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Advanced Welding Process Development for Metals and Steels: Numerical Analysis, Process Optimization, and Joint Performance

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

Deadline for manuscript submissions: 20 August 2026 | Viewed by 5644

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Special Issue Editors

Dr. Lin Wang

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Guest Editor

School of Materials and Physics, China University of Mining and Technology, Xuzhou, China
Interests: additive manufacturing; efficient arc welding; laser cladding; numerical simulation and simulation; arc wire additive manufacturing (WAAM); laser cladding of high-entropy alloys; high-speed arc welding; welding of dissimilar materials; machine learning; numerical simulation analysis

Dr. Tianqing Li

E-Mail Website
Guest Editor Assistant

School of Materials Science and Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, China
Interests: arc welding; laser beam welding; additive manufacturing; numerical analysis of welding process; advanced material connection methods; plasma welding; laser welding; laser-arc hybrid welding process

Special Issue Information

Dear Colleagues,

The development of advanced welding processes for metals and steels is a critical area of research in materials engineering, driven by the demand for high-performance joints in industries such as aerospace, automotive, and energy. Welding, a fundamental manufacturing technique, joins metallic components by applying heat, pressure, or both, often under complex conditions. Advanced welding processes—such as laser welding, friction stir welding, and hybrid techniques—offer improved precision, efficiency, and mechanical properties, addressing various limitations.

This Special Issue aims to advance the understanding and application of innovative welding technologies in materials engineering. It seeks to explore cutting-edge processes—such as laser welding, friction stir welding, and hybrid methods—focusing on their development for metals and steels. By integrating numerical analysis, this Issue aims to provide insights into heat transfer, material behavior, and stress distribution, facilitating process optimization. Emphasis will be placed on enhancing joint performance, including strength, durability, and resistance to environmental degradation, critical for industries like aerospace, automotive, and energy. This Special Issue invites original research and reviews to bridge theoretical advancements and practical solutions, fostering sustainable manufacturing and supporting the evolution of high-performance materials in modern applications.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following: welding processes, numerical analysis, process optimization, and joint performance

We look forward to receiving your contributions.

Dr. Lin Wang
Guest Editor

Dr. Tianqing Li
Guest Editor Assistant

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Keywords

laser welding
arc welding
modeling
weld
joint performance

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Published Papers (6 papers)

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Research

33 pages, 14636 KB

Open AccessArticle

Automated and Low Computational Cost Thermo-Mechanical Simulation of Arbitrary GMAW T-Joint Welds Using a Moving Heat Source

by Sebastian Santarrosa-Rodriguez, Israel Martínez-Ramírez, Motomichi Yamamoto, Rocio A. Lizarraga-Morales, Felipe J. Torres, Isaí Espinoza-Torres and Víctor Manuel Vega-Gutierrez

Materials 2026, 19(5), 1021; https://doi.org/10.3390/ma19051021 - 6 Mar 2026

Viewed by 518

Abstract

Gas Metal Arc Welding (GMAW) is widely adopted in automated manufacturing industries where the accurate prediction of thermal fields and welding-induced distortions is essential to ensure joint integrity of the parts; however, finite element modeling, as the most reliable non-destructive predictive approach, remains time-consuming and highly user-specialized. This work presents an automated and low computational cost thermo-mechanical finite element methodology implemented in Ansys Parametric Design Language (APDL) for the parametric analysis of GMAW T-joints, integrating automated geometry generation, meshing, heat source implementation, and thermo-mechanical modeling for different beam and weld seam dimensions under continuous or intermittent single-pass configurations. A volume element selection strategy is introduced to limit heat input calculations to the active weld pool region, achieving up to a 50% computational time reduction while maintaining high predictive accuracy, in contrast with conventional and partial selection methods. Overall script performance was validated through temperature and displacement comparisons between the numerical and experimental results of two T-joint configurations using SM490A structural steel specimens. The results demonstrate that the developed macro provides a useful tool for automated thermo-mechanical welding analysis, significantly reducing model preparation effort while enabling the evaluation of parametric T-joint geometries and welding conditions with a low computational cost focus. Full article

(This article belongs to the Special Issue Advanced Welding Process Development for Metals and Steels: Numerical Analysis, Process Optimization, and Joint Performance)

