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Recent Advances in Welding and Joining Metallic Materials
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Recent Advances in Welding and Joining Metallic Materials

A special issue of Journal of Manufacturing and Materials Processing (ISSN 2504-4494).

Deadline for manuscript submissions: 31 July 2026 | Viewed by 4255

Special Issue Editor

Dr. Kapil Gangwar

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Wentworth Institute of Technology, Boston, MA 02115, USA
Interests: friction stir welding and processing; high-pressure die casting; additive manufacturing and shape memory alloys; powder metallurgy and particle analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advanced welding and joining processes are pivotal to the evolving and continuously emerging landscape of advanced manufacturing and materials processing, particularly for monolithic metallic systems where exceptional prolonged performance is a key. With rising demands for structural efficiency, sustainability, and digital integration, it is incumbent upon us to develop and characterize joining and welding strategies that not only join materials, but that also control emerging microstructure, improve mechanical properties and enhance performance, and enable predictive quality assurance.

For this Special Issue of the Journal of Manufacturing and Materials Processing, we invite high-quality contributions focusing on innovative welding and joining methodologies that align with modern manufacturing paradigms and concurrent societal needs. Priority will be given to studies that demonstrate process–structure–property–performance–recyclability integration, smart process control, joining advanced or dissimilar alloys, and environmentally conscious or green welding technologies.

The main focus of interest are the following topics:

  • Solid-state welding (friction stir, linear friction, or ultrasonic);
  • Laser and hybrid welding with in situ monitoring;
  • Additive repair or joining processes combined with WAAM or DED;
  • Multi-scale modeling and digital twins for weld process optimization;
  • Sustainable welding practices, energy efficiency, and lifecycle analysis;
  • AI/ML-enhanced defect prediction, diagnostics, and adaptive control.

Research may address macro- and/or micro-scale phenomena, residual stresses, phase transformations, and the inter-relationship between materials design and welding processing parameters. We also invite state-of-the-art reviews and case studies demonstrating industry applications, particularly in the aerospace, automotive, energy, and defense fields.

Special topics to consider are as follows:

  • Intelligent welding and joining technologies;
  • Microstructural evolution and mechanical performance;
  • Dissimilar and lightweight alloy joining;
  • Friction-based and solid-state welding innovations;
  • Process simulation, digital twins, and data-driven models;
  • In situ diagnostics and non-destructive evaluation;
  • Sustainable and low-energy welding processes;
  • Residual stress engineering and performance modeling.

This Special Issue seeks to showcase transformative developments that bring together materials science, manufacturing innovation, and digital tools to define the next generation of welding and joining technologies.

We look forward to your contributions.

Dr. Kapil Gangwar
Guest Editor

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. Journal of Manufacturing and Materials Processing is an international peer-reviewed open access monthly 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 1800 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

  • intelligent welding and joining technologies
  • microstructural evolution and mechanical performance
  • dissimilar and lightweight alloy joining
  • friction-based and solid-state welding innovations
  • process simulation, digital twins, and data-driven models
  • in situ diagnostics and non-destructive evaluation
  • sustainable and low-energy welding processes
  • residual stress engineering and performance modeling

