NISTIR 8127

End-to-End Quality Information 

Framework (QIF) Technology Survey

John Michaloski 

Tom Hedberg 

Hui Huang

Thomas Kramer

This publication is available free of charge from:

http://dx.doi.org/10.6028/NIST.IR.8127 

April 2016 
NISTIR 8127

End-to-End Quality Information 

Framework (QIF) Technology Survey

John Michaloski

Tom Hedberg

Hui Huang

Thomas Kramer

Engineering Laboratory (EL)

This publication is available free of charge from:

http://dx.doi.org/10.6028/NIST.IR.8127

April 2016 

U.S. Department of Commerce

Penny Pritzker, Secretary

National Institute of Standards and Technology 

Willie May, Under Secretary of Commerce for Standards and Technology and Director
i

Table of Contents

Notation........................................................................................................................................................1

Background ...................................................................................................................................................1

Product Lifecycle Background.......................................................................................................................2

Metrology Issues...........................................................................................................................................5

Measurement Uncertainty........................................................................................................................5

Assigning Tolerances.................................................................................................................................6

Lossless Data Exchange and Preventing Digital Tolerance Stack Up ........................................................9

Product Quality ...........................................................................................................................................10

Inspection Product Cycle ........................................................................................................................12

Quality Design.........................................................................................................................................14

Quality Planning......................................................................................................................................14

Quality Results........................................................................................................................................16

Quality Analysis.......................................................................................................................................16

Quality Information Framework (QIF) ........................................................................................................19

QIF Software Development.........................................................................................................................21

QIF Enterprise Integration ......................................................................................................................24

Summary .....................................................................................................................................................24

Disclaimer....................................................................................................................................................25

References ..................................................................................................................................................25
ii

Table of Figures

Figure 1 Product Lifecycle Steps...................................................................................................................3

Figure 2 Manufacturing Activity in Product Lifecycle ...................................................................................3

Figure 3 Ishikawa Cause Effect on Measurement Uncertainty.....................................................................6

Figure 4 Tolerance Analysis ( [18] Used with Permission)............................................................................7

Figure 5 Activities and relations within CLTE [19] (Used with Permission) . ................................................8

Figure 6 Digital Tolerance Stack Up ............................................................................................................10

Figure 7 Dimension and Shape characteristics...........................................................................................11

Figure 8 Bolt pattern...................................................................................................................................11

Figure 9a Example Block with Hole GD&T Symbols....................................................................................12

Figure 10 Manufacturing Phases of Inspection ..........................................................................................13

Figure 11 Inspection Planning.....................................................................................................................15

Figure 12 Quality Analysis and Feedback....................................................................................................18

Figure 13 QIF 2 ‘Life Saver’ Architecture ....................................................................................................20

Figure 14 QIF XML Solution Space ..............................................................................................................23
1

Notation

ANSI American National Standards Institute

CAD Computer-Aided Design

CAM Computer-Aided Manufacturing 

CAPP Computer-Aided Process Planning 

CAQ Computer-Aided Quality 

CIM Computer Integrated Manufacturing 

DOM Document Object Model

FAIR First Article Inspection Report

FEA Finite Element Analysis

GD&T Geometric Dimensioning and Tolerancing

IMTS International Manufacturing Technology Show

MBD Model-Based Design

NIST National Institute of Standards and Technology

PDF Portable Document Format

PLM Product Lifecycle Management

PMI Product and Manufacturing Information

STEP Standard for the Exchange of Product model data

QIF Quality Information Framework

SME Society of Manufacturing Engineers

XML Extensible Markup Language

XSD XML Schema

Background

Quality of a product may be defined as “its ability to fulfill the customer's needs and expectations” [1].

Quality is defined in terms of performance requirements, which vary from product to product. For discrete 

parts, the primary performance requirements, commonly referred to as characteristics, are with respect 

to dimension (e.g., length, diameter, thickness, or area), geometry (e.g., flatness, cylindricity, etc.), and 

appearance (e.g., surface finish, color, or texture). To ensure overall quality, delivered parts must meet 

the required quality characteristics. Thus, part quality is measured by its conformance to the performance 

requirements.

