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Metals
Special Issues
Quality Assessment and Process Management of Welded Joints
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Quality Assessment and Process Management of Welded Joints

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Welding and Joining".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 17875

Special Issue Editor

Prof. Dr. António Bastos Pereira

E-Mail Website1 Website2
Guest Editor
Centre for Mechanical Technology and Automation, University of Aveiro, Campus Santiago, 3810-193 Aveiro, Portugal
Interests: adhesive joints; adhesive strength; mechanical testing; manufacturing processes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to express the maturity and dynamism of welding, particularly on issues related to quality. As an example, quality management in welded construction involves well-regulated procedures which are, however, so vast that they are of great complexity. In fact, there are hundreds of rules to apply to each specific case. This makes it imperative to have a welding coordinator, at least in cases of greater responsibility. In fact, the application of the rules presents yet another difficulty: Along with the breadth of the rules, each country may adopt certain special requirements in their own case. Thus, it is essential to know the necessary path for the proper development of responsible work. First, one must know the various welding processes, their advantages and limitations; then, one must identify the regulations applicable to the work concerned; and finally, one must know what tests and qualifications are required, including the expected defects and acceptance criteria. The purposes of this Special Issue are: (i) to show the developments on new welding technologies and their advantages on welding quality; (ii) to identify processes and procedures to comply with quality rules and standards; (iii) to find ways to check, identify, evaluate, and guarantee the quality of welded joints; (iv) to show existing standardization on the world about welding; and (v) to share weld quality testing protocols, among others.

Prof. Dr. António Bastos Pereira
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Metals 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 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

  • Quality in welding
  • Quality testing protocols
  • Quality assessment
  • Monitoring systems of welded joints
  • Monitoring systems of welding
  • Weld quality control
  • Assurance of welded structures
  • Manufacturing and conformity of welded products

Published Papers (4 papers)

