☆ 4.7 Review

Research and application of artificial intelligence techniques for wire arc additive manufacturing: a state-of-the-art review

ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING (2023)

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Review Automation & Control Systems

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Haogang Li et al.

Summary: This article reviews the mechanisms and properties of extreme pressure (EP) and antiwear (AW) additives in boundary lubrication and discusses the advantages and disadvantages of traditional and nanoparticle additives. It also studies the influence of non-polar chain length on traditional additives and the effects of nanoparticle structure parameters, concentration, and media polarity on properties.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2022)

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Article Automation & Control Systems

Vision-based melt pool monitoring for wire-arc additive manufacturing using deep learning method

Chunyang Xia et al.

Summary: This study proposes applying deep learning in visual monitoring to diagnose various anomalies during the wire-arc additive manufacturing process. Different representative CNN architectures were investigated and compared for their classification performance. The results demonstrate the effectiveness of CNN models in classifying different types of melt pool images.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2022)

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Article Engineering, Multidisciplinary

Rapid surface defects detection in wire and arc additive manufacturing based on laser profilometer

Cheng Huang et al.

Summary: In this study, a non-contact in-situ 3D laser profilometer inspection system was developed to automatically monitor and identify surface defects in the manufacturing process of large-scale metal components. The results showed that the proposed method can accurately detect common defects and small pore defects at the pixel level, which is of great significance for quality evaluation and process control in metal component manufacturing.

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Article Computer Science, Artificial Intelligence

Towards real-time in-situ monitoring of hot-spot defects in L-PBF: a new classification-based method for fast video-imaging data analysis

Matteo Bugatti et al.

Summary: A new framework for detecting defects in metal additive manufacturing processes using high-speed cameras has been proposed in this study. The method utilizes smart data extraction and analysis techniques to monitor process accuracy while maintaining computational efficiency. Experimental results demonstrate that the method is capable of quickly detecting defects while ensuring high computational speed.

JOURNAL OF INTELLIGENT MANUFACTURING (2022)

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Article Computer Science, Artificial Intelligence

Layer-by-layer model-based adaptive control for wire arc additive manufacturing of thin-wall structures

Haochen Mu et al.

Summary: This paper introduces an online layer-by-layer controller for improving the geometric accuracy of wire arc additive manufacturing (WAAM). The controller compensates for geometric errors by measuring the deposited geometry, comparing it with the CAD model, and generating new welding parameters. Two control strategies, PID algorithm and MPC algorithm, are proposed and evaluated. The research shows that increasing the complexity of the control algorithm improves the real-time control performance and reduces geometric fluctuations in the test component. The adaptiveness of the control strategy is also verified by accurately controlling the fabrication of a complex geometry part.

JOURNAL OF INTELLIGENT MANUFACTURING (2022)

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Article Engineering, Industrial

Real-time anomaly detection using convolutional neural network in wire arc additive manufacturing: Molybdenum material

Hae-Won Cho et al.

Summary: This study proposes a real-time anomaly detection method using a convolutional neural network (CNN) in wire arc additive manufacturing (WAAM). The results show that the MobileNetV2 model achieves the best classification accuracy and processing time.

JOURNAL OF MATERIALS PROCESSING TECHNOLOGY (2022)

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Article Computer Science, Interdisciplinary Applications

A digital twin hierarchy for metal additive manufacturing

A. Phua et al.

Summary: Digital twins are a conceptual framework for monitoring and controlling the product life-cycle using a simulated replica of the physical system. They have gained attention as an alternative to costly physical testing, utilizing high fidelity simulations, sensor data, and intelligent control. This paper evaluates the specific features of metal additive manufacturing (AM) and reviews the current literature on modeling, sensing, control, and machine intelligence. The proposed digital twin hierarchy for AM provides a developer framework for engineering digital twins for AM and other intelligent manufacturing systems.

COMPUTERS IN INDUSTRY (2022)

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Article Engineering, Multidisciplinary

Automatic defects detection and classification of low carbon steel WAAM products using improved remanence/magneto-optical imaging and cost-sensitive convolutional neural network

Xiang He et al.

Summary: A new method for automatic defects detection and classification of WAAM products using improved remanence/magneto-optical imaging and cost-sensitive convolutional neural network is proposed in this study. The method is effective in detecting the surface defects of low-carbon steel WAAM products.

