In der Kleinserienproduktion ist eine 100 %-Prüfung üblich. Eine kostengünstigere Stichprobenprüfung ist nicht anwendbar, da produzierte Lose nicht die nötige Datenanzahl aufweisen. Um sie zu erhöhen und die Stichprobenprüfung anwendbar zu machen, können ähnliche Produktmerkmale mit einem Gruppierungsalgorithmus zusammengefasst werden. Darauf aufbauend wird eine Methodik zur Erstellung eines adaptiven Prüfplans für Kleinserien vorgestellt, angewendet und der weitere Forschungsbedarf dargestellt. In small series production, 100 % inspection is common practice. A more cost-effective sampling inspection is not applicable as batches produced do not provide the necessary amount of data. To increase the amount of data and allow for sampling inspection, similar product features can be grouped by a grouping algorithm. Thus, a methodology is presented for designing an adaptive sampling plan for small batch production while further research requirements are discussed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.