Change search
Link to record
Permanent link

Direct link
Alternative names
Publications (10 of 133) Show all publications
Zia, S., Carlson, J. E. & Åkerfeldt, P. (2024). Prediction of manufacturing parameters of additively manufactured 316L steel samples using ultrasound fingerprinting. Ultrasonics, 137, Article ID 107196.
Open this publication in new window or tab >>Prediction of manufacturing parameters of additively manufactured 316L steel samples using ultrasound fingerprinting
2024 (English)In: Ultrasonics, ISSN 0041-624X, E-ISSN 1874-9968, Vol. 137, article id 107196Article in journal (Refereed) Published
Abstract [en]

Metal based additive manufacturing techniques such as laser powder bed fusion can produce parts with complex designs as compared to traditional manufacturing. The quality is affected by defects such as porosity or lack of fusion that can be reduced by online control of manufacturing parameters. The conventional way of testing is time consuming and does not allow the process parameters to be linked to the mechanical properties. In this paper, ultrasound data along with supervised learning is used to estimate the manufacturing parameters of 316L steel samples. The steel samples are manufactured with varying process parameters (speed, hatch distance and power) in two batches that are placed at different locations on the build plate. These samples are examined with ultrasound using a focused transducer. The ultrasound scans are performed in a dense grid in the build and transverse direction, respectively. Part of the ultrasound data are used to train a partial least squares regression algorithm by labelling the data with the corresponding manufacturing parameters (speed, hatch distance and power, and build plate location). The remaining data are used for testing of the resulting model. To assess the uncertainty of the method, a Monte-Carlo simulation approach is adopted, providing a confidence interval for the predicted manufacturing parameters. The analysis is performed in both the build and transverse direction. Since the material is anisotropic, results show that there are differences, but that the manufacturing parameters has an effect of the material microstructure in both directions.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Ultrasound fingerprinting, Additive manufacturing, Supervised learning, Non-destructive evaluation
National Category
Signal Processing
Research subject
Signal Processing; Engineering Materials
Identifiers
urn:nbn:se:ltu:diva-102002 (URN)10.1016/j.ultras.2023.107196 (DOI)2-s2.0-85175642976 (Scopus ID)
Funder
Luleå University of Technology
Note

Validerad;2023;Nivå 2;2023-11-15 (joosat);

CC BY 4.0 License

Available from: 2023-11-01 Created: 2023-11-01 Last updated: 2024-01-18Bibliographically approved
Zia, S., Carlson, J. E., Åkerfeldt, P. & Mishra, P. (2023). Estimating manufacturing parameters of additively manufactured 316L steel cubes using ultrasound fingerprinting. Paper presented at 13th European Conference on Non-Destructive Testing (ECNDT23), Lisbon, Portugal, July 3-7, 2023. Research and Review Journal of Nondestructive Testing (ReJNDT), 1(1), Article ID 28214.
Open this publication in new window or tab >>Estimating manufacturing parameters of additively manufactured 316L steel cubes using ultrasound fingerprinting
2023 (English)In: Research and Review Journal of Nondestructive Testing (ReJNDT), ISSN 2941-4989, Vol. 1, no 1, article id 28214Article in journal (Refereed) Published
Abstract [en]

Metal based additive manufacturing techniques such as laser powder bed fusion (LPBF) can produce parts with complex designs as compared to traditional manufacturing. The quality is affected by defects such as porosity or lack of fusion that can be reduced by online control of manufacturing parameters. The conventional way of testing is time consuming and does not allow the process parameters to be linked to the mechanical properties. In this paper, ultrasound data along with supervised learning is used to estimate the manufacturing parameters of 316L steel cubes. Nine cubes with varying manufacturing parameters (speed, hatch distance and power) are examined with ultrasound using focused transducers. The volumetric energy density (VED) is calculated from the process parameters for each cube. The ultrasound scans are performed in a dense grid in the built and transverse direction. The ultrasound data is used in partial least square regression algorithm by labelling the data with speed, hatch distance and power and then by labelling the same data with the VED. These models are computed for both measurement directions and as the samples are anisotropic, we see different behaviours of estimation in each direction. The model is then validated with an unknown set from the same 9 cubes. The manufacturing parameters are estimated and validated with a good accuracy making way for online process control.

