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

The International Journal of Advanced Manufacturing Technology, Год журнала: 2024, Номер 135(3-4), С. 1051 - 1087

Опубликована: Окт. 5, 2024

Язык: Английский

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Deep Learning-Based Applications in Metal Additive Manufacturing Processes: Challenges and Opportunities - A Review

International Journal of Lightweight Materials and Manufacture, Год журнала: 2025, Номер unknown

Опубликована: Апрель 1, 2025

Язык: Английский

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Accelerated Fatigue Strength Prediction via Additive Manufactured Functionally Graded Materials and High-Throughput Plasticity Quantification

Materials & Design, Год журнала: 2025, Номер unknown, С. 114115 - 114115

Опубликована: Июнь 1, 2025

Язык: Английский

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Simulation of multiphase flow with thermochemical reactions: A review of computational fluid dynamics (CFD) theory to AI integration

Renewable and Sustainable Energy Reviews, Год журнала: 2025, Номер 221, С. 115895 - 115895

Опубликована: Июнь 7, 2025

Язык: Английский

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Thermal-fluid modeling and physics-informed machine learning for predicting molten pool depth in single-layer multi-track fiber laser cladding

The International Journal of Advanced Manufacturing Technology, Год журнала: 2024, Номер 135(7-8), С. 3591 - 3613

Опубликована: Окт. 30, 2024

Язык: Английский

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A review on physics-informed machine learning for process-structure-property modeling in additive manufacturing

Journal of Manufacturing Processes, Год журнала: 2024, Номер 133, С. 524 - 555

Опубликована: Ноя. 30, 2024

Язык: Английский

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Multi-Laser Scan Assignment and Scheduling Optimization for Large Scale Metal Additive Manufacturing

IISE Transactions, Год журнала: 2024, Номер unknown, С. 1 - 16

Опубликована: Авг. 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.

Язык: Английский

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Multi-material fabrication and compressive strength-optimization of reinforced-thermoset structures for mechanical power transmission

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

Опубликована: Сен. 24, 2024

Язык: Английский

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

Springer series in reliability engineering, Год журнала: 2024, Номер unknown, С. 5 - 23

Опубликована: Янв. 1, 2024

Язык: Английский

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

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Авг. 5, 2024

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.

Язык: Английский

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