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31 pages, 7962 KB

Open AccessArticle

Study on a Process Parameter-Driven Deep Learning Prediction Model for Multi-Physical Fields in Flange Shaft Welding

by Chaolong Yang, Zhiqiang Xu, Feiting Shi, Ketong Liu and Peng Cao

Materials 2026, 19(5), 995; https://doi.org/10.3390/ma19050995 - 4 Mar 2026

Viewed by 597

Abstract

Large flange shafts are the core load-bearing and connecting components of high-end equipment, and their welding multi-physical fields directly affect the quality and service safety of the components. Traditional experiments and finite element methods suffer from long cycles and low efficiency, which can hardly meet the demand for rapid prediction. Aiming at the fast and accurate prediction of welding temperature, deformation and residual stress, this study combines thermal–mechanical coupled finite element simulation with machine learning to construct and compare a variety of prediction models. A dataset is built based on simulation data from 100 groups of process parameters. Overfitting is reduced through strategies including early stopping and dropout, and models such as MLP, RF, RBF-SVR, TabNet, XGBoost, and FT-Transformer are established and verified through 10-fold cross-validation. The results show that the MLP model performs best in the prediction of temperature, deformation and residual stress, and is in good agreement with the simulation values. The prediction errors of the peak temperature of the weld and base metal are below 5%, and the errors of deformation and residual stress are controlled within 10%. The average error of peak residual stress is about 6 MPa, and the deviation of most samples is less than 5 MPa. The RF model ranks second in accuracy, with an average error of about 6.5 MPa for peak residual stress, showing a satisfactory interpretability and engineering applicability. RBF-SVR and TabNet can meet basic prediction requirements. Under the small-sample condition in this work, XGBoost and FT-Transformer present relatively large errors and a weak generalization ability, making it difficult to achieve high-precision prediction. The MLP model established in this paper can effectively reproduce the evolution of welding multi-physical fields and supports the rapid prediction and process optimization of large flange shaft welding. The generalization ability and practical performance of the model can be further improved by expanding the dataset and experimental verification in the future. Full article

(This article belongs to the Special Issue Advanced Welding Process Development for Metals and Steels: Numerical Analysis, Process Optimization, and Joint Performance)

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17 pages, 42344 KB

Open AccessArticle

Effect of Heat Input on the Hydrogen Embrittlement Sensitivity of CGHAZ of X60 Pipeline Steel

by Longwei Zhang, Zhongwen Wu, Wenhao Zhou, Qingxue Zhang, Ba Li, Zhihui Zhang, Bing Wang, Qingyou Liu, Shujun Jia and Shubiao Yin

Materials 2026, 19(5), 961; https://doi.org/10.3390/ma19050961 - 2 Mar 2026

Viewed by 418

Abstract

In the coarse grain heat-affected zone (CGHAZ) of welded pipe steel joints, hydrogen damage is a key factor limiting the high-pressure hydrogen transportation performance of the pipeline. This study employed multi-dimensional characterization methods (including microstructure, mechanical properties, and hydrogen distribution) to investigate the influence of welding heat input on the hydrogen embrittlement (HE) sensitivity of X60 pipeline steel in the CGHAZ. The results showed that as the heat input increased, the grains in the CGHAZ became coarser, and the microstructure changed from bainitic ferrite (BF) to granular bainite (GB) and polygonal ferrite (PF). Among them, the BF + GB composite structure had the best resistance to HE (HE sensitivity was 29.8%). At low heat input, the reversible hydrogen distribution occurred at the interfaces between the grain boundaries and the BF blocks, while at high heat input, it would accumulate around the martensite/austenite (M/A) constituents. For the 16 kJ/cm heat input experimental steel, the increase in Σ3 grain boundary density accelerated hydrogen diffusion and reduced its enrichment, thereby resulting in the lowest HE sensitivity. Full article

(This article belongs to the Special Issue Advanced Welding Process Development for Metals and Steels: Numerical Analysis, Process Optimization, and Joint Performance)

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31 pages, 5036 KB

Open AccessArticle

Multiaxial Fatigue Life Assessment of Large Welded Flange Shafts: A Continuum Damage Mechanics Approach

by Zhiqiang Xu, Chaolong Yang, Feiting Shi, Wenzheng Liu, Na Xu, Zengliang Hu, Chuanqi Li, Ketong Liu, Peng Cao and Di Wang

