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Advancements in Friction Stir-Based Solid-State Additive Manufacturing: Mechanisms, Microstructures, and Properties

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: 20 July 2026 | Viewed by 5309

Special Issue Editors

Dr. Ruishan Xie

E-Mail Website
Guest Editor
Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China
Interests: preparation and performance research of composite materials; solid-phase additive manufacturing and simulation
Special Issues, Collections and Topics in MDPI journals
Dr. Runsheng Li

E-Mail Website
Guest Editor
College of Mechanical and Electronic Engineering, China University of Petroleum (East China), Qingdao, China
Interests: solid-state additive manufacturing; aluminum alloys; path planning; simulation; deep learning

Special Issue Information

Dear Colleagues,

Solid-state additive manufacturing presents a promising alternative to traditional subtractive methods and certain additive techniques, facilitating the creation of complex geometries with reduced waste and enhanced material efficiency. Friction stir techniques, in particular, consolidate materials without complete melting by utilizing the heat generated from friction between a rotating tool and the workpiece. This process helps maintain the microstructural integrity and mechanical properties of alloys and composites.

This Special Issue seeks to compile the latest research and advancements in friction-based solid-state additive manufacturing, with an emphasis on process mechanisms, microstructures, and properties. These manufacturing methods have gained prominence due their ability to manufacture components with excellent properties and high efficiency; however, their full potential in the manufacturing arena is still being explored. We encourage submissions that highlight innovative developments, tackle significant challenges, and investigate future applications of these technologies.

Dr. Ruishan Xie
Dr. Runsheng Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Materials is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • friction stir-based additive manufacturing
  • process mechanisms
  • microstructure
  • properties
  • numercial simulation
  • process control

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Published Papers (3 papers)

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Research

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16 pages, 27751 KB  
Article
Microstructure and Properties of CNTs/2A12 Aluminum Matrix Composites Fabricated via Additive Friction Stir Deposition
by Zhiguo Lei, Mengran Zhou, Jiasheng Cao, Gaoqiang Chen, Shicheng Xu, Yu Xue, Yating Zhang and Qingyu Shi
Materials 2026, 19(1), 112; https://doi.org/10.3390/ma19010112 - 29 Dec 2025
Viewed by 718
Abstract
Carbon nanotubes/2Al2 composites, due to their low density, high specific strength, and high elastic modulus, are representative lightweight structural materials for next-generation aerospace applications. Traditional processing methods are inefficient and have long production cycles, making them unsuitable for the demands of efficient, rapid, [...] Read more.
Carbon nanotubes/2Al2 composites, due to their low density, high specific strength, and high elastic modulus, are representative lightweight structural materials for next-generation aerospace applications. Traditional processing methods are inefficient and have long production cycles, making them unsuitable for the demands of efficient, rapid, and intelligent manufacturing of complex structures. This article proposes the use of metal additive manufacturing technology to solve this problem. For the first time, a 22 mm high carbon nanotube/2Al2 composite was fabricated using additive friction stir deposition, and the changes in surface morphology, microstructure, mechanical properties, and corrosion resistance of the as-deposited composite were systematically studied. After additive manufacturing, the composite exhibited a continuous and defect-free, typical onion-like structure. The as-deposited microstructure consists of uniformly equiaxed grains with an average grain size of 1.23 μm to 1.62 μm and uniformly distributed Al2Cu particles. The tensile strength and elongation of the as-deposited composite in both the transverse and processing directions are no less than 450 MPa and 15%, respectively, superior to those of the base material. After additive manufacturing, the as-deposited composite exhibited a corrosion current density of 0.19 μA cm−2 in the transverse direction—only 4% of that of the base material. This enhanced corrosion resistance is attributed to the uniform distribution of precipitated phases achieved through additive manufacturing, which suppresses micro-galvanic corrosion, resulting in minimal, uniform corrosion. This study provides a research foundation and technical support for the additive manufacturing of aluminum-based composites. Full article
(This article belongs to the Special Issue Advancements in Friction Stir-Based Solid-State Additive Manufacturing: Mechanisms, Microstructures, and Properties)
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19 pages, 3609 KB  
Article
Machine Learning-Driven Multi-Objective Optimization of Bead Geometry and Energy Efficiency in Laser–Arc Hybrid Additive Manufacturing
by Chunyang Xia, Kui Zeng, Jiawei Ning, Yaoyu Ding and Yonghui Liu
Materials 2025, 18(24), 5560; https://doi.org/10.3390/ma18245560 - 11 Dec 2025
Viewed by 2480
Abstract
Laser–arc hybrid additive manufacturing (LAHAM) combines the benefits of arc-based deposition and laser precision but involves complex, nonlinear process interactions that challenge the prediction and control of bead geometry and energy consumption. This study develops a machine learning (ML) framework to predict bead [...] Read more.
Laser–arc hybrid additive manufacturing (LAHAM) combines the benefits of arc-based deposition and laser precision but involves complex, nonlinear process interactions that challenge the prediction and control of bead geometry and energy consumption. This study develops a machine learning (ML) framework to predict bead width, height, and Deposition volume per unit energy (DVUE) in LAHAM. Using experimental data, multiple regression models—including Support Vector Regression, Gaussian Process Regression, Neural Networks, and XGBoost—were trained and evaluated. Gaussian Process Regression (GPR) demonstrated superior performance in capturing nonlinear relationships and was further optimized using Bayesian Optimization and Particle Swarm Optimization. The optimized GPR models were integrated with the NSGA-II multi-objective optimization algorithm to simultaneously minimize geometric deviations and maximize DVUE. Results show that the proposed approach effectively identifies Pareto-optimal process parameters, achieving a balance between deposition accuracy and energy utilization rate, thereby providing a reliable and intelligent strategy for process optimization in hybrid additive manufacturing. Full article
(This article belongs to the Special Issue Advancements in Friction Stir-Based Solid-State Additive Manufacturing: Mechanisms, Microstructures, and Properties)
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Review