Intuition, “rules of thumb”, and educated guesses are ineffective ways to guarantee part quality. Quality 

goods can only be manufactured using hard knowledge derived from superior part design, part 

fabrication, and ongoing and continuous collection and evaluation of production data. Of interest in our 

work is the use of quality standards, and especially feedback through quality standards, to improve part 

design and fabrication as a consequence of the quality reporting of products.

Manufacturing systems are by their nature imperfect. Process variability can hinder manufacturers in their 

effort to maintain acceptable part quality. However, inadequate control of process variability can lead to 

scrap, rework, and repair. Maintaining close control of production quality requires measurement and 

adjustment to design and manufacturing process errors. Inspection is used to provide insight and visibility 

to potential production problems so that they can be rectified in a timely manner. Inspection is an integral 

part of manufacturing activities since it measures quality. Inspection planning determines where, when,
2

and how characteristics of a product are to be inspected. With the continued growth of product 

complexity and variety and the constant demand of reducing the product development cycle, industries 

are in search of more automated measurement operations, and integrated measurement knowledge. To 

assess quality, an actual, machined part is measured and geometric dimensioning and tolerancing (GD&T) 

characteristics are assessed by comparing the designed (commonly referred to as “nominal”) values and 

their actual values. Good parts maintain a fit and form within the specified GD&T tolerance limits. 

Tolerancing is widely used in industry to define the allowable variation of discrete parts from their ideal 

shape.

The goal of this paper is to understand how quality information characterizing the manufactured parts 

can be reported in a Extensible Markup Language (XML) standardized format. The Quality Information 

Framework (QIF) is an American National Standards Institute (ANSI) standard sponsored by the 

Dimensional Metrology Standards Consortium (DMSC) that defines an integrated set of XML information 

models to enable the effective exchange of metrology data throughout the entire manufacturing quality 

measurement process – from product design to inspection planning to execution to analysis and 

reporting. The desire is that QIF will help foster a pervasive “digital thread” throughout the product 

lifecycle contributing to feedforward and feedback flow of quality information. The hope is that 

widespread adoption of QIF will lead to better and more optimized part design and manufacturing 

processes performance.

Product Lifecycle Background

The widespread use of computers and the pervasive networking of information have revolutionized 

manufacturing. The advantage of these technologies is that manufacturers now have the opportunity for 

end-to-end dissemination and accumulation of digital information. Yet, although the field of 

manufacturing is constantly improving and adopting innovative techniques, the transition to a completely 

digital enterprise is still only partially complete. The hope is that formal standards for digital 

representation throughout the enterprise will allow feedback and feedforward of manufacturing and 

product information throughout the enterprise. These digital standards must be well-understood, 

transcend the manufacturing domain into mainstream computer technology, yet at the same time have 

industry-wide focus and acceptance or the potential benefits are lost.

Product development is a complex process involving the integration of distributed resources, such as 

human beings, engineering tools, software systems, and a large variety of product-related machines.

Figure 1 shows the basic sequential steps in the product lifecycle with no feedback consideration. Stark 

defines Product Lifecycle Management (PLM) as the business activity of managing, in the most effective 

way, a company’s products all the way across their lifecycles; from the very first idea for a product all the 

way through until it's retired and disposed [2]. 
3

Figure 1 Product Lifecycle Steps

Of interest to this paper, is the product lifecycle that focuses on the “art to part” fabrication and assembly 

of a final sellable good. Figure 2 shows a high-level abstract view of the manufacturing activity product 

lifecycle. As such, we will focus on the product realization process, which is concerned with the design, 

fabrication, assembly, and inspection of the final product. More specifically the digital thread will be 

studied as both an upstream and downstream link to impart production information. 

Figure 2 Manufacturing Activity in Product Lifecycle

Customer

Requirements

Product

Design

Prototyping

Testing

Process

Planning Production Delivery Service RemovalDisposal

Model Tolerances Analysis

Fabrication

Planning

Inspection 

Planning

Process

Planning

Idea

Design

Manufacturing Planning

Solid Model

Fabrication

Assembly

Inspection Product

Manufacturing

Process Plan Data

Inventory

Reporting

Requirements

Cost Simulation Elec/Mech

Facility

Layout

Supply

Chain
4

Product design spans an initial idea, model, refinement, and design, which requires cooperation of 

multidisciplinary design teams and the access to various engineering tools, such as computer aided design

(CAD), finite element analysis (FEA), dynamic and kinematics analysis, simulation, and optimization 

packages, and databases.