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

26 pages, 4321 KB  
Article
Automation of Ultrasonic Monitoring for Resistance Spot Welding Using Deep Learning
by Ryan Scott, Danilo Stocco, Sheida Sarafan, Lukas Behnen, Andriy M. Chertov, Priti Wanjara and Roman Gr. Maev
J. Manuf. Mater. Process. 2026, 10(3), 101; https://doi.org/10.3390/jmmp10030101 - 17 Mar 2026
Viewed by 704
Abstract
Reliable process monitoring and quality evaluation for resistance spot welding (RSW) have become more important now than ever. An ultrasonic probe embedded into welding electrodes has enabled the acquisition of data about molten pool formation throughout welding, but automation of high-performance ultrasonic data [...] Read more.
Reliable process monitoring and quality evaluation for resistance spot welding (RSW) have become more important now than ever. An ultrasonic probe embedded into welding electrodes has enabled the acquisition of data about molten pool formation throughout welding, but automation of high-performance ultrasonic data analyses is still necessary to fully realize a monitoring system. This work proposes a two-stage deep learning (DL) approach for automated ultrasonic data analysis for RSW processing monitoring. The first stage conducts semantic segmentation on ultrasonic M-scan welding process signatures, yielding masks for identified molten pool and stack regions from which weld penetration measurements can be directly extracted, as well as expulsion occurrences throughout welding. From input images and segmentation outputs, the second stage directly estimates resultant weld nugget diameters using an additional neural network. Both stages leveraged architectures based on TransUNet, mixing elements of both convolutional neural networks (CNN) and vision transformers, and the effect of cross-attention for stack-up sheet thickness data fusion was investigated via an ablation study. Additionally, in the diameter estimation stage, the ablation study included alternative feature extraction architectures in the network and investigated the provision of M-scans to the model alongside segmentation masks. In both cases, cross-attention was determined to improve performance, and in the case of diameter estimation, providing M-scans as input was found to be beneficial in general. With cross-attention, the segmentation approach yielded a mean intersection over union (IoU) of 0.942 on molten pool, stack, and expulsion regions in the M-scans with 13.4 ms inference time. With cross-attention, diameter estimates yielded a mean absolute error of 0.432 mm with 4.3 ms inference time, representing a significant improvement over algorithmic approaches based on ultrasonic time of flight. Additionally, the approach attained >90% probability of detection (POD) at 0.830 mm below the acceptable diameter threshold and <10% probability of false alarm (PFA) at 0.828 mm above the threshold. These results demonstrate a novel production-ready application of DL in ultrasonic nondestructive evaluation (NDE) and pave the way for zero-defect RSW manufacturing. Full article
(This article belongs to the Special Issue Recent Advances in Welding and Joining Metallic Materials)
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23 pages, 3484 KB  
Article
A Predictive Crater-Overlap Model for EDM Finishing Relevant to AISI 304 Welded Joints
by Mohsen Forouzanmehr, Mohammad Reza Dashtbayazi and Mahmoud Chizari
J. Manuf. Mater. Process. 2026, 10(2), 75; https://doi.org/10.3390/jmmp10020075 - 21 Feb 2026
Viewed by 631
Abstract
Electrical Discharge Machining (EDM) enables precision post-weld finishing of AISI 304 stainless steel, but stochastic spark overlaps make the fatigue-critical maximum peak-to-valley height (Rmax) difficult to predict. This study develops a validated physics-based framework quantifying how crater overlap governs R [...] Read more.
Electrical Discharge Machining (EDM) enables precision post-weld finishing of AISI 304 stainless steel, but stochastic spark overlaps make the fatigue-critical maximum peak-to-valley height (Rmax) difficult to predict. This study develops a validated physics-based framework quantifying how crater overlap governs Rmax evolution. Experiments on unwelded AISI 304 cylinders—proxying weld metal while excluding heat-affected zone (HAZ) effects—used Central Composite Design (20 trials, 900–9380 μJ discharge energies). Profilometry and scanning electron microscopy (SEM) correlated the crater size, overlap intensity, micro-cracking, and Rmax escalation from 18 to 85 μm. Primary and secondary crater formation under minimum and maximum overlap configurations were simulated using a 2D axisymmetric finite element model with Gaussian heat flux and temperature-dependent thermophysical properties. The predictive metric Rmax,num = (dinitial + dsecondary)/2 achieved 11–19% average error against the experimental Rmax,exp, with complementary valley depth (Rv) validation at 13% error. The Specimen 7 outlier (~50% error) reveals the limitations of deterministic modelling under stochastic debris accumulation and plasma instability at intermediate energies. Crater overlap generates secondary dimples, sharp inter-crater peaks, and rim micro-crack networks, driving the 4.7-fold Rmax increase—approaching International Institute of Welding (IIW) fatigue thresholds (<25 μm for high-cycle categories). The framework explicitly links the discharge energy, plasma channel radius (Rpc), and overlap geometry to surface topography, enabling process optimization (I·ton < 60 A·s maintains Rmax < 25 μm). Mesh independence (<2.5% convergence) and six centre-point replicates (CV = 4.2%) confirm robustness. This validated upper-bound Rmax predictor supports the digital co-optimization of welding and EDM parameters for aerospace/energy applications, with planned extensions to stochastic 3D models incorporating adaptive remeshing and real weld topographies. Full article
(This article belongs to the Special Issue Recent Advances in Welding and Joining Metallic Materials)
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14 pages, 2146 KB  
Article
Method for Determining the Contact and Bulk Resistance of Aluminum Alloys in the Initial State for Resistance Spot Welding
by Andreas Fezer, Stefan Weihe and Martin Werz
J. Manuf. Mater. Process. 2025, 9(8), 266; https://doi.org/10.3390/jmmp9080266 - 7 Aug 2025
Cited by 2 | Viewed by 2030
Abstract
In resistance spot welding (RSW), the total electrical resistance (dynamic resistance) as the sum of bulk and contact resistance is a key variable. Both of these respective resistances influence the welding result, but the exact ratio to the total resistance of a real [...] Read more.
In resistance spot welding (RSW), the total electrical resistance (dynamic resistance) as the sum of bulk and contact resistance is a key variable. Both of these respective resistances influence the welding result, but the exact ratio to the total resistance of a real existing sheet is not known. Due to the high scatter in the RSW of aluminum alloys compared to steel, it is of interest to be able to explicitly determine the individual resistance components in order to gain a better understanding of the relationship between the initial state and the lower reproducibility of aluminum welding in the future. So far, only the total resistance and the bulk resistance could be determined experimentally. Due to the different sample shapes, it was not possible to consistently determine the contact resistance from the measurements. In order to realize this, a method was developed that contains the following innovations with the aid of simulation: determination of the absolute bulk resistance at room temperature (RT), determination of the absolute contact resistance at RT and determination of the ratio of bulk and contact resistance. This method makes it possible to compare the resistances of the bulk material and the surface in the initial state quantitatively. This now allows the comparison of batches regarding the surface resistance, especially for welding processes. For the aluminum sheets (EN AW-5182-O, EN AW-6014-T4) investigated, the method showed that the contact resistance dominates and the bulk resistance is less than 20%. These data can also be used to make predictions about the weldability of the alloy using artificial intelligence (AI). If experimental data are available, the method can also be applied to higher temperatures. Full article
(This article belongs to the Special Issue Recent Advances in Welding and Joining Metallic Materials)
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