Materials 2025, 18(24), 5528; https://doi.org/10.3390/ma18245528 - 9 Dec 2025

Cited by 1 | Viewed by 770

Abstract

This study develops a unified continuum damage mechanics (CDM) model for high-cycle fatigue life prediction of large manually arc-welded flange shafts manufactured from 45Mn steel (quenched and tempered) under combined bending–torsion loading. Fatigue tests revealed consistent crack initiation at the weld toe, with multiaxial loading reducing fatigue life by 35–42% compared to pure bending. The CDM parameters were calibrated against experimental data and implemented through an ABAQUS 2021 UMAT subroutine, achieving prediction errors below 5%—significantly outperforming conventional nominal and hotspot stress methods. For high-cycle fatigue conditions, a simplified CDM model neglecting plastic damage maintained engineering accuracy while improving computational efficiency by 3–5 times. The damage variable D = 0.9 was identified as a universal threshold for accelerated damage progression. These findings provide quantitative basis for multiaxial fatigue design and structural health monitoring of large welded components. Full article

(This article belongs to the Special Issue Advanced Welding Process Development for Metals and Steels: Numerical Analysis, Process Optimization, and Joint Performance)

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20 pages, 9797 KB

Open AccessArticle

The Laser Welding Research of Dissimilar Materials Between AlCoCrFeNi2.1 Eutectic High-Entropy Alloy and GH3030 Nickel-Based Alloy

by Anmin Liu, Ze An, Bin Wang, Hailin Qiao, Keming Chang and Yu Fan

Materials 2025, 18(21), 4970; https://doi.org/10.3390/ma18214970 - 31 Oct 2025

Cited by 1 | Viewed by 938

Abstract

Dissimilar material welding enables the integration of the superior properties of different materials, thereby achieving optimal structural performance and economic efficiency while meeting specific service requirements. The presence of solid-solution strengthening elements such as Ti, Co, and Al, and trace elements such as P and S, in GH3030 nickel-based superalloy leads to their segregation and the formation of intermetallic compounds in the welded joint, resulting in deterioration of joint performance. High-entropy alloys (HEAs), with their high-entropy effect and delayed diffusion effect working synergistically, can effectively suppress compositional segregation caused by uneven elemental diffusion and the formation of intermetallic compounds at interfaces, thereby improving the quality of welded joints and demonstrating great potential for dissimilar material joining. Therefore, in this study, fiber laser welding was used to effectively join AlCoCrFeNi2.1 eutectic high-entropy alloy and GH3030 nickel-based superalloy, with the expectation to improve welded joint element segregation, suppressing the formation of intermetallic compounds, and enhance the welded joint quality and its performance. The AlCoCrFeNi2.1/GH3030 joint exhibits an average yield strength of 1.31 GPa, which is significantly higher than that of the GH3030/GH3030 joint (1.07 GPa). In addition, the AlCoCrFeNi2.1/GH3030 joint shows a higher average work-hardening exponent of 0.337 compared with 0.30 for the GH3030/GH3030 joint, indicating improved plasticity. The results showed that under appropriate welding process parameters, the hardness of the weld zone, transitioning from the nickel-based superalloy to the eutectic high-entropy alloy, exhibited a stable increasing trend, and the joint exhibits good plasticity, with brittle fracture being unlikely. Full article

(This article belongs to the Special Issue Advanced Welding Process Development for Metals and Steels: Numerical Analysis, Process Optimization, and Joint Performance)

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22 pages, 4837 KB

Open AccessArticle

Predictive Correlation Between Hardness and Tensile Properties of Submerged Arc Welded API X70 Steel

by Ali Lahouel, Sameh Athmani, Amel Sedik, Adel Saoudi, Regis Barille, Lotfi Khezami, Ahlem Guesmi and Mamoun Fellah

Materials 2025, 18(19), 4482; https://doi.org/10.3390/ma18194482 - 25 Sep 2025

Viewed by 1849

Abstract

This research investigates the statistical correlation between Vickers hardness and tensile properties of helical submerged arc welded high-strength low-alloy (HSLA) API X70 pipeline steel. Tensile tests were performed on cross-weld joints from 138 pipe specimens. Vickers hardness measurements were also conducted on 138 samples to evaluate the hardness distribution across the base metal, fusion zone, and heat-affected zone. Results show that the fusion zone exhibits the highest hardness, correlating with enhanced tensile strength (R² = 82%). Linear regression models indicate that base metal hardness significantly influences yield strength (R² = 71%), while moderate negative correlations exist with elongation (R² = 54%). These findings suggest that hardness measurements can serve as a non-destructive predictive tool for tensile properties, improving weld quality and mechanical performance. This research provides empirical models that enhance the application of API X70 in critical engineering applications, improving pipeline safety and reliability. Full article

(This article belongs to the Special Issue Advanced Welding Process Development for Metals and Steels: Numerical Analysis, Process Optimization, and Joint Performance)

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