Product manufacturing covers fabrication, machining, inspection and assembly. Production requires 

machines, transfer mechanisms, and supervisory control. Product engineering requires collaborative 

multidisciplinary engineering, including: computer integrated manufacturing (CIM), computer- aided 

manufacturing (CAM), computer-aided process planning (CAPP), computer-aided quality (CAQ), 

computer-aided inspection planning (CAIP), etc.

Product development has historically been a highly sequential activity where manufacturing was 

disconnected from engineering activities. Globalization and the rapid evolving of network tools, such as 

the Internet and web-based technologies, have revolutionized the product development process.

Collaboration takes on a further importance if the manufacturing is geographically performed in different 

locations. Collaboration is possible throughout the entire product lifecycle by using lossless feedforward 

and feedback collaboration, but warrants a bi-directional digital thread.

The ability to collaborate by sharing product design and product data as well as manufacturing data among 

authorized users is now possible. With connectivity, the effective collaboration among designer, suppliers, 

and manufacturers throughout the entire product lifecycle is becoming much more important for 

continued competitiveness. The environment should not only automate individual tasks in the manner of 

traditional computer-aided engineering tools, but also mediate between individual tasks to promote 

collaboration within the context of a product design project [3]. In collaborative manufacturing, product 

lifecycle management (PLM) is recognized as a strategic business model to support collaborative creation, 

management, dissemination, and use of product assets, including data, information, knowledge, etc., 

across the extended enterprise from concept to end of life—integrating people, processes, and 

technology [4]. 

To date, two-dimensional (2D) product information has seen its capabilities far exceeded by threedimensional modeling and analysis systems, yet 2D drawings still retain a dominant role as contracting 

documentation. However, the emphasis on product information has moved drawings from 2D hard copy 

support to three dimensional (3D) digital form. Product and manufacturing information (PMI) facilitates a 

thorough 3D annotation environment that allows the association of geometrical tolerance directly to the 

3D model. Given this 3D digital thread world, manufacturing is primed to automate the connectivity from 

design to shop floor, especially the role of quality feedback in improving the design, fabrication, and 

assembly of parts. The benefits and issues of PMI and the product lifecycle with a standardized digital 

thread with PMI have been studied, but are not yet a prevalent practice [5] [6] [7] [8].

This document will give a background on product life cycle with an emphasis on the role of quality, 

introduce QIF as an XML standard representation and exchange format for part quality information, and 

then discuss the opportunities that QIF can play in a 3D digital thread world. The goal of this document is 
5

to use digital thread concepts in manufacturing for the incorporation of the QIF in the product lifecycle. 

Without doubt, quality is an important consideration, as QIF offers the following potential benefits:

1. Real-time manufacturing and especially quality data integrated into the design phase of the 

product lifecycle,

2. Lossless data feedforward from the design phase into the production quality phase.

Metrology Issues

As background, there are numerous terms used in metrology as applied to inspection measurement 

systems and measurement results. Accuracy, precision, trueness, bias, error, variability, tolerance, 

traceability, reliability, and uncertainty are each integral to understanding metrology concepts. We will 

present definitions to define the metrology concepts [9] since the terminology is confusing. These terms 

(particularly accuracy and traceability) are very often confused (and abused) in practice due to difference 

in meaning to the seller, buyer, and user of metrology instruments [10]. 

Any given measurement result is usually taken as the estimate [11]. No measurement is exact. Trueness 

is the closeness of a measurement result to the actual (true) value. Error is the difference between a 

measured value of quantity and its true value. Variability is an inherent part of measurement systems and 

measurements – no two measurements can be expected to be the same. There are two types of 

measurement error: systematic error and random error. Measurement bias is the systematic error that is 

associated with the fact that a measured value contains a consistent error. A random error is caused by 

inherently unpredictable fluctuations with the measurement device and when a measurement is repeated 

it will generally provide a measured value that is different from the previous value.