MEASUREMENT (2021)

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Article Construction & Building Technology

Mechanical and X ray computed tomography characterisation of a WAAM 3D printed steel plate for structural engineering applications

Z. Al-Nabulsi et al.

Summary: The study investigates the 3D printing of steel in structural engineering using Wire and Arc Additive Manufacturing (WAAM) method, focusing on mechanical properties and internal structure analysis. The results show that WAAM steel meets the requirements for structural steel grade in building structures.

CONSTRUCTION AND BUILDING MATERIALS (2021)

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Comparative evaluation of supervised machine learning algorithms in the prediction of the relative density of 316L stainless steel fabricated by selective laser melting

German Omar Barrionuevo et al.

Summary: This study investigates predicting the relative density of 316L stainless steel 3D printed parts using machine learning algorithms, with gradient boosting regressor showing the highest accuracy among the compared regressors.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2021)

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Review Computer Science, Interdisciplinary Applications

A review of multiple degrees of freedom for additive manufacturing machines

Jingchao Jiang et al.

Summary: This paper reviews publications related to multi-DOF AM and aims to inspire researchers and engineers to further develop and improve multi-DOF AM systems. The findings and future research directions are also discussed in the final part of the paper.

INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING (2021)

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Review Computer Science, Artificial Intelligence

Artificial Neural Networks and Deep Learning in the Visual Arts: a review

Iria Santos et al.

Summary: This article provides an exhaustive analysis of Artificial Neural Networks and Deep Learning in the Visual Arts, highlighting their contributions in prediction, classification, generation, and identification. The investigation results show that their use in the Visual Arts continues to evolve and has recently experienced significant growth.

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A Comprehensive Survey on Transfer Learning

Fuzhen Zhuang et al.

Summary: Transfer learning aims to improve the performance of target learners by transferring knowledge from related source domains, reducing the reliance on target-domain data. This survey aims to systematize and summarize existing research studies in order to help readers understand the current status and ideas in the area of transfer learning.

PROCEEDINGS OF THE IEEE (2021)

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Article Metallurgy & Metallurgical Engineering

Adaptive Intelligent Welding Manufacturing

Y. M. Zhang et al.

Summary: Optimal design of the welding procedure aims to achieve desired welding results, but deviations from nominal conditions require adaptive adjustments of welding parameters. Human welders have limitations in making adjustments, while automated robotic systems require sensing, information extraction, and optimization capabilities to adapt. Additional challenges include extracting needed information from sensor data and the labor-intensive nature of such studies. Various approaches, such as machine learning, learning from skilled humans, and human-robot collaboration, are being explored to address these challenges in adaptive robotic welding.

WELDING JOURNAL (2021)

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Review Chemistry, Analytical

Learning for a Robot: Deep Reinforcement Learning, Imitation Learning, Transfer Learning

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Summary: Dexterous manipulation of robots is crucial for achieving intelligence, and recent advancements in deep learning and reinforcement learning have paved the way for robots to perform highly complex tasks. The future direction of research is focusing on autonomous decision-making and learning to achieve more flexible and efficient robot control.

SENSORS (2021)

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Analysis of GMAW process with deep learning and machine learning techniques

Rogfel Thompson Martinez et al.

Summary: GMAW (Gas Metal Arc Welding) is widely used in both ferrous and non-ferrous materials, with complex non-linear relationships between its initial and final parameters. Arc parameters are difficult to capture due to long image processing times, but recent advances in image processing and computational modeling have shown potential for optimizing processes and reducing costs. Deep learning techniques have proven effective in analyzing complex images and machine learning can contribute to predictive models by utilizing arc parameters obtained through deep learning techniques, demonstrating potential for real-time analysis in predicting GMAW processes.

JOURNAL OF MANUFACTURING PROCESSES (2021)

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Article Engineering, Manufacturing

Machine learning based layer roughness modeling in robotic additive manufacturing

Ahmed Yaseer et al.

Summary: Wire Arc Additive Manufacturing (WAAM) is a manufacturing technique that builds 3D parts layer by layer using metal deposition based on welding processes. Researchers have traditionally focused on weld bead characteristics in WAAM, but layer roughness is also crucial as it impacts machining costs and mechanical properties. This study successfully utilized two machine learning methods, Random Forest and Multilayer Perceptron (MLP), to accurately model surface roughness in WAAM using a weaving path. Random Forest outperformed MLP in terms of accuracy and computational time for predicting layer roughness based on robotic WAAM parameters.

JOURNAL OF MANUFACTURING PROCESSES (2021)

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Coordinated monitoring and control method of deposited layer width and reinforcement in WAAM process

Yiming Wang et al.