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Research

11 pages, 2883 KiB  
Article
Investigation of the Extrapolation Capability of an Artificial Neural Network Algorithm in Combination with Process Signals in Resistance Spot Welding of Advanced High-Strength Steels
by Bassel El-Sari, Max Biegler and Michael Rethmeier
Metals 2021, 11(11), 1874; https://doi.org/10.3390/met11111874 - 22 Nov 2021
Cited by 10 | Viewed by 2275
Abstract
Resistance spot welding is an established joining process for the production of safety-relevant components in the automotive industry. Therefore, consecutive process monitoring is essential to meet the high quality requirements. Artificial neural networks can be used to evaluate the process parameters and signals, [...] Read more.
Resistance spot welding is an established joining process for the production of safety-relevant components in the automotive industry. Therefore, consecutive process monitoring is essential to meet the high quality requirements. Artificial neural networks can be used to evaluate the process parameters and signals, to ensure individual spot weld quality. The predictive accuracy of such algorithms depends on the provided training data set, and the prediction of untrained data is challenging. The aim of this paper was to investigate the extrapolation capability of a multi-layer perceptron model. That means, the predictive performance of the model was tested with data that clearly differed from the training data in terms of material and coating composition. Therefore, three multi-layer perceptron regression models were implemented to predict the nugget diameter from process data. The three models were able to predict the training datasets very well. The models, which were provided with features from the dynamic resistance curve predicted the new dataset better than the model with only process parameters. This study shows the beneficial influence of process signals on the predictive accuracy and robustness of artificial neural network algorithms. Especially, when predicting a data set from outside of the training space. Full article
(This article belongs to the Special Issue Quality Assessment and Process Management of Welded Joints)
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17 pages, 4256 KiB  
Article
Weld-Quality Prediction Algorithm Based on Multiple Models Using Process Signals in Resistance Spot Welding
by Sehyeon Kim, Insung Hwang, Dong-Yoon Kim, Young-Min Kim, Munjin Kang and Jiyoung Yu
Metals 2021, 11(9), 1459; https://doi.org/10.3390/met11091459 - 15 Sep 2021
Cited by 8 | Viewed by 2980
Abstract
An efficient nondestructive testing method of resistance spot weld quality is essential in evaluating the weld quality of all welded joints in the automotive components of a car body production line. This study proposes a quality prediction algorithm for resistance spot welding that [...] Read more.
An efficient nondestructive testing method of resistance spot weld quality is essential in evaluating the weld quality of all welded joints in the automotive components of a car body production line. This study proposes a quality prediction algorithm for resistance spot welding that can predict the geometrical and physical properties of a spot-welded joint and evaluate weld quality based on quality acceptance criteria. To this end, four statistical models that predict the main geometrical and physical properties of a spot-welded joint, including tensile shear strength, indentation depth, expulsion occurrence, and failure mode, were estimated based on material information, dynamic resistance, and electrode displacement signals. The significance of the estimated models was then verified through an analysis of variance. The prediction accuracies of the models were 94.3%, 93.4%, 97.5%, and 85.0% for the tensile shear strength, indentation depth, expulsion occurrence, and failure modes, respectively. A weld quality evaluation methodology that can predict the properties of a spot-welded joint and evaluate the overall quality requirements based on authorized welding standards was proposed using the four statistical models. Full article
(This article belongs to the Special Issue Quality Assessment and Process Management of Welded Joints)
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19 pages, 29539 KiB  
Article
Real-Time Weld Gap Monitoring and Quality Control Algorithm during Weaving Flux-Cored Arc Welding Using Deep Learning
by Chengnan Jin and Sehun Rhee
Metals 2021, 11(7), 1135; https://doi.org/10.3390/met11071135 - 18 Jul 2021
Cited by 4 | Viewed by 5957
Abstract
In the flux-cored arc welding process, which is most widely used in shipbuilding, a constant
external weld bead shape is an important factor in determining proper weld quality; however, the
size of the weld gap is generally not constant, owing to errors generated [...] Read more.
In the flux-cored arc welding process, which is most widely used in shipbuilding, a constant
external weld bead shape is an important factor in determining proper weld quality; however, the
size of the weld gap is generally not constant, owing to errors generated during the shell forming
process; moreover, a constant external bead shape for the welding joint is difficult to obtain when
the weld gap changes. Therefore, this paper presents a method for monitoring the weld gap and
controlling the weld deposition rate based on a deep neural network (DNN) for the automation
of the hull block welding process. Welding experiments were performed with a welding robot
synchronized with the welding machine, and the welding quality was classified according to the
experimental results. Welding current and voltage signals, as the robot passed through the weld
seam, were measured using a trigger device and analyzed in the time domain and frequency domain,
respectively. From the analyzed data, 24 feature variables were extracted and used as input for the
proposed DNN model. Consequently, the offline and online performance verification results for new
experimental data using the proposed DNN model were 93% and 85%, respectively Full article
(This article belongs to the Special Issue Quality Assessment and Process Management of Welded Joints)
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16 pages, 10663 KiB  
Article
Real-Time Detection of Weld Defects for Automated Welding Process Base on Deep Neural Network
by Seungmin Shin, Chengnan Jin, Jiyoung Yu and Sehun Rhee
Metals 2020, 10(3), 389; https://doi.org/10.3390/met10030389 - 18 Mar 2020
Cited by 37 | Viewed by 5451
Abstract
In the process of welding zinc-coated steel, zinc vapor causes serious porosity defects. The porosity defect is an important indicator of the quality of welds and degrades the durability and productivity of the weld. Therefore, this study proposes a deep neural network (DNN)-based [...] Read more.
In the process of welding zinc-coated steel, zinc vapor causes serious porosity defects. The porosity defect is an important indicator of the quality of welds and degrades the durability and productivity of the weld. Therefore, this study proposes a deep neural network (DNN)-based non-destructive testing method that can detect and predict porosity defects in real-time, based on welding voltage signal, without requiring additional device in gas metal arc welding (GMAW) process. To this end, a galvannealed hot-rolled high-strength steel sheet applied to automotive parts was used to measure process signals in real-time. Then, feature variables were extracted through preprocessing, and correlation between the feature variables and weld porosity was analyzed. The proposed DNN based framework outperformed the artificial neural network (ANN) model by 15% or more. Finally, an experiment was conducted by using the developed porosity detection and prediction system to evaluate its field application. Full article
(This article belongs to the Special Issue Quality Assessment and Process Management of Welded Joints)
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