Bead modelling and implementation of adaptive MAT path in wire and arc additive manufacturing

Ding, Donghong; Pan, Zengxi; Cuiuri, Dominic; Li, Huijun; Duin, Stephen van; Larkin, Nathan

doi:10.1016/j.rcim.2015.12.004

Cited by 189 publications

(62 citation statements)

References 18 publications

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“…[5] Machine learning has been successfully applied in applications such as image processing, text classification, and speech recognition. [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] An example of the utility of machine learning in the established quality control method of visual inspection is demonstrated by the use of a neural network to identify flaws in laser powder bed fusion 3D printing. [8] Examples of their use in both monitoring/feedback applications and predictive models include predicting property outcomes based on parameter settings, predicting global parameter settings for specific outcomes, identifying failures during printing, predicting bead geometry, adjusting geometry to prevent failures, and assessing part manufacturability.…”

mentioning

confidence: 99%

Machines as Craftsmen: Localized Parameter Setting Optimization for Fused Filament Fabrication 3D Printing

Gardner

Hunt

Ebel

et al. 2019

Adv Materials Technologies

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FFF is known to have issues with part quality and consistency, which has limited its use to prototyping and noncritical applications where functional reliability does not affect safety. [2] The occurrence of issues such as poor surface finish, layer delamination, and poor dimensional stability depends on a number of parameter settings, including nozzle temperature, print speed, environmental conditions, geometry, and location (Figure 1a,b). [3] Experienced operators must set these parameters according to the material, part geometry, and 3D printer. It can be difficult for even an expert to select optimum parameter settings (Figure 1c,d). [4] An operator can specify settings for over 100 different printing options using a slicing software such as Cura. Attempting all combinations is not practical, so an operator must rely on their knowledge of 3D printing to adjust parameter settings. This leads to operator-dependent part-to-part variations and uncertainty in the performance of the final part. Additionally, an operator will typically select a set of global parameter settings that are used throughout the part, or at most make some layer-to-layer adjustments. A more objective solution for selecting parameter settings is needed to ensure optimal use of the printer/material performance space and consistency in 3D-printed parts, regardless of part geometry, material, system, or operator.Machine learning offers a potential approach to addressing this problem. Machine learning models are trained on thousands to millions of data points to recognize patterns that are too difficult to identify using deterministic algorithms. [5] Machine learning has been successfully applied in applications such as image processing, text classification, and speech recognition. [5][6][7] Potential uses for machine learning in 3D printing have also been studied in a limited capacity. [8] Examples of their use in both monitoring/feedback applications and predictive models include predicting property outcomes based on parameter settings, predicting global parameter settings for specific outcomes, identifying failures during printing, predicting bead geometry, adjusting geometry to prevent failures, and assessing part manufacturability. [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] An example of the utility of machine learning in the established quality control method of visual inspection is demonstrated by the use of a neural network to identify flaws in laser powder bed fusion 3D printing. [28,29] An Quality control and repeatability of 3D printing must be enhanced to fully unlock its utility beyond prototyping and noncritical applications. Machine learning is a potential solution to improving 3D printing performance and is explored for areas including flaw identification and property prediction. However, critical problems must be resolved before machine learning can truly enable 3D printing to reach its potential, including the very large data sets required for training and the inherently local nature of 3D print...

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mentioning

confidence: 99%

Machines as Craftsmen: Localized Parameter Setting Optimization for Fused Filament Fabrication 3D Printing

Gardner

Hunt

Ebel

et al. 2019

Adv Materials Technologies

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“…The variation of the bead geometry can be indicated by RWTH since the cross-sectional area is a constant value. It can be seen that the three repeated experiments ( to WFR/TS [23]. It is difficult to compare different bead geometries if their cross-sectional areas are not the same.…”

Section: Experimental Designmentioning

confidence: 98%

“…For lower WFR, the welding arc is not quite stable and incomplete melting occurs, while for higher WFR, pool overflowing occurs due to excessive heat input as well as the large arc force and strong droplet impingement. Note that the ratio of WFR to TS is fixed at 10 herein in order to maintain the same area of the bead cross section (i.e., the metal deposition rate per unit length) which is in direct proportion to WFR/TS [23]. It is difficult to compare different bead geometries if their cross-sectional areas are not the same.…”

Section: Experimental Designmentioning

confidence: 99%

Thermoelectric Cooling-Aided Bead Geometry Regulation in Wire and Arc-Based Additive Manufacturing of Thin-Walled Structures

Chen

Shi

et al. 2018

Applied Sciences

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Featured Application: This research helps improve the capability of wire and arc-based additive manufacturing in fabricating thin-walled structures in terms of geometric accuracy, productivity, and microstructure.Abstract: Wire and arc-based additive manufacturing (WAAM) is a rapidly developing technology which employs a welding arc to melt metal wire for additive manufacturing purposes. During WAAM of thin-walled structures, as the wall height increases, the heat dissipation to the substrate is slowed down gradually and so is the solidification of the molten pool, leading to variation of the bead geometry. Though gradually reducing the heat input via adjusting the process parameters can alleviate this issue, as suggested by previous studies, it relies on experience to a large extent and inevitably sacrifices the deposition rate because the wire feed rate is directly coupled with the heat input. This study introduces for the first time an in-process active cooling system based on thermoelectric cooling technology into WAAM, which aims to eliminate the difference in heat dissipation between upper and lower layers. The case study shows that, with the aid of thermoelectric cooling, the bead width error is reduced by 56.8%, the total fabrication time is reduced by 60.9%, and the average grain size is refined by 25%. The proposed technique provides new insight into bead geometry regulation during WAAM with various benefits in terms of geometric accuracy, productivity, and microstructure.

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“…Post-process machining must be used to remove the extra materials and improve the accuracy at the cost of material and energy wastage. (e) Adaptive MAT path patterns with varying step-over distance; (f) The predicted void-free deposition with high accuracy at the boundary through using adaptive MAT path [28].…”

Section: Adaptive Mat Pathmentioning

confidence: 99%

Advanced Design for Additive Manufacturing: 3D Slicing and 2D Path Planning

Ding¹,

Pan²,

Cuiuri³

et al. 2016

New Trends in 3D Printing

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Commercial D printers have been increasingly implemented in a variety of fields due to their quick production, simplicity of use, and cheap manufacturing. Software installed in these machines allows automatic production of components from computer-aided design CAD models with minimal human intervention. However, there are fewer options provided, with a limited range of materials, limited path patterns, and layer thicknesses. For fabricating metal functional parts, such as laserbased, electron beam-based, and arc-welding-based additive manufacturing AM machines, usually more careful process design requires in order to obtain components with the desired mechanical and material properties. Therefore, advanced design for additive manufacturing, particularly slicing and path planning, is necessary for AM experts. This chapter introduces recent achievements in slicing and path planning for AM process.

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Bead modelling and implementation of adaptive MAT path in wire and arc additive manufacturing

Cited by 189 publications

References 18 publications

Machines as Craftsmen: Localized Parameter Setting Optimization for Fused Filament Fabrication 3D Printing

Machines as Craftsmen: Localized Parameter Setting Optimization for Fused Filament Fabrication 3D Printing

Thermoelectric Cooling-Aided Bead Geometry Regulation in Wire and Arc-Based Additive Manufacturing of Thin-Walled Structures

Advanced Design for Additive Manufacturing: 3D Slicing and 2D Path Planning

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