Vickers hardness prediction from machine learning methods

to related objects

Location: Deutsche Nationalbibliothek Frankfurt am Main

ISSN: 2045-2322

Extent: Online-Ressource

Language: Englisch

Notes: online resource.

Bibliographic citation: Vickers hardness prediction from machine learning methods ; volume:12 ; number:1 ; day:28 ; month:12 ; year:2022 ; pages:1-12 ; date:12.2022
Scientific reports ; 12, Heft 1 (28.12.2022), 1-12, 12.2022

Creator: Dovale-Farelo, Viviana
Tavadze, Pedram
Lang, Logan
Bautista-Hernandez, Alejandro
Romero, Aldo H.

Contributor: SpringerLink (Online service)

DOI: 10.1038/s41598-022-26729-3

URN: urn:nbn:de:101:1-2023030921371864855914

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 11:00 AM CEST

Data provider

This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.

Show original at data provider

Associated

Dovale-Farelo, Viviana
Tavadze, Pedram
Lang, Logan
Bautista-Hernandez, Alejandro
Romero, Aldo H.
SpringerLink (Online service)

Other Objects (12)

Application of Data Mining and Machine Learning Methods to Industrial Heat Treatment Processes for Hardness Prediction

Synthesis, microstructure and indentation Vickers hardness for (Y3Fe5O12) x /Cu0.5Tl0.5Ba2Ca2Cu3O10-δ composites

Prediction of Hardness after Industrialized Bainitization of 100Cr6 based on Process Parameters by Application of Machine Learning Methods ∗

Machine Learning-Based Hardness Prediction of High-Entropy Alloys for Laser Additive Manufacturing

Machine learning methods for metabolic pathway prediction

Hardness of surface-densified wood. Part 2: prediction of the density profile by hardness measurements

Kapitel

Hardness

Prediction of the muscle strength by the muscle thickness and hardness using ultrasound muscle hardness meter

The influence of different storage media on Vickers hardness and surface roughness of CAD/CAM resin composites

Prediction of cadmium content using machine learning methods

Machine learning methods for adult OSAHS risk prediction

Application of Data Mining and Machine Learning Methods to Industrial Heat Treatment Processes for Hardness Prediction

Synthesis, microstructure and indentation Vickers hardness for (Y3Fe5O12) x /Cu0.5Tl0.5Ba2Ca2Cu3O10-δ composites

Prediction of Hardness after Industrialized Bainitization of 100Cr6 based on Process Parameters by Application of Machine Learning Methods ∗

Machine Learning-Based Hardness Prediction of High-Entropy Alloys for Laser Additive Manufacturing

Machine learning methods for metabolic pathway prediction

Hardness of surface-densified wood. Part 2: prediction of the density profile by hardness measurements

Kapitel

Hardness

Prediction of the muscle strength by the muscle thickness and hardness using ultrasound muscle hardness meter

The influence of different storage media on Vickers hardness and surface roughness of CAD/CAM resin composites

Prediction of cadmium content using machine learning methods

Machine learning methods for adult OSAHS risk prediction

Application of Data Mining and Machine Learning Methods to Industrial Heat Treatment Processes for Hardness Prediction

Synthesis, microstructure and indentation Vickers hardness for (Y3Fe5O12) x /Cu0.5Tl0.5Ba2Ca2Cu3O10-δ composites

Prediction of Hardness after Industrialized Bainitization of 100Cr6 based on Process Parameters by Application of Machine Learning Methods ∗

Machine Learning-Based Hardness Prediction of High-Entropy Alloys for Laser Additive Manufacturing

Machine learning methods for metabolic pathway prediction

Hardness of surface-densified wood. Part 2: prediction of the density profile by hardness measurements

Kapitel

Hardness

Prediction of the muscle strength by the muscle thickness and hardness using ultrasound muscle hardness meter

The influence of different storage media on Vickers hardness and surface roughness of CAD/CAM resin composites

Prediction of cadmium content using machine learning methods

Machine learning methods for adult OSAHS risk prediction

Cultural heritage institutions wishing to register will find more information here.

