Material classification with laser thermography and machine learning

Aujeszky, Tamás; Κορρές, Γεώργιος; Eid, Mohamad

doi:10.1080/17686733.2018.1539895

Cited by 12 publications

(6 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Examples of application of deep learning are available in the literature, from passive thermal examinations for additive manufacturing process monitoring 8 or from automatic materials characterization. 9 It is also proposed the use of deep segmentation models coupled with induction thermography technique, experimenting with simulated data to alleviate the lack of experimental data. 10 Indeed, FST experiment requires expertise in thermal and laser fields and relatively expensive devices like infrared camera or power lasers.…”

Section: Review Of the Literaturementioning

confidence: 99%

Database for transfer learning in crack detection and localization on metallic materials using flying spot thermography and deep learning

Helvig,

Trouvé-Peloux,

Gavérina

et al. 2024

J. Electron. Imag.

View full text Add to dashboard Cite

Flying spot" laser infrared thermography (FST) is a non destructive testing technique able to detect defects by scanning surfaces with a laser heat source. Defects, such as cracks on metallic parts, are revealed by the disturbance of heat propagation measured by an infrared camera. Deep learning approaches are now very efficient to automatically analyze and use contextual information from data and can be used for crack detection. However, in the literature only few works deal with the use of deep learning for the crack detection in FST. Indeed obtaining a large amount of data from FST examinations can be expensive and time-consuming. This work presents an open-access database for "flying spot" laser thermography, annotated for crack-type defect localization. It is followed by a benchmark of several state-of-the-art machine learning architectures. The benefits of this database for transfer to the detection and localization of cracks in coated metallic samples are highlighted, with very reduced amounts of data. Finally, this work gives a preliminary exploration on the use of individual thermal images, yielding to detection and localization performance comparable to the previously studied reconstructed thermal maps, which consists in the normalized mean of all the thermal images on a region of interest, after registration.

show abstract

Section: Review Of the Literaturementioning

confidence: 99%

Database for transfer learning in crack detection and localization on metallic materials using flying spot thermography and deep learning

Helvig,

Trouvé-Peloux,

Gavérina

et al. 2024

J. Electron. Imag.

View full text Add to dashboard Cite

show abstract

“…Furthermore, in the case of optical reflection-based approaches, the color of the test surface affects the measurements significantly. Tamas et al [17,18] successfully classified surface properties based on the measurement of thermal response to laser excitation. However, this method may require relatively expensive equipment for application in smart devices.…”

Section: Surface Identificationmentioning

confidence: 99%

Knocking and Listening: Learning Mechanical Impulse Response for Understanding Surface Characteristics

Ryu¹,

Kim²

2020

Sensors

View full text Add to dashboard Cite

Inspired by spiders that can generate and sense vibrations to obtain information regarding a substrate, we propose an intelligent system that can recognize the type of surface being touched by knocking the surface and listening to the vibrations. Hence, we developed a system that is equipped with an electromagnetic hammer for hitting the ground and an accelerometer for measuring the mechanical responses induced by the impact. We investigate the feasibility of sensing 10 different daily surfaces through various machine-learning techniques including recent deep-learning approaches. Although some test surfaces are similar, experimental results show that our system can recognize 10 different surfaces remarkably well (test accuracy of 98.66%). In addition, our results without directly hitting the surface (internal impact) exhibited considerably high test accuracy (97.51%). Finally, we conclude this paper with the limitations and future directions of the study.

show abstract

“…An emerging, interesting approach is to use active thermography for material characterization [4,5]. A model-based approach for characterizing unknown materials using laser thermography is proposed in [4]. Results demonstrated the ability of the approach to classify different materials based on their thermal properties.…”

Section: Related Workmentioning

confidence: 99%

“…Active thermography is a remote and non-contact method with the potential to examine the physical properties of objects in unknown environments, such as material classification [4] or thermal characterization [5]. This approach relies on shining a laser source at the surface of the object, uses a thermal camera to examine the dissipation of thermal heat at the surface of the object, and feeds the thermal stream into a machine learning classifier or regressor to identify the material class of the object or to estimate its corresponding physical properties.…”

Section: Introductionmentioning

confidence: 99%

Estimating Weight of Unknown Objects Using Active Thermography

Aujeszky¹,

Κορρές²,

Eid³

et al. 2019

Robotics

Self Cite

View full text Add to dashboard Cite

Successful manipulation of unknown objects requires an understanding of their physical properties. Infrared thermography has the potential to provide real-time, contactless material characterization for unknown objects. In this paper, we propose an approach that utilizes active thermography and custom multi-channel neural networks to perform classification between samples and regression towards the density property. With the help of an off-the-shelf technology to estimate the volume of the object, the proposed approach is capable of estimating the weight of the unknown object. We show the efficacy of the infrared thermography approach to a set of ten commonly used materials to achieve a 99.1% R 2 -fit for predicted versus actual density values. The system can be used with tele-operated or autonomous robots to optimize grasping techniques for unknown objects without touching them.

show abstract

Material classification with laser thermography and machine learning

Cited by 12 publications

References 36 publications

Database for transfer learning in crack detection and localization on metallic materials using flying spot thermography and deep learning

Database for transfer learning in crack detection and localization on metallic materials using flying spot thermography and deep learning

Knocking and Listening: Learning Mechanical Impulse Response for Understanding Surface Characteristics

Estimating Weight of Unknown Objects Using Active Thermography

Contact Info

Product

Resources

About