Monitoring and control the Wire Arc Additive Manufacturing process using artificial intelligence techniques: a review

Mattera, Giulio; Nele, Luigi; Paolella, Davide

doi:10.1007/s10845-023-02085-5

Cited by 31 publications

(5 citation statements)

References 95 publications

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“…Real-time control and optimization enable operators to optimize process parameters in response to changing conditions, ensuring Enhancing Efficiency and Energy Optimization: Data-Driven Solutions in Process… 9 optimal performance under dynamic operating environments. Model-free control techniques like reinforcement learning and evolutionary algorithms learn optimal control policies directly from data, enabling more robust and adaptive control strategies (56).…”

Section: Advancements In Data-driven Optimal Controlmentioning

confidence: 99%

Enhancing Efficiency and Energy Optimization: Data-Driven Solutions in Process Industrial Manufacturing

Liu,

Zhang

2024

EAI Endorsed Trans Energy Web

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This paper reviews the current state of research in data analytics and machine learning techniques, focusing on their applications in process industrial manufacturing, particularly in control and optimization. Key areas for future research include selection and transfer learning for process monitoring, addressing time-varying characteristics, and enhancing data-driven optimal control with domain-specific knowledge. Additionally, the paper explores reinforcement learning techniques and robust optimization, including distributional robust optimization, for high-level decision-making. Emphasizing the importance of historical knowledge of plants and processes, this paper aims to identify knowledge gaps and pave the way for future research in data-driven strategies for process industries, with a particular emphasis on energy efficiency and optimization.

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Section: Advancements In Data-driven Optimal Controlmentioning

confidence: 99%

Enhancing Efficiency and Energy Optimization: Data-Driven Solutions in Process Industrial Manufacturing

Liu,

Zhang

2024

EAI Endorsed Trans Energy Web

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“…Regarding arc-DED, as the technology continues to advance, it becomes imperative to explore the testing methodologies to contribute to its optimization. Among the forefront of the testing methods is the integration of artificial intelligence (AI) for defect detection in arc-DED [29]. Researchers have developed sophisticated AI applications, tailored to identifying flaws during the manufacturing process, thus enhancing quality control and efficiency.…”

Section: Introductionmentioning

confidence: 99%

The Use of Virtual Sensors for Bead Size Measurements in Wire-Arc Directed Energy Deposition

Fernández-Zabalza,

Veiga,

Suárez

et al. 2024

Applied Sciences

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Having garnered significant attention in the scientific community over the past decade, wire-arc directed energy deposition (arc-DED) technology is at the heart of this investigation into additive manufacturing parameters. Singularly focused on Invar as the selected material, the primary objective revolves around devising a virtual sensor for the indirect size measurement of the bead. This innovative methodology involves the seamless integration of internal signals and sensors, enabling the derivation of crucial measurements sans the requirement for direct physical interaction or conventional measurement methodologies. The internal signals recorded, the comprising voltage, the current, the energy from the welding heat source generator, the wire feed speed from the feeding system, the traverse speed from the machine axes, and the temperature from a pyrometer located in the head were all captured through the control of the machine specially dedicated to the arc-DED process during a phase of optimizing and modeling the bead geometry. Finally, a feedforward neural network (FNN), also known as a multi-layer perceptron (MLP), is designed, with the internal signals serving as the input and the height and width of the bead constituting the output. Remarkably cost-effective, this solution circumvents the need for intricate measurements and significantly contributes to the proper layer-by-layer growth process. Furthermore, a neural network model is implemented with a test loss of 0.144 and a test accuracy of 1.0 in order to predict weld bead geometry based on process parameters, thus offering a promising approach for real-time monitoring and defect detection.

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“…Our world is transforming rapidly-from the rise of powerful artificial intelligence systems [1][2][3] to the on-going on-demand additive manufacturing drive. [4][5][6][7][8] These changes are underpinned by fundamental research efforts that question the way in which existing paradigms can be challenged, while pushing the limits of the currently possible. In light-driven 3D printing, [9,10] the engine room for a substantial part of the ongoing innovations is the photochemistry that governs the light-induced curing processes.…”

Section: Introductionmentioning

confidence: 99%

Photochemically Activated 3D Printing Inks: Current Status, Challenges, and Opportunities

Gauci,

Vranic,

Blasco

et al. 2023

Advanced Materials

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Abstract3D printing with light is enabled by the photochemistry underpinning it. Without fine control over our ability to photochemically gate covalent bond formation by the light at a certain wavelength and intensity, advanced photoresists with functions spanning from on‐demand degradability, adaptability, rapid printing speeds and tailored functionality are impossible to design. Herein, we critically assess recent advances in photoresist design for light‐driven 3D printing applications and provide an outlook of the outstanding challenges and opportunities. We achieve this by classing the discussed photoresists in chemistries that function photoinitiator‐free and those that require a photoinitiator to proceed. Such a taxonomy is based on the efficiency with which photons are able to generate covalent bonds, with each concept featuring distinct advantages and drawbacks.This article is protected by copyright. All rights reserved

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Monitoring and control the Wire Arc Additive Manufacturing process using artificial intelligence techniques: a review

Cited by 31 publications

References 95 publications

Enhancing Efficiency and Energy Optimization: Data-Driven Solutions in Process Industrial Manufacturing

Enhancing Efficiency and Energy Optimization: Data-Driven Solutions in Process Industrial Manufacturing

The Use of Virtual Sensors for Bead Size Measurements in Wire-Arc Directed Energy Deposition

Photochemically Activated 3D Printing Inks: Current Status, Challenges, and Opportunities

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