In metrology, measurement uncertainty characterizesthe dispersion of measured values to the true value. 

The accuracy of a measurement system is the degree of closeness of measurements of a quantity to the

true value. Precision is the closeness of agreement among a set of measurement results. Precision can 

also be defined with reference to a measurement number of decimal places. Clearly, representing a 

measurement with a millionth decimal place versus a tenth decimal place provides a more exacting result, 

but there is no guarantee that extra decimal places add accuracy. 

Ideally a measurement device is both accurate and precise, with measurements all close to and tightly 

clustered around the true value. The accuracy and precision of a measurement process is usually 

established by repeatedly measuring some traceable reference standard. Traceability to a widely accepted 

standard permits comparisons of measurements among users. Reliability refers to the consistency of 

accurate results over consecutive measurements over time [12].

Measurement Uncertainty

Decision rules for proving conformance or non-conformance with specifications are clearly defined by 

international standards. A component dimension must be accompanied by a tolerance [13] giving a lower 

specification limit (LSL) and an upper specification limit (USL), while a measurement result must be 

accompanied by an estimate of measurement uncertainty (U) [14]. 
6

Accuracy, properly defined as measurement uncertainty [14], is a key performance indicator for 

metrology. The accuracy of the required measurement resources are key determinant factors for the 

selection of inspection methods and technologies. There is a very wide spectrum of physical scale and 

accuracy requirements for which inspection resources need to be selected, ranging from measurement of

small parts to large, complex products. Regardless of the scale, the dimensional measurement results 

need to be accompanied by the statement of uncertainty as defined by “Guide to the Expression of 

Uncertainty in Measurement” (GUM) [14].

Process inspection is dictated by the capability of the process and inspection systems. The level of 

inspection effort is dictated by the risk of Type I or Type II errors. Depending on the industry sector, such 

risk is driven by performance, safety, and fit. The result of any measurement contains errors which 

contribute to the overall value of measurement uncertainty as shown in Figure 3.

Figure 3 Ishikawa Cause Effect on Measurement Uncertainty

The standard ISO 14253 [15] makes it clear that the onus is on the supplier of the measurement data to 

guarantee the conformance to specification (tolerance) of the measurements, and that the data takes 

account of measurement uncertainty [16].

Assigning Tolerances

Variability, uncertainty, and tolerance have to be considered within product design to assure the required 

cost, performance, quality, and reliability. Tolerance is the allowable variance from the true dimension of 

a part. The importance of assigning product tolerances effects both cost and performance.

There is a well-known adage that states “Overtolerancing costs you money.” On one hand, tight tolerances 

help assure satisfaction of the required specifications, but are more costly because the manufacturing 

process must be more precise, thusrequire more expensive manufacturing methods, more costly process, 

more steps, or more accurate fabrication machinery. On the other hand, loose tolerances may be costLogical Human Caused Mechanical

Measurement

Uncertainty

Environmental Methodological Numerical

Rounding

Error

Weak inspection

Process planning

Unnecessary 

realignments

Dust

Light

Temperature Repeated

Clamping

Geometrical

formulas

Errors in

Holding

Measurement

tools

Inconsistency of

Forces while

clamping Compensation

Errors

Uncertainty in

Measuring probes

Tool and jig

inaccuracy

Actuation

uncertainty

Humidity Numerical Stability

Translation

Errors

Excessive

Probing

Translation

Errors
7

effective, but can lead to higher failure probability during manufacturing (e.g., scrap, rework) or 

breakdown during use. 

Tighter tolerances not only contribute to cost, but add complexity which may make producibility more 

difficult. Milner et al. pointed out that “Customer requirements and overly conservative, and in some cases 

required, design margins tend to drive complexity into high-reliability systems which can adversely impact 

producibility by driving higher costs, longer manufacturing cycle times, and poor yields once the design is 

transitioned into production” [17].

Computer aided tolerancing can provide a simulation platform for modeling the effects of tolerance 

setting within a manufacturing process or assembly. Generally, tolerance analysis uses the worst-case 

maximum of dimensions and tolerances to calculate the maximum and minimum distance (clearance or 

interference) between two features or parts. Statistical tolerance analysis evaluates the maximum and 

minimum values based on some method for establishing the likelihood of obtaining the maximum and 

minimum values.