Place, publisher, year, edition, pages
NDT.net, 2023
Keywords
3D-printing, supervised learning, signal processing, ultrasound fingerprinting
National Category
Signal Processing Metallurgy and Metallic Materials Manufacturing, Surface and Joining Technology
Research subject
Signal Processing; Engineering Materials
Identifiers
urn:nbn:se:ltu:diva-99218 (URN)10.58286/28214 (DOI)
Conference
13th European Conference on Non-Destructive Testing (ECNDT23), Lisbon, Portugal, July 3-7, 2023
Note

Godkänd;2023;Nivå 0;2023-08-10 (hanlid);Konferensartikel i tidskrift

Available from: 2023-07-18 Created: 2023-07-18 Last updated: 2024-01-18Bibliographically approved
Lei, X., Wirdelius, H. & Carlson, J. E. (2023). Model-Based Parametric Study of Surface-Breaking Defect Characterization Using Half-Skip Total Focusing Method. Journal of nondestructive evaluation, 42(2), Article ID 37.
Open this publication in new window or tab >>Model-Based Parametric Study of Surface-Breaking Defect Characterization Using Half-Skip Total Focusing Method
2023 (English)In: Journal of nondestructive evaluation, ISSN 0195-9298, E-ISSN 1573-4862, Vol. 42, no 2, article id 37Article in journal (Refereed) Published
Abstract [en]

As the demand of structural integrity in manufacturing industries is increasing, the ultrasonic array technique has drawn more attention thanks to its inspection flexibility and versatility. By taking advantage of the possibility of individual triggering of each array element, full matrix capture (FMC) data acquisition strategy has been developed that contains the entire information of an inspection scenario. Total focusing method (TFM) as one of the ultrasonic imaging algorithms, is preferably applied to FMC dataset since it uses all information in FMC to synthetically focus the sound energy at every image pixel in the region of interest. Half-skip TFM (HSTFM) is proposed in multi-mode TFM imaging that involves a backwall reflection wave path, so that the defect profile could be reconstructed for accurate defect characterization. In this paper, a method involving Snell’s law-based wave mode conversion is proposed to account for more reasonable wave propagation time when wave mode conversion happens at backwall reflection in HSTFM. A series of model based simulations (in software simSUNDT) are performed for parametric studies, with the intention of investigating the capability of defect characterization using HSTFM with varying tilt angle and relative position of surface-breaking notch to array probe. The results show that certain TFM modes could help with defect characterization, but the effectiveness is limited with varying defect features. It is inappropriate to address a certain mode for all characterization perspectives but rather a combination, i.e., multi-mode TFM, should be adopted for possible interpretation and characterization of defect features.

Place, publisher, year, edition, pages
Springer Nature, 2023
Keywords
Ultrasonic array, Defect characterization, Total focusing method, Simulation, simSUNDT
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Signal Processing
Identifiers
urn:nbn:se:ltu:diva-97004 (URN)10.1007/s10921-023-00949-7 (DOI)2-s2.0-85153231650 (Scopus ID)
Note

Validerad;2023;Nivå 2;2023-05-04 (hanlid)

Available from: 2023-05-04 Created: 2023-05-04 Last updated: 2023-05-08Bibliographically approved
Lei, X., Wirdelius, H. & Carlson, J. E. (2023). The effect of ultrasound wave path estimation to defect characterization capability in half-skip total focusing method. Paper presented at 13th European Conference on Nondestructive Testing (ECNDT), Lisbon, Portugal, July 3-7, 2023. Research and Review Journal of Nondestructive Testing (ReJNDT), 1(1)
Open this publication in new window or tab >>The effect of ultrasound wave path estimation to defect characterization capability in half-skip total focusing method
2023 (English)In: Research and Review Journal of Nondestructive Testing (ReJNDT), ISSN 2941-4989, Vol. 1, no 1Article in journal (Refereed) Published
Abstract [en]

