Review of conventional and advanced non-destructive testing techniques for detection and characterization of small-scale defects

Silva, Maria Inês; Malitckii, Evgenii; Santos, Telmo G.; Vilaça, Pedro

doi:10.1016/j.pmatsci.2023.101155

Cited by 37 publications

(2 citation statements)

References 463 publications

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“…The resulting pores with varying sizes from nanoscale or macroscale [80] have a significant influence on the mechanical and thermal properties of additively manufactured components [81][82][83]. Such a defect may originate from in-service operations [84,85], manufacturing [65], or material processing [86], exerting substantial influence on mechanical performance by acting as regions of stress concentration and crack initiation. Therefore, this case study is designed to analyze the inhomogeneous distribution of stress around a pore which has been commonly observed in structural components [87][88][89], especially in the case of additively manufactured parts [71,[90][91][92].…”

Section: Case Study: Stress Field Prediction Around An Additive Manuf...mentioning

confidence: 99%

Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks

Rezasefat,

Hogan

2024

Mach. Learn.: Sci. Technol.

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This study investigates the application of machine learning models to predict time-evolving stress fields in complex three-dimensional structures trained with full-scale finite element simulation data. Two novel architectures, the Multi-Decoder CNN (MUDE-CNN) and the Multiple Encoder-Decoder Model with Transfer Learning (MTED-TL), were introduced to address the challenge of predicting the progressive and spatial evolutional of stress distributions around defects. The MUDE-CNN leveraged a shared encoder for simultaneous feature extraction and employed multiple decoders for distinct time frame predictions, while MTED-TL progressively transferred knowledge from one encoder-decoder block to another, thereby enhancing prediction accuracy through transfer learning. These models were evaluated to assess their accuracy, with a particular focus on predicting temporal stress fields around an additive manufacturing-induced isolated pore, as understanding such defects is crucial for assessing mechanical properties and structural integrity in materials and components fabricated via additive manufacturing. The temporal model evaluation demonstrated MTED-TL's consistent superiority over MUDE-CNN, owing to transfer learning's advantageous initialization of weights and smooth loss curves. Furthermore, an autoregressive training framework was introduced to improve temporal predictions, consistently outperforming both MUDE-CNN and MTED-TL. By accurately predicting temporal stress fields around AM-induced defects, these models can enable real-time monitoring and proactive defect mitigation during the fabrication process. This capability ensures enhanced component quality and enhances the overall reliability of additively manufactured parts.

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Section: Case Study: Stress Field Prediction Around An Additive Manuf...mentioning

confidence: 99%

Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks

Rezasefat,

Hogan

2024

Mach. Learn.: Sci. Technol.

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“…Layered defects are particularly common on building facades, usually occurring at the interface between the finishing layer (such as tiles or decorative paint) and the underlying substrate (such as concrete or brick) (Chew, 1992;Chew and De Silva, 2004;Chew et al, 2005). Since these defects are located below the surface, nondestructive testing (NDT) techniques are required for their detection (Zhao, 2021;Gupta et al, 2022;Shaloo et al, 2022;Silva et al, 2023). Although various NDT techniques, including tapping tests, hammer sound tests, and ground-penetrating radar, have been widely used in the study of facade layering (Deterioration, 2009;Edis et al, 2015;Watase et al, 2015), these methods often require direct contact with the structure, are time-consuming, inefficient in detection, and difficult to combine with automated equipment (such as drones) to alleviate the issues associated with manual testing.…”

Section: Introductionmentioning

confidence: 99%

Semi-real-time infrared thermography for detecting layering defects in plasters solidification within indoor environments

Wan,

Zhao,

Zhang

et al. 2024

Front. Mater.

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In the solidification of plasters, promptly identifying layering defects is vital to reduce later inspection and maintenance expenses. Traditional tapping methods for defect detection, while widespread, are inefficient and can damage walls. This study proposes an innovative method utilizing Infrared Thermal Imaging (IRT) for semi real-time detection of layering defects during the solidification phase. The method was applied within the first 48 h following the application of two different plasters (Cement and Gypsum, mixed on-site as needed, not pre-dosed, and applied in a single layer), systematically examining the effects of plaster composition and environmental temperature conditions. The results showed that all preset defects were successfully identified. It was observed that larger defects are more readily detectable at a given thickness, and conversely, thicker defects are more discernible at a fixed size, with the dimension of the defect having a more pronounced impact on absolute contrast than its thickness. Notably, cement plaster exhibited two distinct temporal windows for defect detection, primarily influenced by environmental temperatures. In contrast, gypsum mortar presented two detection phases, with the initial phase being predominantly governed by the heat of hydration and the latter by ambient temperature conditions. The application of IRT technology in this research demonstrates its efficacy in accurately detecting layering defects during the solidification of plasters. This method offers valuable insights and guidance for the application of plaster layers in real-world engineering scenarios, potentially reducing maintenance costs and improving construction quality.

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A comprehensive study of ultrasonic detection and damage quantification of barely visible impact damages in a carbon fiber reinforced polymer composite material

Ravindranath,

Pulipati,

Emerson

et al. 2024

Polymer Composites

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This work investigates detection and evaluation of barely visible impact damage (BVID) on a carbon fiber reinforced polymer composite using non‐destructive evaluation. Specifically, this paper presents a novel method to analyze full waveform from ultrasonic data captured from a new field portable inspection station. Forty‐eight samples are impacted between 8 and 16 J representing a spectrum of surface damage below the visual threshold to barely visible surface damage. The samples are conditioned at cold and dry (5°C and 10%RH) to hot and wet (50°C and 90%RH) environmental conditions prior to inspection. Data collection for ultrasonic inspection is performed on a novel portable inspection system outside of an immersion tank, and the captured full waveform data is analyzed. The waveforms are analyzed using a new automated analysis approach presented in the current manuscript, and the effective diameter of the subsurface damage as a function of depth is extracted from the data set. The extracted three‐dimensional damage zone from the analysis of the ultrasound data shows damage increasing from the surface that is at or below the visual inspection threshold to a circular like damage zone with an effective diameter subsurface that is more than 20 mm. These damage zones are compared to the analyzed μCT data sets, and the average error is nominally 1 mm in characterizing the effective diameter at any depth. There is little correlation between the error and the impact energy or the environmental conditions studied, with the error ranging from 4% to 6% over the samples investigated.Highlights Out of immersion tank, field‐portable ultrasound inspection station, to detect BVID. Automated procedure to characterize three dimensionally the damage from a barely visible impact damage event. Sensitivity to environmental conditioning is investigated for its impact on inspections. Results validated with x‐ray CT with a typical accuracy of 1.32 mm.

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Review of conventional and advanced non-destructive testing techniques for detection and characterization of small-scale defects

Cited by 37 publications

References 463 publications

Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks

Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks

Semi-real-time infrared thermography for detecting layering defects in plasters solidification within indoor environments

A comprehensive study of ultrasonic detection and damage quantification of barely visible impact damages in a carbon fiber reinforced polymer composite material

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