The Implementation of Neural Networks for Polymer Mold Surface Evaluation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28110%2F24%3A63578843" target="_blank" >RIV/70883521:28110/24:63578843 - isvavai.cz</a>

Alternative codes found

RIV/70883521:28610/24:63578843

Result on the web

<a href="https://www.mdpi.com/2072-666X/15/1/102" target="_blank" >https://www.mdpi.com/2072-666X/15/1/102</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/mi15010102" target="_blank" >10.3390/mi15010102</a>

Result language

angličtina

Original language name

The Implementation of Neural Networks for Polymer Mold Surface Evaluation

Original language description

This paper presents the measurement and evaluation of the surfaces of molds produced using additive technologies. This is an emerging trend in mold production. The surfaces of such molds must be treated, usually using laser-based alternative machining methods. Regular evaluation is necessary because of the gradually deteriorating quality of the mold surface. However, owing to the difficulty in scanning the original surface of the injection mold, it is necessary to perform surface replication. Therefore, this study aims to describe the production of surface replicas for in-house developed polymer molds together with the determination of suitable descriptive parameters, the method of comparing variances, and the mean values for the surface evaluation. Overall, this study presents a new summary of the evaluation process of replicas of the surfaces of polymer molds. The nonlinear regression methodology provides the corresponding functional dependencies between the relevant parameters. The statistical significance of a neural network with two hidden layers based on the principle of Rosenblatt’s perceptron has been proposed and verified. Additionally, machine learning was utilized to better compare the original surface and its replica.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10404 - Polymer science

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

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

Name of the periodical

Micromachines

ISSN

2072-666X

e-ISSN

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Volume of the periodical

15

Issue of the periodical within the volume

1

Country of publishing house

CH - SWITZERLAND

Number of pages

12

Pages from-to

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UT code for WoS article

001150888500001

EID of the result in the Scopus database

2-s2.0-85183321522