The total focusing method (TFM) is a post-processing imaging technique applied on full matrix capture (FMC) ultrasonic inspection (UT) dataset. In TFM the ultrasonic wave energy is synthetically focused on every pixel in the image region of interest (ROI). In terms of half-skip TFM (HSTFM), wave mode conversion happens when the wave rebounds at interface, such as specimen backwall. This paper aims to propose and evaluate a method that involves Snell’s law to address accurate estimation of distanceof-flight (DOF) of wave propagation when wave mode conversion appears in HSTFM. This HSTFM algorithm is applied to both experimental and simulated FMC dataset that inspects a surface-breaking notch for notch image reconstruction. Comparisons between images with and without considering Snell’s law in wave mode conversion show visible difference that could lead to misinterpretations in characterizing the defect. The sensitivity of TFM to varying defect features such as defect tilt angle is also studied using simulated FMC datasets.

Place, publisher, year, edition, pages
NDT.net, 2023
National Category
Signal Processing Fluid Mechanics and Acoustics
Research subject
Signal Processing
Identifiers
urn:nbn:se:ltu:diva-95233 (URN)10.58286/28202 (DOI)
Conference
13th European Conference on Nondestructive Testing (ECNDT), Lisbon, Portugal, July 3-7, 2023
Note

Godkänd;2023;Nivå 0;2023-08-22 (joosat);Konferensartikel i tidskrift;

Licens fulltext: CC-BY-4.0

Available from: 2023-01-11 Created: 2023-01-11 Last updated: 2023-08-22Bibliographically approved
Zia, S., Carlson, J. E. & Åkerfeldt, P. (2023). Ultrasonic Assessment of the Effect of Manufacturing Parameters on the Variability Within Additively Manufactured 316L Samples. In: 2023 IEEE International Ultrasonics Symposium (IUS): . Paper presented at IEEE International Ultrasonics Symposium (IUS 2023), Montreal, Quebec, Canada, September 3-8, 2023. IEEE
Open this publication in new window or tab >>Ultrasonic Assessment of the Effect of Manufacturing Parameters on the Variability Within Additively Manufactured 316L Samples
2023 (English)In: 2023 IEEE International Ultrasonics Symposium (IUS), IEEE, 2023Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2023
Series
IEEE Symposium (IUS) Ultrasonics
National Category
Manufacturing, Surface and Joining Technology Production Engineering, Human Work Science and Ergonomics
Research subject
Signal Processing; Engineering Materials
Identifiers
urn:nbn:se:ltu:diva-102001 (URN)10.1109/IUS51837.2023.10307294 (DOI)
Conference
IEEE International Ultrasonics Symposium (IUS 2023), Montreal, Quebec, Canada, September 3-8, 2023
Note

ISBN for host publication: 979-8-3503-4646-6, 979-8-3503-4645-9

Available from: 2023-11-01 Created: 2023-11-01 Last updated: 2024-01-18Bibliographically approved
Gupta, P. & Carlson, J. E. (2022). Deep Learning for Modeling of Sound Pressure Fields of Real-World Ultrasound Transducers. In: 2022 IEEE International Ultrasonics Symposium (IUS): . Paper presented at 2022 IEEE International Ultrasonics Symposium (IUS), Venice, Italy, 10-13 October, 2022. IEEE
Open this publication in new window or tab >>Deep Learning for Modeling of Sound Pressure Fields of Real-World Ultrasound Transducers
2022 (English)In: 2022 IEEE International Ultrasonics Symposium (IUS), IEEE, 2022Conference paper, Published paper (Refereed)
Abstract [en]