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38 pages, 9342 KB  
Review
Monitoring and Control of the Direct Energy Deposition (DED) Additive Manufacturing Process Using Deep Learning Techniques: A Review
by Yonghui Liu, Haonan Ren, Qi Zhang, Peng Yuan, Hui Ma, Yanfeng Li, Yin Zhang and Jiawei Ning
Materials 2026, 19(1), 89; https://doi.org/10.3390/ma19010089 - 25 Dec 2025
Cited by 2 | Viewed by 1477
Abstract
Directed Energy Deposition (DED), as a core branch of additive manufacturing, encompasses two typical processes: laser directed energy deposition (LDED) and wire and arc additive manufacturing (WAAM), which are widely used in manufacturing aerospace engine blades and core components of high-end equipment. In [...] Read more.
Directed Energy Deposition (DED), as a core branch of additive manufacturing, encompasses two typical processes: laser directed energy deposition (LDED) and wire and arc additive manufacturing (WAAM), which are widely used in manufacturing aerospace engine blades and core components of high-end equipment. In recent years, with the increasing adoption of deep learning (DL) technologies, the research focus in DED has gradually shifted from traditional “process parameter optimization” to “AI-driven process optimization” and “online real-time monitoring”. Given the complex and distinct influence mechanisms of key parameters (such as laser power/arc current, scanning/travel speed) on melt pool behavior and forming quality in the two processes, the introduction of artificial intelligence to address both common and specific issues has become particularly necessary. This review systematically summarizes the application of DL techniques in both types of DED processes. It begins by outlining DL frameworks, such as artificial neural networks (ANNs), recurrent neural networks (RNNs), convolutional neural networks (CNNs), and reinforcement learning (RL), and their compatibility with DED data. Subsequently, it compares the application scenarios, monitoring accuracy, and applicability of AI in DED process monitoring across multiple dimensions, including process parameters, optical, thermal fields, acoustic signals, and multi-sensor fusion. The review further explores the potential and value of DL in closed-loop parameter adjustment and reinforcement learning control. Finally, it addresses current bottlenecks such as data quality and model interpretability, and outlines future research directions, aiming to provide theoretical and engineering references for the intelligent upgrade and quality improvement of both DED processes. Full article
(This article belongs to the Special Issue Advancements in Friction Stir-Based Solid-State Additive Manufacturing: Mechanisms, Microstructures, and Properties)
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