Abstract
AbstractThis 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.
Funder
Technische Universität München
Publisher
Springer Science and Business Media LLC
Subject
Materials Chemistry,Metals and Alloys,Industrial and Manufacturing Engineering,Mechanics of Materials
Reference28 articles.
1. A. Joshi, L.M. Jugulkar, Investigation and analysis of metal casting defects and defect reduction by using quality control tools, in Proceedings of IRF International Conference, Goa, pp. 86–91 (2014)
2. R. Sika, P. Popielarski, Methodology supporting production control in a foundry applying modern DISAMATIC molding line. MATEC Web Conf. 137, (2017). https://doi.org/10.1051/matecconf/201713705007
3. G.G. Patil, K.H. Inamdar, Prediction of casting defects through artificial neural network. Int. J. Sci. Eng. Technol. 2(5), 298–314 (2014)
4. M. Perzyk, A. Kochański, Detection of causes of casting defects assisted by artificial neural networks. J. Eng. Manuf. 217(9), 1279–1284 (2003). https://doi.org/10.1243/095440503322420205
5. N.K. Vedel-Smith, T.A. Lenau, Casting traceability with direct part marking using reconfigurable pin-type tooling based on paraffin-graphite actuators. J. Manuf. Syst. 31(2), 113–120 (2012). https://doi.org/10.1016/j.jmsy.2011.12.001
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献