Interlayer closed-loop control of forming geometries for wire and arc additive manufacturing based on fuzzy-logic inference

Li, Yongzhe; Li, Xinlei; Zhang, Guangjun; Horváth, Imre; Han, Qing

doi:10.1016/j.jmapro.2020.04.009

Cited by 48 publications

(12 citation statements)

References 45 publications

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“…The maximum deviation of the smoothed curves from the reference line among the four faces was then taken as the local imperfection amplitude of each specimen emax, as reported in Table 4. The maximum measured imperfection values typically lie between about 0.5 mm and 2.0 mm; these are higher than the imperfection values typically observed in conventionally formed sections (Meng and Gardner, 2020;Schafer and Pekoz, 1998) and similar to the dimensional accuracy of around ± 1.0 mm to ± 2.0 mm generally quoted for WAAM elements (Kumar et al, 2020;Li et al, 2020;Laghi et al, 2019). There was no clear link in the limited dataset between the imperfection amplitude and either the thickness or width of the examined specimens.…”

Section: Local Geometric Imperfectionsmentioning

confidence: 61%

Structural Testing and Design of Wire Arc Additively Manufactured Square Hollow Sections

et al. 2021

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Wire arc additive manufacturing (WAAM) is a method of metal 3D printing that has the potential for significant impact on the construction industry due to its ability to produce large parts, with reasonable printing times and costs. There is currently however a lack of fundamental data on the performance of structural elements produced using this method of manufacture. Seeking to bridge this gap, the compressive behavior and resistance of WAAM square hollow sections (SHS) are investigated in this study. Testing reported in a previous study by the authors of sheet material produced in the same manner as the studied SHS is first summarized. The production, measurement and testing of a series of stainless steel SHS stub columns are then described. Regular cross-section profiles were chosen to isolate the influence of 3D printing and enable direct comparisons to be made against equivalent sections produced using traditional methods of manufacture. A range of cross-section sizes and thicknesses were considered to achieve variation in the local cross-sectional slenderness of the tested specimens, allowing the influence of local buckling to be assessed. Repeat tests enabled the variability in response between specimens to be evaluated; a total of 14 SHS stub columns of seven different local slendernesses was tested, covering all cross-section classes of AISC 370 and Eurocode 3. Advanced non-contact measurement techniques were employed to determine the as-built geometric properties, while digital image correlation measurements were used to provide detailed insight into the deformation characteristics of the test specimens. Owing to the higher geometric variability of WAAM relative to 2 conventional forming processes, the tested 3D printed stub columns were found to exhibit more variable capacities between repeat specimens than is generally displayed by stainless steel SHS. Comparisons of the stub column test results with existing structural design rules highlight the need to allow for the weakening effect of the geometric undulations that are inherent to the WAAM process, in order to achieve safe-sided strength predictions.

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Section: Local Geometric Imperfectionsmentioning

confidence: 61%

Structural Testing and Design of Wire Arc Additively Manufactured Square Hollow Sections

et al. 2021

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“…However, several fundamental and practical challenges need to be addressed to fully adopt the CSAM technology in commercial applications. One of these is geometric control, which is a common problem for other high production rate additive manufacturing (HPRAM) processes, including Wire and Arc Additive Manufacturing (WAAM) [15,16] and Laser Cladding (LC) [17,18]. Poor geometric control places many limitations on applying HPRAM technologies; examples include varying geometric quality, difficulty producing complex geometries, and geometry-induced property variations [7,8,19].…”

Section: Introductionmentioning

confidence: 99%

Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing

Ikeuchi

Vargas-Uscategui

et al. 2021

Applied Sciences

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Cold spray is emerging as an additive manufacturing technique, particularly advantageous when high production rate and large build sizes are in demand. To further accelerate technology’s industrial maturity, the problem of geometric control must be improved, and a neural network model has emerged to predict additively manufactured geometry. However, limited data on the effect of deposition conditions on geometry growth is often problematic. Therefore, this study presents data-efficient neural network modelling of a single-track profile in cold spray additive manufacturing. Two modelling techniques harnessing prior knowledge or existing model were proposed, and both were found to be effective in achieving the data-efficient development of a neural network model. We also showed that the proposed data-efficient neural network model provided better predictive performance than the previously proposed Gaussian function model and purely data-driven neural network. The results indicate that a neural network model can outperform a widely used mathematical model with data-efficient modelling techniques and be better suited to improving geometric control in cold spray additive manufacturing.

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“…Karuthapani et al [19] studied the relationship between GMAW parameters and macrostructural characteristics and developed a fuzzy logic model to predict the effect of the flat electrode. Li et al [20] proposed a novel controlling mechanism for improving the fabrication accuracy rate with fuzzy logic inference. Xie et al [21] developed a comprehensive evaluation of welding quality using the fuzzy logic controller.…”

Section: Related Work In Fuzzy Logic Systemsmentioning

confidence: 99%

Prediction of weldment mechanical properties in GMAW with robot-assisted using fuzzy logic systems

Devendran¹,

Varthanan²

2021

Mater. Res. Express

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Welding operation decides the quality of product standards in all metal work products like automobiles, aerospace vehicles, and many more. The quality of the welding process is more reliable by automating the process with robots. In this research work, the GMAW operation is automated with the “Fanuc Robot Arc mate 100iC/12” robot. The material characteristics such as ultimate tensile strength, hardness, and impact strength of weldments are predicted using a fuzzy system using triangular membership function (TrMF) and trapezoidal membership function (TMF). The simulated results are validated by comparing with experimental work, the experiments are designed using orthogonal array L18, and material characteristics are studied using fractography test. The fuzzy system is trained with experimental results using the IF-Then rule base with the help of the L18 orthogonal array. The inference system has predicted the accuracy rate of weldment mechanical properties, showing a lower error rate.

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Interlayer closed-loop control of forming geometries for wire and arc additive manufacturing based on fuzzy-logic inference

Cited by 48 publications

References 45 publications

Structural Testing and Design of Wire Arc Additively Manufactured Square Hollow Sections

Structural Testing and Design of Wire Arc Additively Manufactured Square Hollow Sections

Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing

Prediction of weldment mechanical properties in GMAW with robot-assisted using fuzzy logic systems

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