There are several freely available toolboxes for modeling the sound pressure field of ultrasound transducers and transducer arrays (e.g., Field II, k-Wave, and DREAM, etc.). These model the beam patterns, or how the ultrasound pulse changes depending on where we observe it, i.e., they model the spatial impulse response of the transducers. Normally, the transmitted pulse is not modeled using these toolboxes, but instead it is assumed that this pulse shape is known. Also, the models are based on assumption of an ideal behavior of the transducers, which is not necessarily the case for a real-world transducers. As a consequence, fitting these models to real measurement data, in order for them to mimic the individual transducer available in the lab, is not generally not possible with any numerical accuracy. In this paper we show, instead, how a deep learning approach can be adopted to train a model that with numerical accuracy models an transducer individual. We compare the proposed technique with real measurements and models using the Field II toolbox and show that for the actual transducer at hand, the deep learning approach outperforms the results from Field II.

Place, publisher, year, edition, pages
IEEE, 2022
Series
IEEE International Ultrasonics Symposium, ISSN 1948-5719, E-ISSN 1948-5727
Keywords
Ultrasound imaging, Spatial impulse response (SIR), Deep neural networks, Sound pressure field
National Category
Signal Processing
Research subject
Signal Processing
Identifiers
urn:nbn:se:ltu:diva-94159 (URN)10.1109/IUS54386.2022.9958700 (DOI)000896080400493 ()2-s2.0-85143822050 (Scopus ID)
Conference
2022 IEEE International Ultrasonics Symposium (IUS), Venice, Italy, 10-13 October, 2022
Note

ISBN for host publication: 978-1-6654-6657-8

Available from: 2022-11-20 Created: 2022-11-20 Last updated: 2023-09-07Bibliographically approved
Zia, S., Carlson, J. E. & Åkerfeldt, P. (2022). Linking Ultrasound Data to Manufacturing Parameters of 3D-printed Polymers Using Supervised Learning. In: 2022 IEEE International Ultrasonics Symposium (IUS): . Paper presented at 2022 IEEE International Ultrasonics Symposium (IUS), Venice, Italy, 10-13 October, 2022. IEEE
Open this publication in new window or tab >>Linking Ultrasound Data to Manufacturing Parameters of 3D-printed Polymers Using Supervised Learning
2022 (English)In: 2022 IEEE International Ultrasonics Symposium (IUS), IEEE, 2022Conference paper, Published paper (Refereed)
Abstract [en]

Additive manufacturing is used to produce complex and tailored products that cannot be achieved using conventional manufacturing approaches. The products can be made from different materials including polymers, metals, etc. The material is added layer by layer to create a final product. The mechanical properties of the final part depend on the process parameters. To improve the quality of the product these manufacturing parameters need to be optimised and for this purpose machine learning along with ultrasound measurements can be used. In this paper, the manufacturing parameters of 50 mm thick polymer cubes are linked to the ultrasound data using partial least squares regression. Three cubes with varying layer heights are made from PLA and ABS each, and backscattered responses of ultrasound are recorded from these six cubes. The ultrasound data is used in the partial least squares algorithm to estimate the layer height and the filament type. The clusters that are formed using the first few components obtained from the algorithm show that the data points of the six cubes can be distinguished and themanufacturing parameters are estimated with good accuracy.

Place, publisher, year, edition, pages
IEEE, 2022
Series
IEEE International Ultrasonics Symposium, ISSN 1948-5719, E-ISSN 1948-5727
Keywords
3D-printing, supervised learning, signal processing, ultrasound fingerprinting
National Category
Signal Processing
Research subject
Signal Processing; Engineering Materials
Identifiers
urn:nbn:se:ltu:diva-94224 (URN)10.1109/IUS54386.2022.9957554 (DOI)000896080400140 ()2-s2.0-85143800189 (Scopus ID)978-1-6654-6657-8 (ISBN)
Conference
2022 IEEE International Ultrasonics Symposium (IUS), Venice, Italy, 10-13 October, 2022
Available from: 2022-11-22 Created: 2022-11-22 Last updated: 2023-12-01Bibliographically approved
Zia, S., Carlson, J. E. & Åkerfeldt, P. (2022). On Estimation of Sound Velocity and Attenuation in Common 3D-Printing Filaments. In: 2022 IEEE International Ultrasonics Symposium (IUS): . Paper presented at 2022 IEEE International Ultrasonics Symposium (IUS), Venice, Italy, 10-13 October, 2022. IEEE
Open this publication in new window or tab >>On Estimation of Sound Velocity and Attenuation in Common 3D-Printing Filaments
2022 (English)In: 2022 IEEE International Ultrasonics Symposium (IUS), IEEE, 2022Conference paper, Published paper (Refereed)
Abstract [en]