Fields marked * need to be filled in.

Username*

Please enter your username

Email*

Please enter your email address

Please do not fill this field

First name

Last name

Password*

Please enter your password

Confirm password*

Please enter the same password

I have read and agree to the privacy policy for the collection of personal data.*

This field is required.

I would like to subscribe to the newsletter of the Deutsche Digitale Bibliothek. See newsletter subscription info.

Account created

Your "My DDB" account has been successfully created. Before you can log in to your account, you must click the confirmation link in the message we just sent to the email address you provided.

Vickers hardness prediction from machine learning methods

Show object

References and Relationships

Contributors, Places and Time

Further information

Data provider

Associated

Other Objects (12)

Application of Data Mining and Machine Learning Methods to Industrial Heat Treatment Processes for Hardness Prediction

Application of Data Mining and Machine Learning Methods to Industrial Heat Treatment Processes for Hardness Prediction

Synthesis, microstructure and indentation Vickers hardness for (Y3Fe5O12) x /Cu0.5Tl0.5Ba2Ca2Cu3O10-δ composites

Prediction of Hardness after Industrialized Bainitization of 100Cr6 based on Process Parameters by Application of Machine Learning Methods ∗

Machine Learning-Based Hardness Prediction of High-Entropy Alloys for Laser Additive Manufacturing

Machine learning methods for metabolic pathway prediction

Hardness of surface-densified wood. Part 2: prediction of the density profile by hardness measurements

Hardness

Prediction of the muscle strength by the muscle thickness and hardness using ultrasound muscle hardness meter

The influence of different storage media on Vickers hardness and surface roughness of CAD/CAM resin composites

Prediction of cadmium content using machine learning methods

Machine learning methods for adult OSAHS risk prediction

Application of Data Mining and Machine Learning Methods to Industrial Heat Treatment Processes for Hardness Prediction

Application of Data Mining and Machine Learning Methods to Industrial Heat Treatment Processes for Hardness Prediction

Synthesis, microstructure and indentation Vickers hardness for (Y3Fe5O12) x /Cu0.5Tl0.5Ba2Ca2Cu3O10-δ composites

Prediction of Hardness after Industrialized Bainitization of 100Cr6 based on Process Parameters by Application of Machine Learning Methods ∗

Machine Learning-Based Hardness Prediction of High-Entropy Alloys for Laser Additive Manufacturing

Machine learning methods for metabolic pathway prediction

Hardness of surface-densified wood. Part 2: prediction of the density profile by hardness measurements

Hardness

Prediction of the muscle strength by the muscle thickness and hardness using ultrasound muscle hardness meter

The influence of different storage media on Vickers hardness and surface roughness of CAD/CAM resin composites

Prediction of cadmium content using machine learning methods

Machine learning methods for adult OSAHS risk prediction

Application of Data Mining and Machine Learning Methods to Industrial Heat Treatment Processes for Hardness Prediction

Application of Data Mining and Machine Learning Methods to Industrial Heat Treatment Processes for Hardness Prediction

Synthesis, microstructure and indentation Vickers hardness for (Y3Fe5O12) x /Cu0.5Tl0.5Ba2Ca2Cu3O10-δ composites

Prediction of Hardness after Industrialized Bainitization of 100Cr6 based on Process Parameters by Application of Machine Learning Methods ∗

Machine Learning-Based Hardness Prediction of High-Entropy Alloys for Laser Additive Manufacturing

Machine learning methods for metabolic pathway prediction

Hardness of surface-densified wood. Part 2: prediction of the density profile by hardness measurements

Hardness

Prediction of the muscle strength by the muscle thickness and hardness using ultrasound muscle hardness meter

The influence of different storage media on Vickers hardness and surface roughness of CAD/CAM resin composites

Prediction of cadmium content using machine learning methods

Machine learning methods for adult OSAHS risk prediction

Related objects

Reset password