Using Graphs to Link Data Across the Product Lifecycle for Enabling Smart Manufacturing Digital Threads

Abstract

Smart manufacturing promises to provide significant increases in productivity and effectiveness of manufacturing systems by better connecting the data from people, processes, and things. However, there is no uniform, generalized method for deploying linked-data concepts to the manufacturing domain. The literature describes and commercial vendors offer centralized data-repository solutions, but these types of approaches quickly breakdown under the intense burden of managing and reconciling all the data flowing in and out of the various repositories across the product lifecycle. In this paper, we introduce a method for linking and tracing data throughout the product lifecycle using graphs to form digital threads. We describe a prototype implementation of the method and a case study to demonstrate an information round-trip for a product assembly between the design, manufacturing, and quality domains of the product lifecycle. The expected impact from this novel, standards-based, linked-data method is the ability to use digital threads to provide data, system, and viewpoint interoperability in the deployment of smart manufacturing to realize industry’s $30 Billion annual opportunity.

Material product life cycle analysis driven by industrial internet of things

“…Hed-bergTD introduced a method of using graphics to link and track data throughout the product life cycle to form a digital thread. It also describes the prototype implementation and case studies of this method to show the round trip of product assembly information between the design, manufacturing, and quality areas of the product life cycle [2]. Satoh proposed a discrete bass model, which is a discrete simulation of the bass model.…”

“…This loss includes the internal and overall loss of the product and is indirect. Third, when the product fails, it will cause the phenomenon of production (2) x stoppage, which leads to related losses, that is, the loss of production stoppage S (3) . Regardless of the above loss, its occurrence is closely related to the degree of failure of the product, that is, different degrees of failure will produce different types of losses, and the degree of loss is also different.…”

i-Dataquest: A heterogeneous information retrieval tool using data graph for the manufacturing industry

“…Indeed, the graph theory is used in different domains to interrogate the relationships between several artefacts distributed in the enterprise throughout its life cycle (hedberg Jr, et al, 2020); to model the continuous traceability of data interconnecting objects (Kuhn & Franke, 2021), to combine event information captured with their service activity processes (Kammler, et al, 2019); to establish the cost of reconfiguring provisioning chains (Guo, et al, 2018); to target production resources from product descriptions (Beisheim, et al, 2018); to represent the relationships between parts, resources and activities in the industrial complex network (Leng & Jiang, 2019); to version engineering data from different disciplines (Mordinyi, et al, 2015) or for the representation of a semiconductor manufacturing environment including the particular typology that is the spatial representation of the different elements (Schabus & Scholz, 2017).…”

Predicting the Material Removal Rate in Chemical Mechanical Planarization Process: A Hypergraph Neural Network-Based Approach

“…This method can help operators to find potential key components and overall modification plans. A very straightforward way is transforming the working process into a knowledge graph [15] or disassembling the components as nodes in the knowledge graph [16]. More advanced, some work builds several knowledge graphs to satisfy their requirement [17].…”