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用于聚合物模具表面评估的神经网络实现

The Implementation of Neural Networks for Polymer Mold Surface Evaluation.

作者信息

Vrbová Hana, Kubišová Milena, Měřínská Dagmar, Novák Martin, Pata Vladimir, Knedlová Jana, Sedlačík Michal, Šuba Oldřich

机构信息

Faculty of Technology, Tomas Bata University in Zlin, Vavreckova 5669, 760 01 Zlin, Czech Republic.

Centre of Polymer Systems, University Institute, Tomas Bata University in Zlin, Trida T. Bati 5678, 760 01 Zlin, Czech Republic.

出版信息

Micromachines (Basel). 2024 Jan 5;15(1):102. doi: 10.3390/mi15010102.

DOI:10.3390/mi15010102
PMID:38258221
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10821243/
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.

摘要

本文介绍了使用增材制造技术生产的模具表面的测量与评估。这是模具生产中的一个新兴趋势。此类模具的表面必须进行处理，通常采用基于激光的替代加工方法。由于模具表面质量逐渐下降，定期评估是必要的。然而，由于注塑模具原始表面扫描困难，有必要进行表面复制。因此，本研究旨在描述自行开发的聚合物模具表面复制品的制作过程，以及确定合适的描述参数、比较方差的方法和表面评估的平均值。总体而言，本研究给出了聚合物模具表面复制品评估过程的新总结。非线性回归方法提供了相关参数之间的相应函数依赖关系。基于罗森布拉特感知器原理的具有两个隐藏层的神经网络的统计显著性已被提出并验证。此外，利用机器学习来更好地比较原始表面及其复制品。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bad/10821243/9c9d51d082a3/micromachines-15-00102-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bad/10821243/fb8af6d894fc/micromachines-15-00102-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bad/10821243/e43c4c51c2ad/micromachines-15-00102-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bad/10821243/28b43de3281a/micromachines-15-00102-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bad/10821243/f122c5471e56/micromachines-15-00102-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bad/10821243/c2d96ae6783b/micromachines-15-00102-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bad/10821243/1280e57c8eef/micromachines-15-00102-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bad/10821243/c2dd45847458/micromachines-15-00102-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bad/10821243/b967152856dd/micromachines-15-00102-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bad/10821243/105de9a6170a/micromachines-15-00102-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bad/10821243/9c9d51d082a3/micromachines-15-00102-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bad/10821243/fb8af6d894fc/micromachines-15-00102-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bad/10821243/e43c4c51c2ad/micromachines-15-00102-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bad/10821243/28b43de3281a/micromachines-15-00102-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bad/10821243/f122c5471e56/micromachines-15-00102-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bad/10821243/c2d96ae6783b/micromachines-15-00102-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bad/10821243/1280e57c8eef/micromachines-15-00102-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bad/10821243/c2dd45847458/micromachines-15-00102-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bad/10821243/b967152856dd/micromachines-15-00102-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bad/10821243/105de9a6170a/micromachines-15-00102-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bad/10821243/9c9d51d082a3/micromachines-15-00102-g010.jpg

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本文引用的文献

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Fracture Resistance Analysis of 3D-Printed Polymers.3D打印聚合物的抗断裂性分析
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2
Replication of Overmolded Orthopedic Implants with a Functionalized Thin Layer of Biodegradable Polymer.带有可生物降解聚合物功能化薄层的包覆成型骨科植入物的复制
Polymers (Basel). 2018 Jun 26;10(7):707. doi: 10.3390/polym10070707.
3
Generalising Ward's Method for Use with Manhattan Distances.推广沃德法以用于曼哈顿距离。
PLoS One. 2017 Jan 13;12(1):e0168288. doi: 10.1371/journal.pone.0168288. eCollection 2017.