Digital Image Correlation for Measuring Full-Field Residual Stresses in Wire and Arc Additive Manufactured Components

Boruah, Dibakor; Dewagtere, Nele; Ahmad, Belal; Silva, Rafael Gomes Nunes; Tacq, Jeroen; Zhang, Xiang; Hua, Guo; Verlinde, Wim; Waele, Wim De

doi:10.3390/ma16041702

Cited by 6 publications

(4 citation statements)

References 56 publications

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“…Subsequently, the research expanded to improving forecast models for warpage, based on general beam theory [73]. Other techniques encompass neutron diffraction (ND) [22][23][24]74], X-ray diffraction (XRD) [25][26][27]75], contour method [13,[76][77][78] hole drilling methods [32], thermomechanical coupling model [79], digital image correlation (DIC) [80], deep hole drilling (DHD) [81], Operando synchrotron or synchrotron X-ray diffraction (SXRD) [82,83]. The selection of the methods depends on elements such as material, component size, geometry, and the level of accuracy required for the measurement.…”

Section: Residual Stress Measurement Methodsmentioning

confidence: 99%

Review of Measuring Methods of Residual Stress in Wire Arc Additive Manufacturing Products

Gurmesa,

Lemu,

Adugna

et al. 2024

Preprint

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This literature review provides an in-depth exploration of the research conducted on the investigation and measurement of residual stress (RS) in Wire Arc Additive Manufacturing (WAAM) products, particularly focusing on how process parameters influence the phenomenon. RS plays a crucial role in determining the mechanical behavior and structural integrity of WAAM components. This review article gives an understanding of the relationship between process parameters and RS to optimize the WAAM processes and ensure the durability of the final products. It also summarizes key findings, measurement techniques, challenges, and future directions in this evolving field. The review also analyzes a measurement of RS in the products of WAAM as a function of process parameters depending on examining various research studies, techniques, and methodologies employed in their studies. Experimental measuring techniques and numerical analysis of RS to determine the impacts of RS in mechanical response in products of WAAM were discussed. By offering a comprehensive overview of current research trends and insights, this review serves as a valuable resource to guide future investigations, fostering the advancement of WAAM as a robust and efficient manufacturing technology.

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Section: Residual Stress Measurement Methodsmentioning

confidence: 99%

Review of Measuring Methods of Residual Stress in Wire Arc Additive Manufacturing Products

Gurmesa,

Lemu,

Adugna

et al. 2024

Preprint

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“…The DIC method can, in fact, be extensively and efficiently applied to different monitoring methods, especially of components produced by any additive manufacturing technique [39]. In particular, this full-filed optical observation can provide the relaxation displacements of the entire surface of a component after the removal of clampings, which can in turn be interpreted to evaluate the residual stresses, as recently shown by Boruah et al [40].…”

Section: Introductionmentioning

confidence: 96%

Residual Stress Determination with the Hole-Drilling Method on FDM 3D-Printed Precurved Specimen through Digital Image Correlation

Santus,

Neri,

Romoli

et al. 2024

Applied Sciences

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The hole-drilling method (HDM) is a common technique used for the determination of residual stresses, especially for metal alloy components, though also for polymers. This technique is usually implemented with strain gages, though other methods for determining the fields of displacements are quite mature, such as the use of digital image correlation (DIC). In the present paper, this combined methodology is applied to a 3D-printed PLA precurved specimen that is flattened in order to impose a bending distribution which can be considered known with a reasonable accuracy. The back-calculated stress distribution is in agreement with the expected (imposed) bending stress, however, a converging iterative procedure for obtaining the solution is introduced and discussed in the paper.

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“…This is because of the advantages such as an elevated deposition rate, flexible building volume, high material deposition efficiency, and the low cost of equipment investment, as well as raw material [1][2][3]. However, the high heat accumulation and the complex thermal evolution during the deposition strongly affects the microstructures and the mechanical properties of the as-built component [4][5][6], as well as the residual stresses [7]. These effects are generally investigated using experimental methods.…”

Section: Introductionmentioning

confidence: 99%

An ANN Hardness Prediction Tool Based on a Finite Element Implementation of a Thermal–Metallurgical Model for Mild Steel Produced by WAAM

Cheng,

Ling,

De Waele

2024

Metals

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WAAM has emerged as a promising technique for manufacturing medium- and large-scale metal parts due to its high material deposition efficiency and automation level. However, its high heat accumulation and complex thermal evolution strongly affect the resulting microstructures and mechanical properties. The heterogeneous and unpredictable nature of these properties hinder the widespread application of WAAM in the steel construction industry. In this study, an artificial neural network (ANN) hardness model is developed, based on a thermal–metallurgical model for mild steel. The objective is to establish non-linear relationships between the input process parameters and the desired output, i.e., hardness. The thermal–metallurgical model utilizes a well-distributed heat source model, a death-and-birth algorithm, and a metallurgical model to simulate the temperature field and to calculate the microstructure phase fraction. The temperature prediction errors at four thermocouple positions are mostly below 20%. Because of the limited experimental data, twenty-five simulation experiments are performed using the L25 orthogonal array based on the Taguchi method. The analysis of variance (ANOVA) reveals that the travel speed has the greatest impact on hardness. With the dataset from the thermal–metallurgical model, an ANN model to predict hardness is developed. A comparison to experimental data shows excellent performance and accuracy, with the Mean Absolute Percentage Error (MAPE) of ANN predictions within 10% of the targeted hardness.

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Digital Image Correlation for Measuring Full-Field Residual Stresses in Wire and Arc Additive Manufactured Components

Cited by 6 publications

References 56 publications

Review of Measuring Methods of Residual Stress in Wire Arc Additive Manufacturing Products

Review of Measuring Methods of Residual Stress in Wire Arc Additive Manufacturing Products

Residual Stress Determination with the Hole-Drilling Method on FDM 3D-Printed Precurved Specimen through Digital Image Correlation

An ANN Hardness Prediction Tool Based on a Finite Element Implementation of a Thermal–Metallurgical Model for Mild Steel Produced by WAAM

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