Accurate Estimation of Tensile Strength of 3D Printed Parts Using Machine Learning Algorithms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F22%3A10250054" target="_blank" >RIV/61989100:27230/22:10250054 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/2227-9717/10/6/1158" target="_blank" >https://www.mdpi.com/2227-9717/10/6/1158</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/pr10061158" target="_blank" >10.3390/pr10061158</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Accurate Estimation of Tensile Strength of 3D Printed Parts Using Machine Learning Algorithms

Popis výsledku v původním jazyce

Manufacturing processes need optimization. Three-dimensional (3D) printing is not an exception. Consequently, 3D printing process parameters must be accurately calibrated to fabricate objects with desired properties irrespective of their field of application. One of the desired properties of a 3D printed object is its tensile strength. Without predictive models, optimizing the 3D printing process for achieving the desired tensile strength can be a tedious and expensive exercise. This study compares the effectiveness of the following five predictive models (i.e., machine learning algorithms) used to estimate the tensile strength of 3D printed objects: (1) linear regression, (2) random forest regression, (3) AdaBoost regression, (4) gradient boosting regression, and (5) XGBoost regression. First, all the machine learning models are tuned for optimal hyperparameters, which control the learning process of the algorithms. Then, the results from each machine learning model are compared using several statistical metrics such as R-2, mean squared error (MSE), mean absolute error (MAE), maximum error, and median error. The XGBoost regression model is the most effective among the tested algorithms. It is observed that the five tested algorithms can be ranked as XG boost &gt; gradient boost &gt; AdaBoost &gt; random forest &gt; linear regression.

Název v anglickém jazyce

Accurate Estimation of Tensile Strength of 3D Printed Parts Using Machine Learning Algorithms

Popis výsledku anglicky

Manufacturing processes need optimization. Three-dimensional (3D) printing is not an exception. Consequently, 3D printing process parameters must be accurately calibrated to fabricate objects with desired properties irrespective of their field of application. One of the desired properties of a 3D printed object is its tensile strength. Without predictive models, optimizing the 3D printing process for achieving the desired tensile strength can be a tedious and expensive exercise. This study compares the effectiveness of the following five predictive models (i.e., machine learning algorithms) used to estimate the tensile strength of 3D printed objects: (1) linear regression, (2) random forest regression, (3) AdaBoost regression, (4) gradient boosting regression, and (5) XGBoost regression. First, all the machine learning models are tuned for optimal hyperparameters, which control the learning process of the algorithms. Then, the results from each machine learning model are compared using several statistical metrics such as R-2, mean squared error (MSE), mean absolute error (MAE), maximum error, and median error. The XGBoost regression model is the most effective among the tested algorithms. It is observed that the five tested algorithms can be ranked as XG boost &gt; gradient boost &gt; AdaBoost &gt; random forest &gt; linear regression.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20301 - Mechanical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Processes

ISSN

2227-9717

e-ISSN

—

Svazek periodika

10

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

16

Strana od-do

nestrankovano

Kód UT WoS článku

000817333900001

EID výsledku v databázi Scopus

2-s2.0-85132721393