A Physics-Informed Machine Learning Model for Mounting Optimization in Printed Circuit Boards DOI
Jae-Woo Kim, Abdelrahman Farrag, Nieqing Cao

et al.

Lecture notes in mechanical engineering, Journal Year: 2024, Volume and Issue: unknown, P. 66 - 74

Published: Jan. 1, 2024

Language: Английский

A review of machine learning in additive manufacturing: design and process DOI

Kefan Chen,

Peilei Zhang, Hua Yan

et al.

The International Journal of Advanced Manufacturing Technology, Journal Year: 2024, Volume and Issue: 135(3-4), P. 1051 - 1087

Published: Oct. 5, 2024

Language: Английский

Citations

4
Deep Learning-Based Applications in Metal Additive Manufacturing Processes: Challenges and Opportunities - A Review DOI Creative Commons
Tuğrul Özel

International Journal of Lightweight Materials and Manufacture, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Citations

0
Accelerated Fatigue Strength Prediction via Additive Manufactured Functionally Graded Materials and High-Throughput Plasticity Quantification DOI Creative Commons
C. Bean,

Mathieu Calvat,

Yuheng Nie

et al.

Materials & Design, Journal Year: 2025, Volume and Issue: unknown, P. 114115 - 114115

Published: June 1, 2025

Language: Английский

Citations

0
Simulation of multiphase flow with thermochemical reactions: A review of computational fluid dynamics (CFD) theory to AI integration DOI
Dongke Zhang, Tanzila Anjum,

Zhiqiang Chu

et al.

Renewable and Sustainable Energy Reviews, Journal Year: 2025, Volume and Issue: 221, P. 115895 - 115895

Published: June 7, 2025

Language: Английский

Citations

0
Thermal-fluid modeling and physics-informed machine learning for predicting molten pool depth in single-layer multi-track fiber laser cladding DOI
Kaixiong Hu, Yiwei Wang,

Feiyang Li

et al.

The International Journal of Advanced Manufacturing Technology, Journal Year: 2024, Volume and Issue: 135(7-8), P. 3591 - 3613

Published: Oct. 30, 2024

Language: Английский

Citations

2
A review on physics-informed machine learning for process-structure-property modeling in additive manufacturing DOI Creative Commons
Meysam Faegh, Suyog Ghungrad,

João Pedro Oliveira

et al.

Journal of Manufacturing Processes, Journal Year: 2024, Volume and Issue: 133, P. 524 - 555

Published: Nov. 30, 2024

Language: Английский

Citations

2
Multi-Laser Scan Assignment and Scheduling Optimization for Large Scale Metal Additive Manufacturing DOI
Yuxin Yang, Lijing Yang,

Abdelrahman Farrag

et al.

IISE Transactions, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 16

Published: Aug. 6, 2024

Metal additive manufacturing (AM) has attracted significant attention in various industry sectors for large-scale fabrication. However, the limited fabrication efficiency hindered its practical implementation. In comparison to traditional methods of tuning process parameters, concurrent AM equipped with multiple independently driven lasers is a more promising technique recently developed efficient large metal parts. To maximize while ensuring quality multi-laser processes, an optimization problem proposed this work scanning plan, including scan vector assignment and scheduling. The goal minimize makespan considering factors that may affect parts as constraints. Specifically, constraints associated heat-affected zones (HAZs) user-specified single-laser area are considered. model solved by deep reinforcement learning (DRL), offering flexibility include or exclude considerations different quality/process requirements. Two case studies demonstrate application DRL models sets compare their performance two baseline scheduling terms violation addition, impact laser number on operational improvement computational cost also studied.

Language: Английский

Citations

1
Multi-material fabrication and compressive strength-optimization of reinforced-thermoset structures for mechanical power transmission DOI
Parth Patpatiya, Anshuman Shastri,

Shailly Sharma

et al.

Progress in Additive Manufacturing, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 24, 2024

Language: Английский

Citations

1
Physics-Informed Machine Learning for Industrial Reliability and Safety Engineering: A Review and Perspective DOI

Dac Hieu Nguyen,

Hien Nguyen Thi, Kim Duc Tran

et al.

Springer series in reliability engineering, Journal Year: 2024, Volume and Issue: unknown, P. 5 - 23

Published: Jan. 1, 2024

Language: Английский

Citations

1
Thermal deformation prediction for additive manufacturing of thin-walled components based on multi-layer transfer learning DOI Creative Commons

Linxuan WANG,

Jinghua Xu, Shuyou Zhang

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 5, 2024

Abstract This paper presents a thermal deformation prediction method for additive manufacturing of thin-walled components based on multi-layer transfer learning (MTL). The printability is forwardly designed via multi-objective optimization (MOO) by evaluating scanning length, spot amount and segment amount, accompanied support material. To avoid the burdened time-consuming simulation FEM various geometric characteristics components, feed-forward perceptron was constructed as main structure MTL to rapidly obtain temperature distributions manufactured parts. proposed verified SLM mechanical unshrouded turbine. metallographic diagrams were generated observe fabricating quality verify effectiveness MTL-based method. experiment fabricated piece proves that microstructure cross-section molten pool spindly columnar crystals. morphology size different due process parameters, making width grain about 1µm. especially useful metal 3D printing under uncertainty.

Language: Английский

Citations

0