Browsing by Author "Otto, Kevin, Prof., University of Melbourne, Australia"

Now showing 1 - 1 of 1

QUALITY ASSURANCE THROUGH ADVANCED MANUFACTURING IN METAL CASTING FOUNDRIES
(2022) Uyan, Tekin Çağın
School of Engineering | Doctoral dissertation (article-based)
Quality control is used in metal casting foundries to identify special cause defects and root causesby correlating process input variations with casting defects. A difﬁculty exists in associating collected process data with individual cast parts as the parts are processed through the foundry and then out into the supply chain. Typically, manual identiﬁcation of the castings with route-paper based tracing approaches have been used. Such manual-based systems make root cause analysis of quality defect issues tedious, prone to error, and limited in extent of supply chain abled to be studied. This thesis aimed to provide improvements in three main problems which remain as obstacles for the industrial 4.0 transformation of the metal foundry industry which are respectively: providing understanding of the production means to form digitally readable permanent cast part markings, of foundry process data collection and traceable association with these cast parts, and of the necessary statistical quality control methods utilizing the newly available complete production data. In this thesis, a novel semi-automated approach is developed using additively manufactured 2D matrix code (AM2D) tags to be used as sand mold inserts that forms a directly cast identiﬁcation code into the parts. Furthermore, an Industry 4.0 digital foundry serial production digital tracking and process data collection of the individual cast parts is demonstrated utilizing the AM2D tags and captured part-by-part data, to enable a foundry wide root cause analysis. Finally, state of art supervised machine learning (ML) classiﬁcation models are demonstrated using the data extracted by the cloud-based system to identify conditions that predict defectives. The novel developments are demonstrated in a real foundry, manufacturing automobile wheel via an aluminum low-pressure-die-casting process. The results in this thesis indicate that AM2D tags enable automated part tracking throughout the casting process, and here uniquely at the very early operations including mold making, enabling data collected in these early operations to also be associated with each part processed. It was further conﬁrmed the tag solution worked within a foundry operation. An operator can uniquely mark, identify, and track the parts throughout the foundry. This application enables a smart foundry quality management system: simple digital tracking operation via industrial tablets or mobile phones utilizing readily available barcode reader applications. Finally, the ML modelling approach presented maps the complex relationship between adverse process conditions and defective wheel rim castings, which was helpful in assisting process parameter tuning on new product pre-series production to lower defectives. Overall, novel digital part marking, industry 4.0 part tracking and process data collection, and ML based defective classiﬁcation provides greater capability and understanding of foundry quality management.