Estimation of frequency-dependent attenuation and speed of sound using ultrasound is of great importance. The acoustic properties can be used for material characterization and to study the local variations in a solid. As ultrasound is a mechanical wave, it is directly sensitive to changes in the material properties. The layered nature of additively manufactured prod-ucts pose a challenge for the estimation of acoustic properties. The non-parametric approaches using frequency transforms are sensitive to noise. In this paper, a parametric model is used to estimate the phase velocity and attenuation of 3D-printed cubes. The received signal from the cubes is a superposition of the backscattered responses from multiple layers of the printed part. A reference echo from aluminium is used as an input to the linear model and to estimate the received ultrasound response. The estimate of the ultrasound signal using the linear model is similar to the measured data and it suggests that it can be used to estimate wave propagation in additively manufactured products. The estimated acoustic properties show an increasing trend with the frequency and dispersion can be seen due to the layered nature of the material.

Place, publisher, year, edition, pages
IEEE, 2022
Series
IEEE International Ultrasonics Symposium, ISSN 1948-5719, E-ISSN 1948-5727
Keywords
3D-printing, signal processing, Phase velocity, Attenuation
National Category
Signal Processing Other Materials Engineering
Research subject
Signal Processing; Engineering Materials
Identifiers
urn:nbn:se:ltu:diva-94162 (URN)10.1109/IUS54386.2022.9958029 (DOI)000896080400290 ()2-s2.0-85143826678 (Scopus ID)978-1-6654-6657-8 (ISBN)
Conference
2022 IEEE International Ultrasonics Symposium (IUS), Venice, Italy, 10-13 October, 2022
Available from: 2022-11-20 Created: 2022-11-20 Last updated: 2023-12-01Bibliographically approved
Ashraf, A., Carlson, J. E. & van de Beek, J. (2022). The solid-body reverberating ultrasound communications channel and its OFDM interference. In: Igor M. Moraes; Miguel Elias M. Campista; Yacine Ghamri-Doudane; Luís Henrique M. K. Costa; Marcelo G. Rubinstein (Ed.), 2022 IEEE Latin-American Conference on Communications (LATINCOM): . Paper presented at 14th IEEE Latin-American Conference on Communications (LATINCOM), Rio de Janeiro, November 30 - December 2, 2022. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>The solid-body reverberating ultrasound communications channel and its OFDM interference
2022 (English)In: 2022 IEEE Latin-American Conference on Communications (LATINCOM) / [ed] Igor M. Moraes; Miguel Elias M. Campista; Yacine Ghamri-Doudane; Luís Henrique M. K. Costa; Marcelo G. Rubinstein, Institute of Electrical and Electronics Engineers (IEEE), 2022Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we present an analytical approach to the solid-state ultrasound communications channel.  Channel reverberations and the long associated channel delay spreads pose the possibility that the channel length exceeds that of the moderate cyclic prefix in an orthogonal frequency division multiplexing (OFDM) system, resulting in intersymbol and intercarrier interference.  We present a channel model based on the propagation material characteristics and evaluate the extent and impact of the intrinsic OFDM interferences. We derive an analytical expression and show with simulations that the intersymbol and intercarrier interference (ISI and ICI) are spectrally concentrated to the lower frequencies of the OFDM multiplex.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
Keywords
Inter-symbol interference (ISI), inter-carrier interference (ICI), orthogonal frequency division multiplexing (OFDM), Cyclic Prefix (CP), Impulse Response (IR)
National Category
Telecommunications
Research subject
Signal Processing
Identifiers
urn:nbn:se:ltu:diva-95395 (URN)10.1109/LATINCOM56090.2022.10000637 (DOI)000918010500054 ()2-s2.0-85146712656 (Scopus ID)
Conference
14th IEEE Latin-American Conference on Communications (LATINCOM), Rio de Janeiro, November 30 - December 2, 2022
Funder
Swedish Research Council, 2019-05376
Note

