Eddy Current Testing of Artificial Defects in 316L Stainless Steel Samples Made by Additive Manufacturing Technology

Geľatko, Matúš; Hatala, Michal; Botko, František; Vandžura, Radoslav; Hajnyš, Jiří

doi:10.3390/ma15196783

Cited by 11 publications

(5 citation statements)

References 40 publications

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“…If we wanted to achieve a much worse classification scale, we would have to focus on the chemical composition (properties) of the degreaser, the temperature of the solution, the time of degreasing. [14], [17].…”

Section: Discussionmentioning

confidence: 99%

Analysis of the Adhesion of the Surface Layer Formed due to Cataphoresis Coating

Fejko,

Dobránsky,

Balint Bali

2023

TEM Journal

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The paper focuses on the cross-cut test, which serves as a basis for determining the adhesion test on the surface of aluminium parts. The coating adhesion test was performed according to the ISO 2409 standard where. Before carrying out the actual measurement, the tools necessary for making those individual measurements were prepared. A knife, an adhesive tape and a grid were used. The result was evaluated on the basis of the ISO 2409 standard. Spacing of a grid was 2mm apart. Grid exam cut test was carried out on 32 samples. The classification of the samples ranged from 0 to 1. At 1, we found that the surface of the material was slightly damaged. At the scale of 0, a smooth cut was made on the material surface, and no damage occurred. Through research, we found that with a constant chemical degreasing factor, the adhesion test varies according to the EN ISO 2409 standard at values 0 and 1. It follows that if we wanted to achieve worse cutting conditions in the grid, we would have to change the temperature of the solution, the time depositions.

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Section: Discussionmentioning

confidence: 99%

Analysis of the Adhesion of the Surface Layer Formed due to Cataphoresis Coating

Fejko,

Dobránsky,

Balint Bali

2023

TEM Journal

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“…It was found that the method can detect surface and shallow defects in a conductive material. For a similar process, Gelatko et al [16] used eddy current sensors on the artificially generated defects in samples made up of 316L stainless steel. The study found that the testing method not only detected the defects but also helped in characterising the shape and size of defects.…”

Section: Imaging Defectsmentioning

confidence: 99%

A Deep Learning Approach to Classify and Detect Defects in the Components Manufactured by Laser Directed Energy Deposition Process

Patil,

Nigam,

Mohapatra

et al. 2023

Machines

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This paper presents a deep learning approach to identify and classify various defects in the laser-directed energy manufactured components. It mainly focuses on the Convolutional Neural Network (CNN) architectures, such as VGG16, AlexNet, GoogLeNet and ResNet to perform the automated classification of defects. The main objectives of this research are to manufacture components using the laser-directed energy deposition process, prepare a dataset of horizontal wall structure, vertical wall structure and cuboid structure with three defective classes such as voids, flash formation, and rough textures, and one non-defective class, use this dataset with a deep learning algorithm to classify the defect and use the efficient algorithm to detect defects. The next objective is to compare the performance parameters of VGG16, AlexNet, GoogLeNet and ResNet used for classifying defects. It has been observed that the best results were obtained when the VGG16 architecture was applied to an augmented dataset. With augmentation, the VGG16 architecture gave a test accuracy of 94.7% and a precision of 80.0%. The recall value is 89.3% and an F1-Score is 89.5%. The VGG16 architecture with augmentation is highly reliable for automating the defect detection process and classifying defects in the laser additive manufactured components.

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“…The materials that form the matrices of the composites belong to the group of engineering (structural) or highly durable (high-tech) polymer composites. From the group of structural polymers, the PBT material (polybutylene terephthalate) and PPA material (polyphthalamide) were selected [ 13 , 14 ].…”

Section: Experiments Preparationmentioning

confidence: 99%

The Influence of the Use of Technological Waste and the Simulation of Material Lifetime on the Unnotched Impact Strength of Two Different Polymer Composites

2022

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The article deals with the assessment of the impact of technological polymer waste resulting from plastic injection technology and the subsequent simulation of the lifetime of polymer products on the impact strength of two different polymer composites. Two different types of polymer composites used to produce plastic parts in the automotive industry were chosen for the research. Based on the evaluation of the PBT composite before exposing the specimens to elevated temperature, it can be concluded that the concentration of the recycled material in the virgin material only affects the values of the unnotched impact strength of the PBT composite when the recycled material accounts for at least 50 wt.%. The results of the unnotched impact strength of the test specimens exposed at 150 °C/500 h make it evident that the addition of recycled material to the virgin material significantly reduces the components’ service lives. The same trend could be observed with the second tested composite material, PPA.

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Eddy Current Testing of Artificial Defects in 316L Stainless Steel Samples Made by Additive Manufacturing Technology

Cited by 11 publications

References 40 publications

Analysis of the Adhesion of the Surface Layer Formed due to Cataphoresis Coating

Analysis of the Adhesion of the Surface Layer Formed due to Cataphoresis Coating

A Deep Learning Approach to Classify and Detect Defects in the Components Manufactured by Laser Directed Energy Deposition Process

The Influence of the Use of Technological Waste and the Simulation of Material Lifetime on the Unnotched Impact Strength of Two Different Polymer Composites

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