Design, Simulation and Optimization of an Additive Laser-Based Manufacturing Process for Gearbox Housing with Reduced Weight Made from AlSi10Mg Alloy

Magerramova, Liubov; Исаков, В. В.; Shcherbinina, Liana; Gukasyan, S. G.; Petrov, Michael A.; Povalyukhin, Daniil; Volosevich, Darya; Klimova-Korsmik, Olga

doi:10.3390/met12010067

Cited by 8 publications

(5 citation statements)

References 19 publications

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“…AlSi10Mg is an aluminum alloy characterized by its lightweight properties, energy absorption capabilities, and high strength-to-weight ratio, making it a popular choice in various industrial applications, such as automotive parts [1], housings [2], and for rapid prototyping applications, such as 3D printing of mechanical parts [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Modeling of compressive stress in AlSi10Mg alloys using feed-forward neural networks

Rodríguez-Sánchez,

Acevedo-Alvarado

2024

Eng. Res. Express

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This study addresses the challenge of modeling compressive stress in AlSi10Mg foams by introducing a method that employs feedforward artificial neural networks (ANNs) and their interpretability, which helps to simulate and analyze material behavior under various conditions. The main objective is to develop a predictive ANN model that can effectively simulate material responses under several factors, incorporating diverse testing parameters and material specifications related with its synthesis. An optimized ANN model, featuring eleven neurons in its hidden layer, was used and demonstrated high predictive accuracy, achieving $R^{2}$ values exceeding 0.94. Additionally, a SHAP interpretability analysis was conducted to assess the influence of key factors such as strain and material conditions on the stress response. The results highlight the significant role of material synthesis processes, compared to the strain rate, in the stress response. In conclusion, this method presents a comprehensive tool for studying complex stress behaviors in AlSi10Mg-based foams, offering insights that could guide future material development and research.

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Section: Introductionmentioning

confidence: 99%

Modeling of compressive stress in AlSi10Mg alloys using feed-forward neural networks

Rodríguez-Sánchez,

Acevedo-Alvarado

2024

Eng. Res. Express

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“…Furthermore, according to the authors' best knowledge, there have been very few FE simulation-based studies that combine prediction of the melt pool at the mesoscale and macro properties, such as geometric distortions for additively manufactured AlSi10Mg alloy. This is due to the reason that either the focus is mainly on the temperature evolution and melt pool formation [30][31][32][33] or entirely on predicting part-scale properties such as geometrical defects [34,35]. This research fills this gap by formulating a thermomechanical simulation of the PBF process for AlSi10Mg using Abaqus.…”

Section: Introductionmentioning

confidence: 99%

Prediction and validation of melt pool dimensions and geometric distortions of additively manufactured AlSi10Mg

Ullah

Lian

Akmal

et al. 2023

Int J Adv Manuf Technol

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A finite element–based thermomechanical modeling approach is developed in this study to provide a prediction of the mesoscale melt pool behavior and part-scale properties for AlSi10Mg alloy. On the mesoscale, the widely adopted Goldak heat source model is used to predict melt pool formed by laser during powder bed fusion process. This requires the determination of certain parameters as they control temperature distribution and, hence, melt pool boundaries. A systematic parametric approach is proposed to determine parameters, i.e., absorption coefficient and transient temperature evolution. The simulation results are compared in terms of morphology of melt pool with the literature results. Considering the part-scale domain, there is increasing demand for predicting geometric distortions and analyzing underlying residual stresses, which are highly influenced by the mesh size and initial temperature setup. This study aims to propose a strategy for evaluating the correlation between the mesh size and the initial temperature to provide correct residual stresses when increasing the scale of the model for efficiency. The outcomes revealed that the predicted melt pool error produced by optimal Goldak function parameters is between 5 and 12%. On the part-scale, the finite element model is less sensitive to mesh size for distortion prediction, and layer-lumping can be used to increase the speed of simulation. The effect of large time increments and layer lumping can be compensated by appropriate initial temperature value for AlSi10Mg. The study aids practitioners and researchers to establish and validate design for additive manufacturing within the scope of desired part quality metrics.

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“…Considering its strength, hardness, and dynamic properties, it is used for products subjected to higher force loads. This alloy is used in aerospace and automotive applications due to its good mechanical properties and low weight [ 10 , 11 , 12 , 13 , 14 ]. The mountain bike stem was selected with regard to combined bending and rotational loads occurring on the part while riding a bicycle.…”

Section: Introductionmentioning

confidence: 99%

Case Study of Additively Manufactured Mountain Bike Stem

et al. 2023

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This article is focused on a case study of the topology optimisation of a bike stem manufactured by selective laser melting (SLM) additive technology. Topology optimisation was used as a design tool to model a part with less material used for transferring specific loads than the conventional method. For topology optimisation, Siemens NX 12 software was used with loads defined from the ISO 4210-5 standard. Post-processing of the topology-optimised shape was performed in Altair Inspire software. For this case study, the aluminium alloy AlSi10Mg was selected. For qualitative evaluation, the mechanical properties of the chosen alloy were measured on the tensile specimens. The design of the new bike stem was evaluated by Ansys FEA software with static loadings defined by ISO 4210-5. The functionality of the additively manufactured bike stem was confirmed by actual experiments defined by ISO 4210-5. The resulting new design of the bike stem passed both static tests and is 7.9% lighter than that of the reference.

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Design, Simulation and Optimization of an Additive Laser-Based Manufacturing Process for Gearbox Housing with Reduced Weight Made from AlSi10Mg Alloy

Cited by 8 publications

References 19 publications

Modeling of compressive stress in AlSi10Mg alloys using feed-forward neural networks

Modeling of compressive stress in AlSi10Mg alloys using feed-forward neural networks

Prediction and validation of melt pool dimensions and geometric distortions of additively manufactured AlSi10Mg

Case Study of Additively Manufactured Mountain Bike Stem

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