Robotic skeleton arc additive manufacturing of aluminium alloy

Wu, Bintao; Zhou, Yu; Duin, Stephen van; Li, Huijun; Pierson, E.

doi:10.1007/s00170-021-07077-4

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…Li et al [ 12 ] used the WAAM-GMAW method to manufacture aluminum alloy multi-angle inclined rods and large multi-layer lattice structures through multi-axis welding robots. Wu et al [ 13 ] realized the fabrication of an aluminum alloy pyramid lattice based on WAAM-GWAM. In this study, a workbench with multiple degrees of freedom and a welding torch fixed in the Z direction were used.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Estimation of Force Balance of Near-Suspended Melt Pool for Drooping and Collapsing Prediction

Luo,

Qian,

et al. 2024

Sensors

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Wire-arc additive manufacturing (WAAM) is favored by the industry for its high material utilization rate and low cost. However, wire-arc additive manufacturing of lattice structures faces problems with forming accuracy such as broken rod and surface morphology defects, which cannot meet the industrial demand. This article innovatively combines the melt pool stress theory with visual perception algorithms to visually study the force balance of the near-suspended melt pool to predict the state of the melt pool. First, the method for melt pool segmentation was studied. The results show that the optimized U-net achieved high accuracy in melt pool segmentation tasks, with accuracies of 98.18%, MIOU 96.64%, and Recall 98.34%. In addition, a method for estimating melt pool force balance and predicting normal, sagging, and collapsing states of the melt pool is proposed. By combining experimental testing with computer vision technology, an analysis of the force balance of the melt pool during the inclined rod forming process was conducted, showing a prediction rate as high as 90% for the testing set. By using this method, monitoring and predicting the state of the melt pool is achieved, preemptively avoiding issues of broken rods during the printing process. This approach can effectively assist in adjusting process parameters and improving welding quality. The application of this method will further promote the development of intelligent unmanned WAAM and provide some references for the development of artificial intelligence monitoring systems in the manufacturing field.

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Section: Introductionmentioning

confidence: 99%

Vision-Based Estimation of Force Balance of Near-Suspended Melt Pool for Drooping and Collapsing Prediction

Luo,

Qian,

et al. 2024

Sensors

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“…The interlayer temperature is another critical parameter that significantly influences the lattice structure morphology. Wu et al 2020 [ 23 ] and Yu et al 2022 [ 21 ] investigated the impact of the interlayer temperature on the structs morphology in the construction of aluminium alloy lattice structures. Most of the studies on the fabrication of lattice structures using WAAM have been focused on the analysis of the morphology of the struts, neglecting the analysis of their mechanical and microstructural properties, as well as its correlation with the heat input or thermal cycles.…”

Section: Introductionmentioning

confidence: 99%

Thermal, Microstructural, and Mechanical Analysis of Complex Lattice Structures Produced by Direct Energy Deposition

Andrade,

Zhu,

Miranda

et al. 2024

Materials

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Lattice structures have gained attention in engineering due to their lightweight properties. However, the complex geometry of lattice structures and the high melting temperature of metals present significant manufacturing challenges for the large-scale fabrication of these structures. Direct Energy Deposition (DED) methods, such as the Wire Arc Additive Manufacturing (WAAM) technique, appear to be an interesting solution for overcoming these limitations. This study provides a detailed analysis of the manufacturing process of carbon steel lattice structures with auxetic geometry. The study includes thermal analysis using infrared thermography, microstructural characterization through metallography, and mechanical evaluation via hardness and mechanical testing. The findings reveal the significant impact of heat input, thermal cycles, and deposition sequence on the morphology and mechanical properties of the lattice structures. Fast thermal cycles are related to areas with higher hardness values, smaller strut diameters, and porous formations, which shows that controlling heat input and heat dissipation is crucial for optimizing the properties of lattice structures produced using WAAM.

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“…In the automotive industry, the pursuit of safe and lightweight materials is unremitting [1][2][3]. The 7-series aluminum alloy has broad development prospects in the field of electric vehicles because of its light weight, high strength, good corrosion resistance, good formability, and excellent impact resistance.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Hot Formability of AA7075 Aluminum Alloy Sheet

Wang

Sui

Guan

2023

Metals

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A uniaxial high-temperature tensile test of an AA7075 aluminum alloy sheet was conducted using an established induction heating test system. Five different types of specimens were designed to obtain the stress–strain curves at different forming temperatures and strain rates so as to construct and modify the Johnson–Cook constitutive model. The uniaxial tensile test of different stress states was numerically simulated using ABAQUS finite element software, and the stress triaxiality and equivalent fracture strain parameters were extracted. The Johnson–Cook ductile fracture model was constructed, and an error evaluation scheme was designed to determine the best failure parameter combination. Based on the obtained Johnson–Cook ductile fracture model, finite element models of different strain paths were constructed, and theoretical forming limit curves at different temperatures and strain rates were obtained. The results of the Nakazima test and finite element simulation are mostly consistent, which confirms the reliability of the constructed fracture model and theoretical forming limit curves.

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Robotic skeleton arc additive manufacturing of aluminium alloy

Cited by 6 publications

References 20 publications

Vision-Based Estimation of Force Balance of Near-Suspended Melt Pool for Drooping and Collapsing Prediction

Vision-Based Estimation of Force Balance of Near-Suspended Melt Pool for Drooping and Collapsing Prediction

Thermal, Microstructural, and Mechanical Analysis of Complex Lattice Structures Produced by Direct Energy Deposition

Prediction of Hot Formability of AA7075 Aluminum Alloy Sheet

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