Predicting the Relative Density of Stainless Steel and Aluminum Alloys Manufactured by L-PBF Using Machine Learning DOI Creative Commons
José Luis Mullo, Iván La Fé-Perdomo, Jorge Ramos‐Grez

и другие.

Journal of Manufacturing and Materials Processing, Год журнала: 2025, Номер 9(6), С. 185 - 185

Опубликована: Июнь 3, 2025

Metal additive manufacturing is a disruptive technology that changing how various alloys are processed. Although this has several advantages over conventional manufacturing, it still necessary to standardize its properties, which dependent on the relative density (RD). In addition, since experimental designs costly, one solution using machine learning algorithms allow effects of variations in processing parameters resulting additively manufactured components be anticipated. This work assembled database based data from 673 observations and 10 predictors forecast 316L stainless steel AlSi10Mg produced by laser powder bed fusion (L-PBF). LazyPredict was employed select algorithm best models variability inherent data. Ensemble boosting regressors offer higher accuracy, providing hyperparameter fitting optimization advantages. The predictions’ precision for aluminum obtained an R2 value greater than 0.86 0.83, respectively. results SHAP values indicated power energy have greatest impact predictability Al-Si10-Mg SS materials processed L-PBF. study presents compendium fabrication alloys, offering researchers guide understanding influence RD.

Язык: Английский

A review of machine learning (ML) and explainable artificial intelligence (XAI) methods in additive manufacturing (3D Printing) DOI Creative Commons

Jeewanthi Ukwaththa,

Sumudu Herath, D.P.P. Meddage

и другие.

Materials Today Communications, Год журнала: 2024, Номер 41, С. 110294 - 110294

Опубликована: Сен. 6, 2024

Язык: Английский

Процитировано

30
Performance evaluation of machine learning techniques in surface roughness prediction for 3D printed micro-lattice structures DOI

B. Veera Siva Reddy,

Ameer Malik Shaik,

C. Chandrasekhara Sastry

и другие.

Journal of Manufacturing Processes, Год журнала: 2025, Номер 137, С. 320 - 341

Опубликована: Фев. 7, 2025

Язык: Английский

Процитировано

3
Accelerated design of high-entropy alloy coatings for high corrosion resistance via machine learning DOI
Hongxu Cheng, Hong Luo, C.G. Fan

и другие.

Surface and Coatings Technology, Год журнала: 2025, Номер unknown, С. 131978 - 131978

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

1
Machine Learning-Assisted Stress and Deformation Prediction for 316 L Stainless Steel Hybrid Lattice Structures Fabricated Through Laser Powder Bed Fusion DOI

Samala Thirupathi,

Amit Rai Dixit, Pratik Kumar Shaw

и другие.

Materials Today Communications, Год журнала: 2025, Номер unknown, С. 112037 - 112037

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

1
Machine learning for screw design in single‐screw extrusion DOI Creative Commons
Nickolas D. Polychronopoulos, Konstantinos Moustris, Theodoros E. Karakasidis

и другие.

Polymer Engineering and Science, Год журнала: 2025, Номер unknown

Опубликована: Март 10, 2025

Abstract Artificial intelligence (AI) methods have significantly impacted various areas of technology, particularly in fields where large datasets are available. Screw designs proprietary, and there is very limited information available the open literature. In this study, we generated a dataset 232 using computer simulation software for screw extrusion, involving solids transport, melting, melt pumping. The parameters (features) outputs (targets) were introduced into four powerful machine learning (ML) algorithms. capabilities algorithms assessed by comparing predictions each to corresponding results simulations. Three demonstrated satisfactory performance, with best‐performing one being further tested on an “unseen” dataset, which involved 75 mm another 127 diameter. A machine‐learning technique called Permutation Feature Importance (PFI) was used identify features (parameters) greatest impact predictions. It suggested that same ML methodologies could be applied existing real designs. Highlights Dataset obtained from software. Four employed. Assessment based training testing data. Identification having impact. Satisfactory mass flow rate, exit temperature, melting length, more.