Figure 4 Tolerance Analysis ( [18] Used with Permission)

Tolerance-cost-optimization maximizes the product specifications tolerance while meeting an acceptable 

failure probability thus minimizing cost. Tolerance optimization during design has a positive impact on the 

production yields; with better yields reducing product cost and improving product quality. Tolerance-costoptimization uses either tolerance stackup analysis or a statistical tolerance analysis to compute the best 

tolerances.

When products fail to meet expectations, designers need to understand why the assigned tolerances 

failed to yield the exact part and assembly dimensions expected from manufacturing. Designers can then 

compensate for these errors in the next version of the product.

Discerning the reason for bad parts can be improved within a digital thread manufacturing environment

through the use of quality feedback to help understand the manufacturing processes and product form 

and fit issues. It would be better yet, if advice were automated to show the designer explicit flaws and 

suggest potential improvements to the product. 

Krogstie and Martinsen introduce Closed Loop Tolerance Engineering (CLTE) as a model that understands

how actors, tools, and communication flow in collaborative design between the different main activities 

Tolerance Analysis Methods

Basic approaches

• Worst case criterion or methods of 

extremes

• Root sum square (RSS) criterion or 

sample statistical method

Advanced aproaches

• Based on complexities in process 

distribution

• Based on complexities in assembly 

response function
8

in product development [19]. The CLTE model has its origin in a survey within both the design and 

manufacturing units of a manufacturing company. The CLTE model has the following activities:

• Functional Requirements –The definition of the product to satisfy the customer, including the 

definition of acceptance limits for function and a rough selection of technical concepts.

• Tolerancing - All activities that lead to the definition of “limits of specifications” of product or 

process related parameters.

• Process Capabilities - The manufacturing processes to produce the product as specified.

• Product Performance - The validation of the functional behavior of the product through physical 

testing or simulation.

Given this model feedforward and feedback relationships are developed to associate the various elements 

in product development to tolerance engineering. Figure 5 shows the feedforward and feedback 

relationships within the CLTE model.

Figure 5 Activities and relations within CLTE [19] (Used with Permission) .

Merging the tolerancing language (GD&T) with variation insight can lead to better tolerance analysis and 

more robust products. But tolerances, variation, and robustness are drawing from different sources of 

knowledge and require collaboration to share pertinent tolerance information. Figure 5 shows the 

relationships that form the basis of information distribution between activities. In the CLTE relationship 

diagram, actors can be designers, project leaders, management, manufacturing operators, test engineers, 

process engineers, foremen, etc. Tools and practices support the actors in making appropriate decisions

when working with the four main activities. The lack of tools or wrong application of them can, in the 

same way, lead to inappropriate decisions. Table 1 presents the activities involved in the differing CLTE 

relationships. Of importance to the digital thread, is the automation of quality information flow in a CLTE 

model. As a first automation attempt at CLTE, the feedback relations pointing to the left in the CLTE model 

could be automated using QIF XML to allow reuse of existing tolerance knowledge.

Functional

Requirements Tolerancing Process

Capabilities

Product

Performance

1a

1b

2a

2b

3a

3b

4a 5a

6a

4b 5b

6b
9

Table 1 CLTE Relationship Activities [19]

Relation Activities

1a How are functional requirements transformed into specifications? Which actors translate such 

requirements? 

1b Given existing limits of specifications: How are the assumptions for the tolerances stored for 

following projects?

2a For each single parameter in a product or process: How well do the specifications fit the capability 

of the process?

2b For each single parameter in a product or process: Are the critical parameters identified? Does 

capability data exist?

3a Are the critical parameters for product performance known? How are the capability data for these 

satisfactory?

3b Are sources of variation in product performance understood? Is knowledge gained in test 

departments looped back?

4a Do functional requirements lead to a pre-definition of the manufacturing process? Is the 

parameter possible to manufacture?

4b Can critical and less critical parameters be distinguished? Does capability data exist for 

critical parameters?

