Foundry Data Collection and Part Tracking Using Additively Manufactured Digital Code Direct-Part-Marking Tags

Abstract

The Smart Foundry concept promises benefits of improved foundry supply chain quality, more sustainable metal processing, and improved customer support. A significant need includes automated data gathering and visualization of the data. In metal foundries regardless of manufacturing small parts in mass production or big parts in small production, metal castings are difficult to trace individually. Furthermore, to identify causes of defects through statistical correlation of recorded process inputs to inspected part defects becomes challenging. In this paper we present a sand-casting Smart Foundry operation including automated scan-based tracking of cast parts through the foundry and supply chain. This allowed process data collected to be automatically associated with the part being processed. This study proved that additively manufactured tags can be utilized in foundry serial production operations for direct-part-marking of castings and both digital tracking and process data collection of individual cast parts. Further we made use of the captured part-by-part data to develop a root cause analysis for quality defect causal correlation. The results indicated that the casting feature dimensional quality was highly correlated with variations in sand bending strength, tin content in aluminum, and pouring time, among others. Such insights are available when tracking process and part data as part of a Smart Foundry.