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2023Subject
600 Technology 660 Chemical engineering SpritzgießenMaschinelles LernenÄhnlichkeitAnalysePrognosemodellFallbasiertes SchließenMetadata
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Aufsatz
Parameter recommendation for injection molding based on similarity analysis of injection molded parts
Abstract
Injection molding is a widespread manufacturing process for producing complex plastic shapes. The process is controlled by a large number of parameters, which show interactions with each other. Thereby the determination of an optimal operating point during sampling is a resource-consuming and demanding task. The data, which is generated during sampling, is usually not used for subsequent sampling processes. In the context of Industry 4.0, manufacturing companies attempt to optimize production efficiency to save time and resources and remain competitive. Machine learning methods offer great potential to analyze correlations in already generated data in order to predict optimal parameters of the operating point for newly developed parts, which can only be determined with high experimental effort.
The aim of the investigations is to provide a method for recommending setting parameters of the injection molding machine for new injection molds. This method is intended to make a significant contribution to reducing the elaborate tests during sampling. For this purpose, a concept was developed that, in the first step, calculates the geometric similarity of the new mold to a known database with part information from molds that have already been sampled (here: 743 parts). In the second step, the new injection mold is assigned to a cluster containing geometrically similar parts. Since the information on the robust operating point is known for each part of the database, this information is used to calculate the operating point for the new part. The applied case-based reasoning method provides the machine operator with a recommended operating point for a new injection mold without the need to carry out elaborate tests beforehand. Since the operating point of an injection molding machine consists of several parameters, the investigations were initially focused on the flow rate, which is one of the key parameters for controlling the injection phase.
The aim of the investigations is to provide a method for recommending setting parameters of the injection molding machine for new injection molds. This method is intended to make a significant contribution to reducing the elaborate tests during sampling. For this purpose, a concept was developed that, in the first step, calculates the geometric similarity of the new mold to a known database with part information from molds that have already been sampled (here: 743 parts). In the second step, the new injection mold is assigned to a cluster containing geometrically similar parts. Since the information on the robust operating point is known for each part of the database, this information is used to calculate the operating point for the new part. The applied case-based reasoning method provides the machine operator with a recommended operating point for a new injection mold without the need to carry out elaborate tests beforehand. Since the operating point of an injection molding machine consists of several parameters, the investigations were initially focused on the flow rate, which is one of the key parameters for controlling the injection phase.
Citation
In: Journal of Manufacturing Processes Volume 95 (2023) , S. 171-182 ; eissn:2212-4616Additional Information
© This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/Citation
@article{doi:10.17170/kobra-202401039319,
author={Volke, Julia and Reit, Margarita and Heim, Hans-Peter},
title={Parameter recommendation for injection molding based on similarity analysis of injection molded parts},
journal={Journal of Manufacturing Processes},
year={2023}
}
0500 Oax 0501 Text $btxt$2rdacontent 0502 Computermedien $bc$2rdacarrier 1100 2023$n2023 1500 1/eng 2050 ##0##http://hdl.handle.net/123456789/15437 3000 Volke, Julia 3010 Reit, Margarita 3010 Heim, Hans-Peter 4000 Parameter recommendation for injection molding based on similarity analysis of injection molded parts / Volke, Julia 4030 4060 Online-Ressource 4085 ##0##=u http://nbn-resolving.de/http://hdl.handle.net/123456789/15437=x R 4204 \$dAufsatz 4170 5550 {{Spritzgießen}} 5550 {{Maschinelles Lernen}} 5550 {{Ähnlichkeit}} 5550 {{Analyse}} 5550 {{Prognosemodell}} 5550 {{Fallbasiertes Schließen}} 7136 ##0##http://hdl.handle.net/123456789/15437
2024-02-06T09:04:51Z 2023 doi:10.17170/kobra-202401039319 http://hdl.handle.net/123456789/15437 © This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ eng Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ injection molding machine learning similarity analysis predictive models case-based reasoning 600 660 Parameter recommendation for injection molding based on similarity analysis of injection molded parts Aufsatz Injection molding is a widespread manufacturing process for producing complex plastic shapes. The process is controlled by a large number of parameters, which show interactions with each other. Thereby the determination of an optimal operating point during sampling is a resource-consuming and demanding task. The data, which is generated during sampling, is usually not used for subsequent sampling processes. In the context of Industry 4.0, manufacturing companies attempt to optimize production efficiency to save time and resources and remain competitive. Machine learning methods offer great potential to analyze correlations in already generated data in order to predict optimal parameters of the operating point for newly developed parts, which can only be determined with high experimental effort. The aim of the investigations is to provide a method for recommending setting parameters of the injection molding machine for new injection molds. This method is intended to make a significant contribution to reducing the elaborate tests during sampling. For this purpose, a concept was developed that, in the first step, calculates the geometric similarity of the new mold to a known database with part information from molds that have already been sampled (here: 743 parts). In the second step, the new injection mold is assigned to a cluster containing geometrically similar parts. Since the information on the robust operating point is known for each part of the database, this information is used to calculate the operating point for the new part. The applied case-based reasoning method provides the machine operator with a recommended operating point for a new injection mold without the need to carry out elaborate tests beforehand. Since the operating point of an injection molding machine consists of several parameters, the investigations were initially focused on the flow rate, which is one of the key parameters for controlling the injection phase. restricted access Volke, Julia Reit, Margarita Heim, Hans-Peter doi:10.1016/j.jmapro.2023.03.072 Spritzgießen Maschinelles Lernen Ähnlichkeit Analyse Prognosemodell Fallbasiertes Schließen acceptedVersion eissn:2212-4616 Journal of Manufacturing Processes 171-182 Volume 95 2025-06-09 2025-06-09 false
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