Machine Intelligence in Welding and Additive Manufacturing

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

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 10081

Special Issue Editors

Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506, USA
Interests: smart manufacturing; machine learning; human-robot collaboration
School of Mechanical, Material, Mechatronic and Biomedical Engineering, Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, NSW 2522, Australia
Interests: programming for industrial robots; wire arc additive manufacturing (WAAM); industrial automation
Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506, USA
Interests: applied machine vision; control systems; manufacturing processes; robotic welding
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Special Issue Information

Dear Colleagues,

Arc welding and wire arc additive manufacturing (WAAM) use arcs to melt/process workpieces. While arc welding joins separate workpieces together, WAAM joins the added materials to the workpieces to grow it. All such processes share a common mechanism: arc melts/heats/processes the workpiece forming a weld pool whose dimension/shape/cooling decides the outputs of concern. This is an extremely complex process involving complex heat/mass transport and metallurgical reactions with many variables to determine the outputs of concern.

Producing desirable outputs requires the ability to predict and control process dynamics, upon fundamental knowledge about how process parameters/operations affect the dynamics. Experimental approaches to acquire such knowledge first conduct experiments to measure carefully chosen phenomena, which are not only measurable but most importantly believed to be capable of fully determining the outputs. Because of the complexity of the underlying physics, it is not straightforward how the measured phenomena, although sufficient and redundant, determine the outputs and what plays fundamental roles in the measured phenomena.

Conventional approaches propose/define features or characteristic variables to represent the measured phenomena to determine the outputs. Features are extracted/computed from the measured phenomena and correlated to the outputs through data fitting using chosen model structures that can be linear or nonlinear, parametric or non-parametric, and can be as simple as linear regression models or as complex as multi-layer artificial neural networks. Despite the variation in the model form and complexity, they share a common basis: the features are manually selected, and changing to new features to improve the modeling is a discontinued process requiring human intervention. The resultant modeling process is thus labor-intensive, not automated, and the best results are not easily achievable.

Deep learning (DL) networks are capable of automatically generating different “features” so that proposing features and evaluating the degree of success and effectiveness are automated toward the best results. To provide the capability to generate different features, such networks are typically very complex in structure and require a large amount of data to train. Recent advancements in deep learning have resulted in not only capable structures but also effective training approaches. This opens opportunities to operate arc welding and WAAM processes for higher quality and productivity in innovative ways. To share the experience in taking advantage of this historical opportunity to advance the arc welding and WAAM process, this Special Issue calls for papers that innovatively use the deep learning approach to better solve existing challenges in arc welding and WAAM and to lead to more effective arc welding and WAAM processes. Relevant topics include but are not limited to:

  • DL for feature extraction and representation of welding state evolution
  • DL for modeling the process-quality relationship toward real-time quality prediction
  • Data-driven optimization and learning techniques for adaptive process control
  • Robotic welding perception, modeling, optimization, and control
  • DL for analyzing sophisticated operations by skilled human welders in complex welding
  • Transfer learning for improving the generalization of DL models in prediction and control
  • Big data processing, learning, automated network architecture search and refining for online, real-time decision making

Dr. Peng Wang
Prof. Dr. Zengxi Pan
Prof. Dr. YuMing Zhang
Guest Editors

Manuscript Submission Information

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Published Papers (3 papers)