5a For a given product or process: Are the key parameters (and their variation) influencing the 

product performance known?

5b For a given product or process: Can variation in product performance be traced back to 

variation in key parameters?

6a Can the chosen functional requirements be met by corresponding product performance?

6b To what extent is existing knowledge from testing, simulation, and manufacturing relevant 

for following projects?

The relationship and questions posed by the CLTE model form a basis for understanding the role of quality 

in the feedback and feedforward of production knowledge represented by the manufacturing digital 

thread.

Lossless Data Exchange and Preventing Digital Tolerance Stack Up

In a complete digital thread, all transfer of information is done through the use of “lossless” digital 

representation. Generally, lossless data exchange refers to compression techniques (such as Joint 

Photographic Experts Group (JPEG), or Moving Picture Experts Group (MPEG)) but in this case refers to 

the exchange of information from one stage in the product lifecycle to another. It may be preferable from

the perspective of the Standard for the Exchange of Product model data (STEP) to use the STEP standard 

all the time for content as well as any translation. In theory, using the same representation will not result 

in any loss of information (or injection of errors). 

Translation from one design language into another imparts variability into the design due to 

heterogeneous mapping of one representation into another. If the language translations imparted zero 

variability, the information would be identical; however, this is not usually the case. Digital tolerance 

stackup refers to the loss of precision in translating from one representation language to another (CAD-

>STEP->QIF) and the requirement to increase the tolerance of the part design to compensate for the loss 

of precision.
10

For example, a translated part must maintain a given tolerance of +/- 0.05, so it must decrease its 

tolerance zone to +/- 0.04 to accommodate the potential imprecision of 0.01 due to the translation from 

language A to language B. Thus, an acceptable translation from CAD into STEP may allow a +/-0.01 

allowance around a box, which when stacked up with the translation of STEP into QIF means another loss 

of +/-0.01. In order for the part to maintain a manufacturability of +/- 0.05, it hasto decrease the tolerance 

of the original part to +/- 0.04 and then to +/-0.03 to accommodate the loss of accuracy. Stacking two 

translations means a loss of +/- 0.01 for each translation and a loss of +/- 0.02 precision total, which 

corresponds to a decrease in tolerance of +/- 0.01 for each translation. So the effect is a decrease in 

tolerance (which costs money) for each loss of precision for each translation.

Figure 6 Digital Tolerance Stack Up

There has been much activity in developing manufacturing open standards: STEP [20], JTOpen [21], and 

QIF [22]. Despite all the effort in open standards, adoption has been slow. One of the major reasons for 

this is that meaning is often lost in data translation. Correct implementation of authoring systems and 

translators using product PMI associated with a CAD model such as geometric tolerances, dimensional 

tolerances, and datum features is essential for broader industry adoption of open standards. Lipman and 

Lubell discuss an approach used at NIST for checking the conformance of STEP files to the recommended 

practice for PMI representation [23]. Until CAD and PMI can guarantee a lossless translation between 

proprietary and neutral standard representations, manufacturers will be reluctant to attempt this action.

Product Quality

Quality needs to be initially defined in terms of characteristics, which vary from product to product. To 

ensure overall quality, delivered products must adhere to quality characteristics. Product quality is 

measured by its conformance to the design requirements including dimension, tolerance, and surface 

finish. The selection of a manufacturing process to produce a quality part depends on many constituent 

factors (machine tools, cutting tools, fixtures, process plan) and is greatly based on cost and process 

capabilities. Thus for example, the ability to produce a part depends on the machining of parts that adhere 

to given tolerances. Missing part tolerances are harmful as both cost and quality suffer. Clearly, no 

manufacturing process can make a perfect part, such that, designers must specify the acceptable 

variations to determine whether an actual part is a “good” part.