Summary: WAAM technology is widely used in metal manufacturing due to its low cost and high efficiency, but issues such as dimensional accuracy, layered morphology, and metallurgical defects limit its further application. This research proposes a method for extracting global information of molten pool and a control method to achieve coordinated monitoring and control of the width and reinforcement in the WAAM process. Experiments show satisfactory control accuracy, providing a feasible way for monitoring and controlling weld shape in the WAAM welding process.

JOURNAL OF MANUFACTURING PROCESSES (2021)

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Article Engineering, Manufacturing

Multi-layer ultrasonic imaging of as-built Wire plus Arc Additive Manufactured components

Rastislav Zimermann et al.

Summary: The study introduces a new method for inspecting non-planar Additively Manufactured components without the need for post-manufacturing machining, using a conformable phased array roller-probe. Experimental validation on two titanium WAAM components demonstrates the method's effectiveness in defect detection and its strong agreement with X-Ray CT data, confirming its capability for inspecting original WAAM components.

ADDITIVE MANUFACTURING (2021)

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Prediction of the interpass temperature of a wire arc additive manufactured wall: FEM simulations and artificial neural network

Francisco Werley Cipriano Farias et al.

Summary: Finite element method (FEM) simulations are used to analyze the effect of idle time on interpass temperature in wire arc additive manufacturing, and an artificial neural network (ANN) is developed for predictive modeling to help manufacturers achieve a balance between productivity and part behavior.

ADDITIVE MANUFACTURING (2021)

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Review Automation & Control Systems

A review on wire-arc additive manufacturing: typical defects, detection approaches, and multisensor data fusion-based model

Xi Chen et al.

Summary: WAAM technology integrates the advantages of additive manufacturing and traditional welding technology to form large metal components with low cost and high deposition rates. However, potential issues exist in morphological accuracy, microstructure, and properties due to complex thermal cycles. A multisensor data fusion-based quality control model is proposed to address these challenges.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2021)

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Article Automation & Control Systems

Hyperspectral imaging for prediction of surface roughness in laser powder bed fusion

Niklas Gerdes et al.

Summary: This article discusses the relevance of in situ quality assurance in metal additive manufacturing for cost-efficient product qualification, presenting an approach for monitoring the LPBF process and predicting surface roughness using a convolutional neural network. Experimental results show the network's ability to accurately predict surface roughness, laying the foundation for future research on linking hyperspectral process images to relevant part properties for quality assurance purposes.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2021)

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Article Chemistry, Multidisciplinary

Geometry and Distortion Prediction of Multiple Layers for Wire Arc Additive Manufacturing with Artificial Neural Networks

Christian Wacker et al.

Summary: This study utilized an artificial neural network (ANN) to predict welding distortion and geometric accuracy for multilayer WAAM structures, achieving good results. By using deformation and geometry from the previously deposited layer, an enhanced ANN was applied to improve accuracy.

APPLIED SCIENCES-BASEL (2021)

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Development of Defect Detection AI Model for Wire plus Arc Additive Manufacturing Using High Dynamic Range Images

Chaekyo Lee et al.

Summary: This study proposes a method for detecting defects in the arc welding process in WAAM using images acquired by a high dynamic range camera, and through preprocessing and the application of an artificial intelligence model, normal and abnormal statuses can be classified. Experimental results show that the detection accuracy in the metal transfer region-of-interest reached 99%.

APPLIED SCIENCES-BASEL (2021)

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Article Engineering, Manufacturing

Active disturbance rejection control of layer width in wire arc additive manufacturing based on deep learning

Yiming Wang et al.

Summary: In this study, a feedback control strategy for the width of deposited layer in wire arc additive manufacturing (WAAM) process was developed based on melt pool vision. The vision sensing system was used to collect melt pool images and determine weld width using the EPNet segmentation network. An active disturbance rejection control (ADRC) algorithm was designed to achieve real-time control of melt pool width during welding, ensuring satisfactory control accuracy.

JOURNAL OF MANUFACTURING PROCESSES (2021)

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Review Engineering, Manufacturing

Perspectives of using machine learning in laser powder bed fusion for metal additive manufacturing

S. L. Sing et al.

Summary: The adoption of laser powder bed fusion (L-PBF) for metals by the industry has been limited due to inconsistency in parts. Machine learning (ML) has the potential to overcome this obstacle by utilizing datasets obtained at various stages of the process chain, leading to better quality control.

VIRTUAL AND PHYSICAL PROTOTYPING (2021)

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Robotic Process Automation and Artificial Intelligence in Industry 4.0-A Literature review

Jorge Ribeiro et al.

