Review on additive manufacturing and non-destructive testing

Ramírez, Isaac Segovia; Márquez, Fausto Pedro Garcı́a; Papaelias, Mayorkinos

doi:10.1016/j.jmsy.2022.12.005

Cited by 63 publications

(8 citation statements)

References 88 publications

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“…ISO 9712 defines the non-destructive testing (NDT) method as a discipline that employs a physical principle [ 12 ]. Current NDT detection methods could be used for additive manufacturing processes including acoustic emission testing, eddy current testing, magnetic testing, and laser-ultrasonic testing, etc., which can precisely identify the location and the size of the defects [ 13 , 14 , 15 ]. However, these techniques normally rely on specific and costly equipment that is quite difficult to install within AM apparatus to realize in-situ inspection.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Defect Detection Strategy for Additive Manufacturing Processes Based on Deep Learning and Machine Vision Technologies

Wang,

Zhang

et al. 2023

Micromachines

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Nowadays, additive manufacturing (AM) is advanced to deliver high-value end-use products rather than individual components. This evolution necessitates integrating multiple manufacturing processes to implement multi-material processing, much more complex structures, and the realization of end-user functionality. One significant product category that benefits from such advanced AM technologies is 3D microelectronics. However, the complexity of the entire manufacturing procedure and the various microstructures of 3D microelectronic products significantly intensified the risk of product failure due to fabrication defects. To respond to this challenge, this work presents a defect detection technology based on deep learning and machine vision for real-time monitoring of the AM fabrication process. We have proposed an enhanced YOLOv8 algorithm to train a defect detection model capable of identifying and evaluating defect images. To assess the feasibility of our approach, we took the extrusion 3D printing process as an application object and tailored a dataset comprising a total of 3550 images across four typical defect categories. Test results demonstrated that the improved YOLOv8 model achieved an impressive mean average precision (mAP50) of 91.7% at a frame rate of 71.9 frames per second.

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Section: Introductionmentioning

confidence: 99%

A Real-Time Defect Detection Strategy for Additive Manufacturing Processes Based on Deep Learning and Machine Vision Technologies

Wang,

Zhang

et al. 2023

Micromachines

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“…This approach offers both speed and costeffectiveness. 39,40 In the literature, there is significant research on the utilization of polymeric 3D printing in manufacturing and characterizing composite materials, particularly focusing on sandwich composites and 3D printed bonded joints. [41][42][43] Additionally, investigations have been conducted on the fracture behavior of 3D printed specimens under Mode I and Mode II loadings.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Mode-I and Mode-II fracture toughness of carbon fiber/epoxy laminated composites using 3D-printed polyamide interlayers

Beylergil,

Duman

2023

Proceedings of the Institution of Mechanical Engineers, Part L:

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Delamination is a critical concern in laminated composites, affecting their structural integrity and overall performance. This study investigates the enhancement of Mode-I and Mode-II fracture toughness in carbon fiber/epoxy (CF/EP) composites through the incorporation of 3D-printed polyamide (PA) interlayers. Vacuum-assisted resin transfer molding was utilized to fabricate composite laminates with and without 3D-printed PA interlayers. Comprehensive testing was conducted to assess the effect of 3D-printed PA interlayers on the Mode-I and Mode-II fracture toughness, interlaminar shear strength, and flexural properties, as well as thermomechanical response using dynamic mechanical analysis. The results revealed a significant improvement in critical energy release rates for both Mode-I and Mode-II (GIc and GIIc), increasing by 43.5% and 81.2% respectively, compared to the reference composites. This enhancement was primarily attributed to crack bridging and plastic deformation of PA filaments in the interlaminar region. Additionally, interlaminar shear strength increased by 17.4%. While the reference composites had a glass transition temperature of 117.3 °C, the PA-reinforced composites showed a slightly higher value at 119.6 °C, with no significant change in the glass transition temperature. tanδmax values increased from 0.321 to 0.576, suggesting better energy dissipation in PA-reinforced composites. However, flexural properties were adversely affected by the increased thickness and reduced fiber volume fraction due to the introduction of 3D-printed PA interlayers, with the flexural modulus decreasing by approximately 28% and the flexural strength by around 50%. These findings offer promising opportunities to enhance the performance of CF/EP composites under specific loading scenarios, thus expanding their potential applications across diverse industries.

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“…Many nondestructive testing (NDT) techniques-such as eddy current testing, penetrant testing, X-ray computed tomography (CT), infrared thermographic testing, acoustic emission testing, and ultrasound testing (UT)-are feasible and practical for assessing metallic workpieces manufactured through SLM [11]. The advantages of each NDT modality are listed in Fig.…”

Section: Introductionmentioning

confidence: 99%

Quality Assessment of Metal Additive Manufactured Parts by a Multiscale Convolutional Fuzzy Neural Network Using Ultrasound Images as Input Data

Lin,

Wang

2023

IEEE Access

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The successful production of metallic workpieces through selective laser melting requires a quality assurance process that can effectively and nondestructively assess internal defects. Ultrasound testing is a nondestructive testing modality that can be used to identify defects and characterize the microstructure and properties of a material. Moreover, developments in computer vision techniques have led to the increased use of neural networks (NNs) in quality assurance processes. Therefore, a novel multiscale convolutional fuzzy NN (MCFNN) that uses ultrasound images as input data was developed in this study to automatically evaluate Inconel 718 workpieces fabricated through selective laser melting under various combinations of process parameters. The average accuracy, precision, recall, F1 score, and number of required parameters for the developed MCFNN were comparable to those of state-of-the-art models. Moreover, several concatenation methods, multiscale fusion strategies, and fuzzy inference systems were implemented in the developed MCFNN for performance comparison. Subsequently, the workpieces were examined through microcomputed tomography to verify the results obtained using ultrasound images. The experimental results indicated that among the compared models, the MCFNN achieved the highest average accuracy (91.44% ± 4.73%), precision (92.74% ± 3.79%), recall (91.44% ± 4.73%), and F1 score (91.35% ± 4.82%) and required the fewest parameters (107,138). The experimental results demonstrate that the developed MCFNN has high potential for implementation in the embedded devices of portable ultrasound scanning systems.

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Review on additive manufacturing and non-destructive testing

Cited by 63 publications

References 88 publications

A Real-Time Defect Detection Strategy for Additive Manufacturing Processes Based on Deep Learning and Machine Vision Technologies

A Real-Time Defect Detection Strategy for Additive Manufacturing Processes Based on Deep Learning and Machine Vision Technologies

Enhancing Mode-I and Mode-II fracture toughness of carbon fiber/epoxy laminated composites using 3D-printed polyamide interlayers

Quality Assessment of Metal Additive Manufactured Parts by a Multiscale Convolutional Fuzzy Neural Network Using Ultrasound Images as Input Data

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