Rebecka Lindvall

E-postadressrebecka.lindvalliprod.lthse

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Forskning

PhD student with focus on wear mechanisms in super-hard tool materials: polycrystalline diamond (PCD), polycrystalline cubic boron nitride (pcBN), and cemented carbide when machining advanced materials such as titanium alloys.

Fingeravtryck

Utforska forskningsämnen där Rebecka Lindvall är aktiv. Dessa ämnesetiketter kommer från personens arbeten. Tillsammans bildar de ett unikt fingeravtryck.

1 Liknande profiler

8 Artikel i vetenskaplig tidskrift

Predicting wear mechanisms of ultra-hard tooling in machining Ti6Al4V by diffusion couples and simulation
Lindvall, R., Bjerke, A., Salmasi, A., Lenrick, F., M'Saoubi, R., Ståhl, J. E. & Bushlya, V., 2023, I: Journal of the European Ceramic Society. 43, 2 , s. 291-303
Forskningsoutput: Tidskriftsbidrag › Artikel i vetenskaplig tidskrift › Peer review

Öppen tillgång
Characterisation of worn WC tool using STEM-EDS aided by principal component analysis
Lazar, I., Lindvall, R., Lenrick, F., Bushlya, V. & Ek, M., 2021 juli 1, I: Journal of Microscopy. 283, 1, s. 64-73 10 s.
Forskningsoutput: Tidskriftsbidrag › Artikel i vetenskaplig tidskrift › Peer review

Öppen tillgång
Improvement of tool utilization when hard turning with cBN tools at varying process parameters
Gutnichenko, O., Nilsson, M., Lindvall, R., Bushlya, V. & Andersson, M., 2021 juli 18, I: Wear. 477, 203900.
Forskningsoutput: Tidskriftsbidrag › Artikel i vetenskaplig tidskrift › Peer review

Öppen tillgång
Performance and wear mechanisms of uncoated cemented carbide cutting tools in Ti6Al4V machining
Lindvall, R., Lenrick, F., M'Saoubi, R., Ståhl, J. E. & Bushlya, V., 2021 juli 18, I: Wear. 477, 203824.
Forskningsoutput: Tidskriftsbidrag › Artikel i vetenskaplig tidskrift › Peer review

Öppen tillgång
A machine learning based approach for determining the stress-strain relation of grey cast iron from nanoindentation
Weng, J., Lindvall, R., Zhuang, K., Ståhl, J. E., Ding, H. & Zhou, J., 2020, I: Mechanics of Materials. 148, 103522.
Forskningsoutput: Tidskriftsbidrag › Artikel i vetenskaplig tidskrift › Peer review

Rebecka Lindvall

Personlig profil

Forskning

Fingeravtryck

Nätverk

Se detaljer

Predicting wear mechanisms of ultra-hard tooling in machining Ti6Al4V by diffusion couples and simulation

Characterisation of worn WC tool using STEM-EDS aided by principal component analysis

Improvement of tool utilization when hard turning with cBN tools at varying process parameters

Performance and wear mechanisms of uncoated cemented carbide cutting tools in Ti6Al4V machining

A machine learning based approach for determining the stress-strain relation of grey cast iron from nanoindentation

Rebecka Lindvall

Personlig profil

Forskning

Fingeravtryck

Nätverk

Forskningsoutput

Predicting wear mechanisms of ultra-hard tooling in machining Ti6Al4V by diffusion couples and simulation

Characterisation of worn WC tool using STEM-EDS aided by principal component analysis

Improvement of tool utilization when hard turning with cBN tools at varying process parameters

Performance and wear mechanisms of uncoated cemented carbide cutting tools in Ti6Al4V machining

A machine learning based approach for determining the stress-strain relation of grey cast iron from nanoindentation