Data-driven process analysis for iron foundries with automatic sand molding process

  • This paper proposes a methodological framework to develop a data-driven process control using pure industrial production data from a cast iron foundry, despite the limitation of complete casting traceability. The aim is to help sand foundries to produce good castings. A reference foundry, which produces mainly automotive and oven parts with automatic sand molding and pouring machines, was selected. Past data, where only good castings were produced, were extracted from the database to determine parameter control limits (upper and lower control limits) with the aid of statistical approach. To identify critical process parameters associated with casting defects, process data from the zero and high scrap production batches were systematically compared. This method clearly identified unstable parameters without exact synchronization between inline and part quality data. Molding sand moisture, temperature and compactability, liquidus temperature of the melt, phosphorus content, carbonThis paper proposes a methodological framework to develop a data-driven process control using pure industrial production data from a cast iron foundry, despite the limitation of complete casting traceability. The aim is to help sand foundries to produce good castings. A reference foundry, which produces mainly automotive and oven parts with automatic sand molding and pouring machines, was selected. Past data, where only good castings were produced, were extracted from the database to determine parameter control limits (upper and lower control limits) with the aid of statistical approach. To identify critical process parameters associated with casting defects, process data from the zero and high scrap production batches were systematically compared. This method clearly identified unstable parameters without exact synchronization between inline and part quality data. Molding sand moisture, temperature and compactability, liquidus temperature of the melt, phosphorus content, carbon equivalent and pouring temperature were found to be the critical parameters to be stabilized. Finally, a regression model for predicting and controlling of molding sand moisture and liquidus temperature of the melt was created. The determined boundaries and the models were helpful for the foundry in assisting ongoing production control and correction of process inputs to achieve target casting quality.show moreshow less

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Metadaten
Author:Chinnadit Baitiang, Konrad Weiß, Mathias Krüger, Wolfram Volk, Philipp LechnerORCiDGND
URN:urn:nbn:de:bvb:384-opus4-1091903
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/109190
ISSN:1939-5981OPAC
ISSN:2163-3193OPAC
Parent Title (English):International Journal of Metalcasting
Publisher:Springer Science and Business Media LLC
Type:Article
Language:English
Date of first Publication:2023/07/15
Publishing Institution:Universität Augsburg
Release Date:2023/11/15
Tag:Materials Chemistry; Metals and Alloys; Industrial and Manufacturing Engineering; Mechanics of Materials
Volume:18
First Page:1135
Last Page:1150
DOI:https://doi.org/10.1007/s40962-023-01080-z
Institutes:Mathematisch-Naturwissenschaftlich-Technische Fakultät
Mathematisch-Naturwissenschaftlich-Technische Fakultät / Institut für Materials Resource Management
Mathematisch-Naturwissenschaftlich-Technische Fakultät / Institut für Materials Resource Management / Juniorprofessur für Data-driven Materials Processing
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 50 Naturwissenschaften / 500 Naturwissenschaften und Mathematik
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)