The benefits of automation and business process systemization are made possible with meaningful, semantic data packaged in the QIF format. With Model Based Definition (MBD) data (i.e., PMI, FT&A, etc.) becoming more commonplace, QIF is becoming an attractive complete and unambiguous MBD delivery mechanism for industrial end users. In addition to automation benefits, QIF helps to provide data traceability in this age of Big Data, where traceability is sorely needed. MBE provides a paradigm for organizing this data by mapping it all to a meaningful product definition: the master model-based definition enabled by a product data management system. QIF is designed to instantiate this MBE approach to data management.

This paper was authored by a team of DMSC Board Members and will explain the background for why QIF is needed, and the features built into QIF which will ensure that it is equipped to handle the needs of modern industry.

BACKGROUND

Today’s manufacturing enterprise is plagued with the problem of disconnected, disorganized, and duplicated data. Furthermore, recent advances have seen an exponential increase in the volume of industrial data being generated, particularly data related to computer-controlled systems of all types. Terms like “Industrial Internet of Things” (IIoT) and “Industry 4.0” describe this revolution in big data. However, fundamental issues exist that need resolution. First, the majority of data is only minimally accessible and is unable to be mapped to data from other domains of a manufacturing enterprise. For example, how does raw data collected from a sensor embedded in a jet engine gets mapped to the machine tool operation which machined the blades or the shaft of the engine? A mapping of this type would require a human-in-the-loop to carry out the association based on a tedious study of the part serial number, then a review of the records for the manufacturing process, then the machine tool reporting logs, and finally then a hand-generated mapping to the machining of the feature in question. Reliance on a human-in-the-loop presents the following issues: it is a tedious process of high cognitive load which introduces errors, it is costly, it is not an efficient use of engineer’s time, and it prevents software algorithms for automatically mining the data for meaningful patterns and anomalies. A second fundamental problem faced in the field of industrial big data is the inaccessibility of the different data formats encountered. Data is stored in a variety of file formats (e.g., PDF, TXT, TIF, CSV, XLS, STEP, JT, IGES, PRT, QIF, XML, etc.). Some of these formats are proprietary, and require costly software tools to access the data. Other formats are common for archival, but are of minimal utility for software data mining (e.g., a coordinate measuring machine (CMM) inspection report stored in PDF format). Therefore, the issue of non-robust data formats prevents industry from easily connecting troves of data to the digital thread.

THE QUALITY INFORMATION FRAMEWORK (QIF)

The Quality Information Framework (QIF) was created by a group of manufacturers, software and hardware vendors, and metrology experts within the Digital Metrology Standards Consortium (DMSC) as a response to this dilemma. QIF is a feature-based ontology of manufacturing metadata, built on XML technology, with the foundational requirement of maintaining traceability of all metadata to the “single source of truth” – the product and all its components as defined in CAD/MBD. It is an open, ANSI standard which includes support for a complete semantic derivative Model Based Definition (MBD) model, measurement planning information, and measurement results information. This characterization allows users to establish a Digital Twin by capturing the duality of aspects of manufacturing data: the as-designed product, and the as-manufactured product – including the mappings between the two aspects …

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The term “digital transformation” is used in business to describe the use of digital technology to radically improve the performance or reach of enterprises. It is, at its core, about process digitization and the strategic use of information to drive new value.

QIF (the Quality Information Framework) is an open ANSI standard for manufacturing, designed to support the digital transformation of quality inspection. QIF is a major step forward in enabling fully-digital quality inspection processes, and in supporting the strategic use of quality information throughout the enterprise and the product development process.

In this paper, we will cover:
Fundamental Challenge
Enabling Digital Quality
Digitizing Product Definition
Communicating Via the Digital Thread
Exploiting the Digital Twin
Designed for Openness
How to Get Started
Glossary of Terms

Fundamental Challenge
The fundamental challenge in transforming quality inspection into a fully digital process is interoperability.^[1]  While interoperability is often thought of as merely the ability to exchange computer processable data between different components of a system, in the case of quality inspection, this is not enough. It must be possible for data to flow seamlessly into, through, and out of the system.

If you look at a quality inspection system from a high-level perspective, it can be thought of as supporting four major activities, each implemented as a number of components:

[1] The concepts of quality inspection, and dimensional metrology (synonymous with dimensional inspection) are closely related.

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