Язык: Английский

Процитировано

1
Characterisation and prediction of mechanical properties in laser powder bed fusion-printed parts: a comparative analysis using machine learning DOI Creative Commons
Naol Dessalegn Dejene, Hirpa G. Lemu

Materials Technology, Год журнала: 2024, Номер 39(1)

Опубликована: Окт. 25, 2024

This study investigates the effects of process parameters including scanning strategy, build orientation, and hatching distance on mechanical properties AlSi10Mg parts produced by Laser Powder Bed Fusion (L-PBF). The experiment varied these within defined ranges used statistical analysis to evaluate their impact tensile strength ductility. Results showed that strategy had greatest influence, followed distance, while orientation affected anisotropic properties. Microstructural clear correlation between conditions strength, thereby showing underlying mechanisms govern material behavior. Moreover, Machine learning models, Random Forest Regression (RFR), Support Vector (SVR), Artificial Neural Networks (ANNs), were applied predict ductility characteristics. RFR SVR outperformed ANNs, high predictive accuracy with limited datasets. These findings emphasize importance optimizing L-PBF minimize anisotropy achieve consistent in parts.

Язык: Английский

Процитировано

5
Artificial Intelligence-Driven Innovations in Laser Processing of Metallic Materials DOI Creative Commons
Serguei P. Murzin

Metals, Год журнала: 2024, Номер 14(12), С. 1458 - 1458

Опубликована: Дек. 20, 2024

This article explores the integration of artificial intelligence (AI) and advanced digital technologies into laser processing, highlighting their potential to enhance precision, efficiency, process control. The study examines application twins machine learning (ML) for optimizing machining, reducing defects, improving analysis laser–material interactions. Emphasis is placed on AI’s role in additive manufacturing microprocessing, particularly real-time monitoring, defect prediction, parameter optimization. Additionally, addresses emerging challenges, such as adaptation AI models complex material behaviors intelligent systems existing environments. optical technologies, free-form optics diffractive elements, discussed relation enhancing system adaptability performance. concludes with a discussion future trends, emphasizing need interdisciplinary collaboration overcome technical economic complexities while leveraging meet growing demand precision customization industrial manufacturing.

Язык: Английский

Процитировано

5
Advancing Additive Manufacturing Through Machine Learning Techniques: A State-of-the-Art Review DOI Creative Commons
Shaoping Xiao, Junchao Li, Zhaoan Wang

и другие.

Future Internet, Год журнала: 2024, Номер 16(11), С. 419 - 419

Опубликована: Ноя. 13, 2024

In the fourth industrial revolution, artificial intelligence and machine learning (ML) have increasingly been applied to manufacturing, particularly additive manufacturing (AM), enhance processes production. This study provides a comprehensive review of state-of-the-art achievements in this domain, highlighting not only widely discussed supervised but also emerging applications semi-supervised reinforcement learning. These advanced ML techniques recently gained significant attention for their potential further optimize automate AM processes. The aims offer insights into various technologies employed current research projects promote diverse AM. By exploring latest advancements trends, seeks foster deeper understanding ML’s transformative role AM, paving way future innovations improvements practices.

Язык: Английский

Процитировано

3
Predictive modelling of flexural behaviour of polymer composites: a machine learning approach through material extrusion DOI
Akash Jain,

Saloni Upadhyay,

Kanishka Pathik

и другие.

Progress in Additive Manufacturing, Год журнала: 2024, Номер unknown

Опубликована: Дек. 5, 2024

Язык: Английский

Процитировано

3
Explainable Machine Learning for Predicting Tensile Properties of Aluminum Alloys in the Laser Powder Bed Fusion Process DOI
Abdul Wahid Shah, Kang Wang, Jabir Ali Siddique

и другие.

Опубликована: Янв. 1, 2025

The rapid solidification and unique thermal gradients inherent to the laser powder bed fusion (LPBF) process limit suitability of conventional aluminum (Al) alloys, necessitating optimization existing alloys or development new compositions achieve desired tensile properties while ensuring good processability. Experimental exploration alloy is labor-intensive, costly, time-consuming. Machine learning (ML) offers a cost-effective, flexible approach streamline design accelerate advancements in AM technologies. This study introduces data-driven predictive framework for predicting Al LPBF. To address limited data on LPBF restricted range systems investigated, (including cast wrought alloys) laser-directed energy deposition (LDED) built were also included, alongside data. dataset incorporates comprehensive pool features such as composition, processing parameters, grain size, elemental properties. Pearson correlation coefficient (PCC) with feature importance-based selection was implemented balance model complexity accuracy via reducing dimensionality overfitting. resulting ML demonstrates excellent generalizability, successfully extending its applicability unseen systems. reliable tool optimizing designs, significantly reliance costly experimental trials. inclusion Explainable AI provided detailed interpretability, elucidating influence individual predictions, predictions scientifically grounded.

Язык: Английский

Процитировано

0