Predicting part distortion field in additive manufacturing: a data-driven framework

Aljarrah, Osama; Li, Jun; Heryudono, Alfa; Huang, Weiping; Bi, Jing

doi:10.1007/s10845-021-01902-z

Cited by 12 publications

(3 citation statements)

References 45 publications

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“…5(b)), i.e., when considering all the axes, with a maximum value of 0.25 mm being seen in the Nickel layer. As discussed in the literature [21][22][23][24], distortions are changes in the shape and dimensions of components subjected to thermal cycling, as a result of material movement (plastic deformation) that occurs due to the thermal stresses developed during the arc deposition manufacturing process. Distortions affect the shape and dimensions of a component or structure both globally and locally (when they are also called deformities).…”

Section: Simulation Of the Electric Arc Process With Controlled Short...mentioning

confidence: 99%

Simulation of Wire Arc Additive Manufacturing of Dissimilar Transition Joint in Steel-Aluminum

Silva,

Coelho,

Sade

2024

Preprint

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Dissimilar welding of non-ferrous metals can present difficulties since it can result in the formation of very fragile intermetallic compounds. The aim of this study was therefore to perform a computer simulation of the interaction between steel and aluminum using alloy UNS N06625 or copper as the transition joint, with a view to the deposition of dissimilar materials using the wire arc additive manufacturing process. The computer simulation was carried out using Simufact Welding® software, observing the characteristics of the part before carrying out the deposition process, determining the probable properties and dilutions of the selected materials, and the distortions and residual stresses of the part produced. Through this study, it was possible to evaluate the characteristics of the deposition of dissimilar materials by applying alloy UNS N06625 or copper as a transition joint between steel and aluminum and to predict the properties of the part produced by different welding processes.

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Section: Simulation Of the Electric Arc Process With Controlled Short...mentioning

confidence: 99%

Simulation of Wire Arc Additive Manufacturing of Dissimilar Transition Joint in Steel-Aluminum

Silva,

Coelho,

Sade

2024

Preprint

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“…Predicting when machines will reach their maximum pace allows automated systems to flexibly adapt manufacturing processes to changes in the system conditions, increasing productivity while decreasing energy use. Comparisons with province forecasting analytics were made using detailed experimental evaluations of actual data from a metal packaging mill, improving the suggested method's efficacy (Aljarrah et al, 2023). Peng et al (2023) introduced laser additive manufacturing optimization to enhance their external qualities.…”

Section: Literature Reviewmentioning

confidence: 99%

Manufacturing Process Optimization in the Process Industry

Liu,

Cheng

2024

International Journal of Information Technology and Web Engineering

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This paper introduces a technology, a data-driven optimization model of manufacturing service in intelligent manufacturing process using deep learning algorithm and resource agent (DDR), and a data-driven resource agent that represents available manufacturing resources. Asset agent is an intelligent module of entity production unit, which has powerful functions of data processing and service management. This paper includes the method of designing expert-based processes, the current process realization model, and the key performance indicators (KPI) used to evaluate the optimization work. The model aims to maximize efficiency, reduce the cost of manufacturing resources, improve the production and maintenance efficiency of network resources, and improve the manufacturing service level. Finally, the efficiency and technical feasibility of the model are evaluated through a typical example of industrial product production process.

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“…In AM, the classification is used for pore detection from process monitoring data [15]. The regression capabilities can be used to predict the distortion of AM parts [16][17][18]. However, published results were limited in the geometrical variety of the specimens.…”

Section: Neural Networkmentioning

confidence: 99%

Towards neuronal network enhanced finite element simulations in additive manufacturing

Behrens,

Ploshikhin

2023

IVth International Conference on Simulation for Additive Manufacturing

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Predicting part distortion field in additive manufacturing: a data-driven framework

Cited by 12 publications

References 45 publications

Simulation of Wire Arc Additive Manufacturing of Dissimilar Transition Joint in Steel-Aluminum

Simulation of Wire Arc Additive Manufacturing of Dissimilar Transition Joint in Steel-Aluminum

Manufacturing Process Optimization in the Process Industry

Towards neuronal network enhanced finite element simulations in additive manufacturing

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