Summary: With the technological evolution and proliferation of information systems in society, digital services have become the majority of services provided by companies and institutions. Robotic Process Automation (RPA) offers numerous advantages in automating organizational and business processes. The complementary use of Artificial Intelligence (AI) algorithms and techniques can improve the accuracy and effectiveness of RPA processes.

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Effects of defects on mechanical properties in metal additive manufacturing: A review focusing on X-ray tomography insights

Anton du Plessis et al.

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Ohyung Kwon et al.

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Qi Yang et al.

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Yuqian Lu et al.

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Virtual Reality Robot-Assisted Welding Based on Human Intention Recognition

Qiyue Wang et al.

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2020)

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YuanBin Wang et al.

SCIENCE CHINA-TECHNOLOGICAL SCIENCES (2020)

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Wire and arc additive manufacturing of metal components: a review of recent research developments

Jienan Liu et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2020)

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A review on wire arc additive manufacturing: Monitoring, control and a framework of automated system

Chunyang Xia et al.

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Biodegradable Polymers for Biomedical Additive Manufacturing

Dario Puppi et al.

APPLIED MATERIALS TODAY (2020)

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Thermal sensing and heat input control for thin-walled structure building based on numerical simulation for wire and arc additive manufacturing

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ADDITIVE MANUFACTURING (2020)

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Invited review: Machine learning for materials developments in metals additive manufacturing

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Continuous Eulerian tool path strategies for wire-arc additive manufacturing of rib-web structures with machine-learning-based adaptive void filling

Lam Nguyen et al.

ADDITIVE MANUFACTURING (2020)

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Digital Twins for Additive Manufacturing: A State-of-the-Art Review

Li Zhang et al.

APPLIED SCIENCES-BASEL (2020)

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Weld Reinforcement Analysis Based on Long-Term Prediction of Molten Pool Image in Additive Manufacturing

Yiming Wang et al.

IEEE ACCESS (2020)

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Evaluation of genetic programming-based models for simulating bead dimensions in wire and arc additive manufacturing

Biranchi Panda et al.

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Online Monitoring and Model-Free Adaptive Control of Weld Penetration in VPPAW Based on Extreme Learning Machine

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Unit block-based process planning strategy of WAAM for complex shell-shaped component

Yun Zhao et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2019)

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Design and Parameter Identification of Wire and Arc Additively Manufactured (WAAM) Steel Bars for Use in Construction

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METALS (2019)

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Xinbo Qi et al.

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Automated defect classification of Aluminium 5083 TIG welding using HDR camera and neural networks

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Ultrasonic phased array inspection of a Wire plus Arc Additive Manufactured (WAAM) sample with intentionally embedded defects

Yashar Javadi et al.

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In Situ Quality Monitoring in AM Using Acoustic Emission: A Reinforcement Learning Approach

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Modeling and real-time prediction for complex welding process based on weld pool

Hang Dong et al.

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Adaptive process control of wire and arc additive manufacturing for fabricating complex-shaped components

Fang Li et al.

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Enhanced beads overlapping model for wire and arc additive manufacturing of multi-layer multi-bead metallic parts

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A review of the wire arc additive manufacturing of metals: properties, defects and quality improvement

Bintao Wu et al.

JOURNAL OF MANUFACTURING PROCESSES (2018)

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Proceedings Paper Engineering, Industrial

Study on Arc Welding processes for High Deposition Rate Additive Manufacturing

Ivan Tabernero et al.

19TH CIRP CONFERENCE ON ELECTRO PHYSICAL AND CHEMICAL MACHINING (2018)

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PROCEEDINGS OF 21ST INTERNATIONAL ESAFORM CONFERENCE ON MATERIAL FORMING (ESAFORM 2018) (2018)

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Andi Wang et al.

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2017)

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Alternative production strategies based on the comparison of additive and traditional manufacturing technologies

Charisios Achillas et al.

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2017)

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Fabrication of inclined thin-walled parts in multi-layer single-pass GMAW-based additive manufacturing with flat position deposition

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JOURNAL OF MATERIALS PROCESSING TECHNOLOGY (2017)

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Review Engineering, Multidisciplinary

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Modeling, control algorithm, and cloud technology application of an automated SAW platform for large-size and thick-wall workpieces

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Linking process, structure, property, and performance for metal-based additive manufacturing: computational approaches with experimental support

Jacob Smith et al.

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Donghong Ding et al.

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