Spatial and Temporal Data Alignment from Disparate Sources for Feature Association

Published

April 25, 2024

Author(s)

Benjamin Standfield, Eric Holterfield, Russell Waddell

Abstract

Among the chief topic areas of Industry 4.0, digital thread technology offers the opportunity for increased productivity, efficiency, and traceability throughout a product's lifecycle. While the basic concept of digital thread is easy to realize by specifying aggregation of data from multiple stages of an entity's life cycle, digital threads must contain internal representations and associations of data across the life cycle to bring about improved traceability features and promised productivity. In this work, we bring missing pieces into digital threads to realize future production, traceability, and efficiency-increasing capabilities within the design, planning, execution, and quality stages of discrete part manufacturing. Namely, we (1) developed a standards-based digital thread using standards commonly found in the industry, specifically STEP AP242, unstandardized NC, MTConnect, and QIF standards, (2) perform spatial alignment within the four stages to geometries of interest defined through STEP AP242 shape aspects, and (3) perform temporal alignment of execution data by identifying ISO 8061 timestamp intervals where the geometric features were actively worked on. With this work, we hope to influence ongoing standards development in the four previously mentioned standards and standardize the development of future digital threads, such as ISO 23247, by providing a standards-based methodology that allows for internal association of the data within the thread.

Citation

Grant/Contract Reports (NISTGCR) - 24-050

Report Number

24-050

NIST Pub Series

Grant/Contract Reports (NISTGCR)

Pub Type

NIST Pubs

Download Paper

https://doi.org/10.6028/NIST.GCR.24-050

Local Download

Keywords

Industry 4.0, QIF, MTConnect, STEP AP242, Digital Thread, Spatial Alignment, Temporal Alignment, Manufacturing Life Cycle.

Citation

Standfield, B. , Holterfield, E. and Waddell, R. (2024), Spatial and Temporal Data Alignment from Disparate Sources for Feature Association, Grant/Contract Reports (NISTGCR), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.GCR.24-050, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=957946 (Accessed May 6, 2024)