An investigation of the ensemble machine learning techniques for predicting mechanical properties of printed parts in additive manufacturing

Deb, Jayanta Bhusan; Chowdhury, Shilpa; Ali, Nur Mohammad

doi:10.1016/j.dajour.2024.100492

Decision Analytics Journal

2024

DOI: 10.1016/j.dajour.2024.100492

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An investigation of the ensemble machine learning techniques for predicting mechanical properties of printed parts in additive manufacturing

Jayanta Bhusan Deb,

Shilpa Chowdhury,

Nur Mohammad Ali

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2024

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Cited by 3 publications

References 24 publications

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Neuro-Fuzzy Model Evaluation for Enhanced Prediction of Mechanical Properties in AM Specimens

Mantalas,

Sagias,

Zacharia

et al. 2024

Applied Sciences

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This paper explores the integration of adaptive neuro-fuzzy inference systems (ANFIS) with additive manufacturing (AM) to enhance the prediction of mechanical properties in 3D-printed components. Despite AM’s versatility in producing complex geometries, achieving consistent mechanical performance remains challenging due to various process parameters and the anisotropic behavior of printed parts. The proposed approach combines the learning capabilities of neural networks with the decision-making strengths of fuzzy logic, enabling the ANFIS to refine printing parameters to improve part quality. Experimental data collected from AM processes are used to train the ANFIS model, allowing it to predict outputs such as stress, strain, and Young’s modulus under various printing parameters values. The predictive performance of the model was assessed with the root mean square error (RMSE) and coefficient of determination (R2) as evaluation metrics. The study initially examined the impact of key parameters on model performance and subsequently compared two fuzzy partitioning techniques—grid partitioning and subtractive clustering—to identify the most effective configuration. The experimental results and analysis demonstrated that ANFIS could dynamically adjust key printing parameters, leading to significant improvements in the prediction accuracy of stress, strain, and Young’s modulus, showcasing its potential to address the inherent complexities of additive manufacturing processes.

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Neuro-Fuzzy Model Evaluation for Enhanced Prediction of Mechanical Properties in AM Specimens

Mantalas,

Sagias,

Zacharia

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

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Explainable AI Techniques for Comprehensive Analysis of the Relationship between Process Parameters and Material Properties in FDM-Based 3D-Printed Biocomposites

Kharate,

Anerao,

Kulkarni

et al. 2024

JMMP

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This study investigates the complex relationships between process parameters and material properties in FDM-based 3D-printed biocomposites using explainable AI techniques. We examine the effects of key parameters, including biochar content (BC), layer thickness (LT), raster angle (RA), infill pattern (IP), and infill density (ID), on the tensile, flexural, and impact strengths of FDM-printed pure PLA and biochar-reinforced PLA composites. Mechanical testing was used to measure the ultimate tensile strength (UTS), flexural strength (FS), and impact strength (IS) of the 3D-printed samples. The extreme gradient boosting (XGB) algorithm was used to build a predictive model based on the data collected from mechanical testing. Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and Partial Dependence Plot (PDP) techniques were implemented to understand the effects of the interactions of key parameters on mechanical properties such as UTS, FS, and IS. Prediction by XGB was accurate for UTS, FS, and IS, with R-squared values of 0.96, 0.95, and 0.85, respectively. The explanation showed that infill density has the most significant influence on UTS and FS, with SHAP values of +2.75 and +5.8, respectively. BC has the most significant influence on IS, with a SHAP value of +2.69. PDP reveals that using 0.3 mm LT and 30° RA enhances mechanical properties. This study contributes to the field of the application of artificial intelligence in additive manufacturing. A novel approach is presented in which machine learning and XAI techniques such as SHAP, LIME, and PDP are combined and used not only for optimization but also to provide valuable insights about the interaction of the process parameters with mechanical properties.

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Experimental Study and Random Forest Machine Learning of Surface Roughness for a Typical Laser Powder Bed Fusion Al Alloy

Shan,

Gao,

Rao

et al. 2024

Metals

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Surface quality represents a critical challenge in additive manufacturing (AM), with surface roughness serving as a key parameter that influences this aspect. In the aerospace industry, the surface roughness of the aviation components is a very important parameter. In this study, a typical Al alloy, AlSi10Mg, was selected to study its surface roughness when using Laser Powder Bed Fusion (LPBF). Two Random Forest (RF) models were established to predict the upper surface roughness of printed samples based on laser power, laser scanning speed, and hatch distance. Through the study, it is found that a two-dimensional (2D) RF model is successful in predicting surface roughness values based on experimental data. The best and minimum surface roughness is 2.98 μm, which is the minimum known without remelting. More than two-thirds of the samples had a surface roughness of less than 7.7 μm. The maximum surface roughness is 11.28 μm. And the coefficient of determination (R2) of the model was 0.9, also suggesting that the surface roughness of 3D-printed Al alloys can be predicted using ML approaches such as the RF model. This study helps to understand the relationship between printing parameters and surface roughness and helps print components with better surface quality.

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An investigation of the ensemble machine learning techniques for predicting mechanical properties of printed parts in additive manufacturing

Cited by 3 publications

References 24 publications

Neuro-Fuzzy Model Evaluation for Enhanced Prediction of Mechanical Properties in AM Specimens

Neuro-Fuzzy Model Evaluation for Enhanced Prediction of Mechanical Properties in AM Specimens

Explainable AI Techniques for Comprehensive Analysis of the Relationship between Process Parameters and Material Properties in FDM-Based 3D-Printed Biocomposites

Experimental Study and Random Forest Machine Learning of Surface Roughness for a Typical Laser Powder Bed Fusion Al Alloy

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