ISBN för värdpublikation: 978-1-6654-8225-7

Available from: 2023-01-25 Created: 2023-01-25 Last updated: 2023-09-04Bibliographically approved
Carlson, J. E., Jansson, A. & Lundin, P. (2022). Ulraljudsmetod för mätning av belastning på bergbultar: [Ultrasound Method for Measurement of Load on Rock Bolts]. In: : . Paper presented at Bergdagarna 2022, Älvsjö, Sweden, September 28-29, 2022.
Open this publication in new window or tab >>Ulraljudsmetod för mätning av belastning på bergbultar: [Ultrasound Method for Measurement of Load on Rock Bolts]
2022 (Swedish)Conference paper, Published paper (Refereed)
Abstract [sv]

Inom både gruvindustri och infrastrukturprojekt är tillståndskontroll av bergbultar av stort intresse. Det finns dedikerade mätbultar som kan installeras för att detektera och följa förändringar över tid. Det finns även metoder för att kontrollera ingjutningen av bultar vid installation. Däremot saknas idag mätmetoder för att följa tillståndet hos generiska bergbultar över tid, eller för att detektera avvikelser bland sedan tidigare installerade bultar.I detta projekt utvecklas en metod baserad på ultraljud för att följa förändringar i mekaniska egenskaper över tid och för att kunna detektera avvikande bultar i en population av redan installerade bultar. Metoden bygger på en kort ultraljudspuls skickas in i bulten från den fria änden och att en reflekterat ljud från hela bultens längd samlas in. Detta fingeravtryck av bulten kan sedan jämföras bultar emellan, eller följas för en enskild bult över tid.För att demonstrera principen visar vi hur uppmätta ultraljudssignaturer kan användas för att modellera hela dragprovskurvan (kraft mot töjning), från vila till brottgräns för en c:a 3 meter lång dynamisk bergbult.

Abstract [en]

In both the mining industry and infrastructure projects, condition monitoring of rock bolts is of great interest. There are dedicated measuring bolts that can be installed to detect and monitor changes over time. There are also methods to check the grouting of bolts during installation. There is, however, there are no available measurement technique for monitoring the condition of generic rock bolts over time, or for detecting deviations among previously installed bolts. In this project, a method based on ultrasound is developed to monitor changes in mechanical properties over time and to be able to detect deviating bolts in a population of already installed bolts. The method is based on a short ultrasonic pulse being sent into the bolt from the free end and that the reflected sound from the entire length of the bolt is collected. This fingerprint of the bolt can then be compared between bolts, or monitored over time, for an individual. To demonstrate the principle, we show how measured ultrasonic signatures can be used to model the entire stress-strain curve (force-elongation), from rest to breakage for an approximately 3 meter long dynamic rock bolt.

Keywords
Condition monitoring, rock bolts, ultrasound, machine learning, Tillståndskontroll, bergbultar, ultraljud, maskininlärning
National Category
Signal Processing Infrastructure Engineering
Research subject
Signal Processing
Identifiers
urn:nbn:se:ltu:diva-90041 (URN)
Conference
Bergdagarna 2022, Älvsjö, Sweden, September 28-29, 2022
Funder
Vinnova, 2019-01155Rock Engineering Research Foundation (BeFo), 421
Note

Funder: LKAB; Atlas Copco Industrial Technique; BESAB AB; AFRY AB

Available from: 2022-04-01 Created: 2022-04-01 Last updated: 2023-08-11Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6216-6132

Search in DiVA

Show all publications