuming and overlooks key powder properties. An alternative approach involves the use of efficient Volumetric Energy Density (VEDeff), which represents the energy density effectively available for the L-PBF process, considering both the process parameters and powder properties such as absorptivity and thermal diffusivity. In this study, VEDeff was applied and compared to a work window defined by thermodynamic data, with limits corresponding to the energy needed for titanium melting and evaporation. Forty-two tests were performed with different combinations of laser powers and scanning speeds; the samples were then characterized in terms of porosity, microstructure, and hardness. The findings showed no correlation between VED and the work window while VEDeff aligned with the work window, although the highest relative densities (>99%) and hardness values were achieved in a narrower range. Despite this, the VEDeff approach proved to be a useful starting point for optimizing the process parameters.

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

Cited by 3 publications

References 43 publications

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

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

Predicting the fatigue life of additively manufactured AlSi10Mg alloy using a physics-informed neural network incorporating continuous damage mechanics

Laser Powder Bed Fusion of Pure Titanium: Optimization of Processing Parameters by Means of Efficient Volumetric Energy Density Approach

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