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13 pages, 2370 KiB  
Article
A New Perspective of Post-Weld Baking Effect on Al-Steel Resistance Spot Weld Properties through Machine Learning and Finite Element Modeling
J. Manuf. Mater. Process. 2023, 7(1), 6; https://doi.org/10.3390/jmmp7010006 - 28 Dec 2022
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Abstract
The root cause of post-weld baking on the mechanical performance of Al-steel dissimilar resistance spot welds (RSWs) has been determined by machine learning (ML) and finite element modeling (FEM) in this study. A deep neural network (DNN) model was constructed to associate the [...] Read more.
The root cause of post-weld baking on the mechanical performance of Al-steel dissimilar resistance spot welds (RSWs) has been determined by machine learning (ML) and finite element modeling (FEM) in this study. A deep neural network (DNN) model was constructed to associate the spot weld performance with the joint attributes, stacking materials, and other conditions, using a comprehensive experimental dataset. The DNN model positively identified that the post-weld baking reduces the joint performance, and the extent of degradation depends on the thickness of stacking materials. A three-dimensional finite element (FE) model was then used to investigate the root cause and the mechanism of the baking effect. It revealed that the formation of high thermal stresses during baking, from the mismatch of thermal expansion between steel and Al alloy, causes damage and cracking of the brittle intermetallic compound (IMC) formed at the interface of the weld nugget during welding. This in turn reduces the joint performance by promoting undesirable interfacial fracture when the welds were subjected to externally applied loads. The FEM model further revealed that increase in structural stiffness, because of increase in steel sheet thickness, reduces the thermal stresses at the interface caused by the thermal expansion mismatch and consequently lessens the detrimental effect of post-weld baking on the joint performance. Full article
(This article belongs to the Special Issue Machine Intelligence in Welding and Additive Manufacturing)
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13 pages, 19741 KiB  
Article
Top Surface Roughness Modeling for Robotic Wire Arc Additive Manufacturing
J. Manuf. Mater. Process. 2022, 6(2), 39; https://doi.org/10.3390/jmmp6020039 - 21 Mar 2022
Cited by 7 | Viewed by 3755
Abstract
Wire Arc Additive Manufacturing (WAAM) has many applications in fabricating complex metal parts. However, controlling surface roughness is very challenging in WAAM processes. Typically, machining methods are applied to reduce the surface roughness after a part is fabricated, which is costly and ineffective. [...] Read more.
Wire Arc Additive Manufacturing (WAAM) has many applications in fabricating complex metal parts. However, controlling surface roughness is very challenging in WAAM processes. Typically, machining methods are applied to reduce the surface roughness after a part is fabricated, which is costly and ineffective. Therefore, controlling the WAAM process parameters to achieve better surface roughness is important. This paper proposes a machine learning method based on Gaussian Process Regression to construct a model between the WAAM process parameters and top surface roughness. In order to measure the top surface roughness of a manufactured part, a 3D laser measurement system is developed. The experimental datasets are collected and then divided into training and testing datasets. A top surface roughness model is then constructed using the training datasets and verified using the testing datasets. Experimental results demonstrate that the proposed method achieves less than 50 μm accuracy in surface roughness prediction. Full article
(This article belongs to the Special Issue Machine Intelligence in Welding and Additive Manufacturing)
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Review

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22 pages, 9645 KiB  
Review
Key Technology of Intelligentized Welding Manufacturing and Systems Based on the Internet of Things and Multi-Agent
J. Manuf. Mater. Process. 2022, 6(6), 135; https://doi.org/10.3390/jmmp6060135 - 05 Nov 2022
Cited by 1 | Viewed by 3211
Abstract
With the development of the Internet of Things (IoT), Big Data, Artificial Intelligence technology, and the emergence of modern information technologies such as intelligent manufacturing, welding systems are changing, and intelligentized welding manufacturing and systems (IWMS) utilizing these technologies are attracting attention from [...] Read more.
With the development of the Internet of Things (IoT), Big Data, Artificial Intelligence technology, and the emergence of modern information technologies such as intelligent manufacturing, welding systems are changing, and intelligentized welding manufacturing and systems (IWMS) utilizing these technologies are attracting attention from both academia and industry. This paper investigates sensing technology, multi-information sensor fusion technology, feature recognition technology, the quality prediction method, control method, and intelligent welding production line application in the IWMS. Combining IoT technology and multi-agent systems, a hierarchical structure model welding manufacturing system (IoT-MAS) in the form of “leader-following” was constructed. The multi-agent welding manufacturing system has the advantages of distribution, intelligence, internal coordination and so on. The IoT-MAS consists of several sub-agents, which are divided into five categories according to their functions and internal processing logic. Combined with the functions of the intelligent welding manufacturing system, the agent structure of the whole welding process was proposed, and the matching communication technology and algorithm were designed. The intelligent welding manufacturing system based on IoT-MAS proposed in this paper can effectively solve the integrated design problem of large welding manufacturing systems. Full article
(This article belongs to the Special Issue Machine Intelligence in Welding and Additive Manufacturing)
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