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The Implementation of Neural Networks for Polymer Mold Surface Evaluation

Authors :
Hana Vrbová
Milena Kubišová
Dagmar Měřínská
Martin Novák
Vladimir Pata
Jana Knedlová
Michal Sedlačík
Oldřich Šuba
Source :
Micromachines, Vol 15, Iss 1, p 102 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

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.

Details

Language :
English
ISSN :
2072666X
Volume :
15
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Micromachines
Publication Type :
Academic Journal
Accession number :
edsdoj.052c02a9117945a09c9012904237afc8
Document Type :
article
Full Text :
https://doi.org/10.3390/mi15010102