Today, symbols to represent GD&T information on a part drawing have been standardized under different 

organizations, including ISO [24], as well as the ANSI Y14.5 [25] The standard GD&T symbols provide a 

+/- .05

+/- 0.05 

+/- 0 .01
11

means for specifying the shape requirements of, and the interrelationships between, part features. GD&T 

defines the nominal, as-modeled, or as-intended geometry. In GD&T, a datum is a theoretically exact 

point, axis, or plane and serves as the origin from which the tolerance characteristics of features of a part 

are established. GD&T specifications define the allowable datum variation by assigning tolerances, which 

are generally categorized into geometrical and dimensional tolerances. Geometrical tolerances are used 

to control the form, profile, orientation, location and runout of features. Dimensional tolerances are used 

to control the size and location of a feature referring to the datum. Form tolerances are applicable to 

single (individual) features or elements of a single feature; therefore, form tolerances are not related to 

datum. Form tolerance is a subset of geometric tolerances. It includes straightness, flatness, circularity, 

and cylindricity. This type of tolerance can be used for two distinctly different controls: surface control 

and control of derived median planes or derived median lines. 

Figure 7 Dimension and Shape characteristics

Since drilling holes constitutes anywhere from 60 % to 80 % of machining operations, 

we will use a bolt pattern shown in Figure 8 as an example to explain the various 

interrelated modeling GD&T elements. In three dimensions, holes are represented by 

cylindrical features typically contained within a block. Figure 9a shows a rectangular 

part with a hole at (10,5) from the part origin (left hand bottom corner), and a 0.05 

cylindricity specification defining the hole tolerance. The adjoining Figure 9b 

shows that this means that there is a tolerance zone for the cylindrical axis, and that 

three (3) circles generated from measuring points on the top/mid/bottom circles within the cylinder and 

the line generated from fitting these centers must be within 0.05 mm of the actual center line.

Surface

Texture

Geometric

characteristic

Dimensional

characteristic

Waviness Roughness Form Location Orientation Angular Linear

Characteristic

Figure 8 Bolt pattern
12

Figure 9a Example Block with Hole GD&T Symbols Figure 9b Cylindricity Tolerance fitting

Inspection Product Cycle

Given this example, there are multiple phases of inspection. Figure 10 gives a brief overview of the main 

information modeling aspects of quality and the relationship to manufacturing processes (part design and 

tolerancing, inspection planning, inspection, analysis, and reporting.)

0.05 C A B
13

Figure 10 Manufacturing Phases of Inspection

DESIGN. In the Design phase, a characteristic (e.g., cylindricity tolerance) can be associated to one or more 

part features (i.e., the hole). In our terminology, a part means the part design, and a physical instance of 

a part is called an actual component.

PLAN. Once the feature and characteristic are developed, a Plan can be undertaken to assign nominal 

measurement points for the inspection device to probe. These nominal measurement points are assigned 

to the Feature Nominal, as defined in Figure 10 as the Point2Measure box.

INSPECT. Now an inspection can be done. Inspection using a measurement device produces data for the 

Measurement Points box which contains the probe readings corresponding to the nominal point lists. This 

Measurement Points could be generated by a simulation in the case that validation of tolerance planning 

and analysis is desired.

ANALYZE. An Inspection is performed and a set of points are recorded (MeasuredPoints in Figure 10). The 

recorded inspection points are contained in the Feature Actual measured data point set. Given the 

measured points, the part can be compared to the GD&T specification to determine the goodness of the 

part.

Today, symbols to represent GD&T tolerances on a part drawing have been standardized by different 

standards organizations. The standard GD&T symbols provide a means for specifying the shape 

requirements of, and the interrelationships between, part features. Although automated inspection using 

GD&T technology has become commonplace, the solutions for the planning, programming, and reporting 

Feature(s)

Characteristic

1,,n

Plan

Points2Measure

1,,n Inspect

Measured 

Points

1to1mapping

Analyze

Feature(s)

Characteristic

1,,n

Points2Measure

1,,n

Feature(s)

Characteristic

1,,n

Measured Points

1to1mapping

Points2Measure

1,,n

Feature(s)

Characteristic

1,,n

Goodness

1to1mapping

Good/Bad

DESIGN PLAN INSPECT ANALYZE
14

of measuring data are still vendor specific, and as a result interoperability and quality results portability 

suffer. 

Quality Design

Product design spans an initial idea, model, and refinement. This process requires cooperation of 

multidisciplinary design teams and access to various engineering tools (such as CAD, FEA, dynamic and 

kinematics analysis, simulation, and optimization packages), databases, and knowledge bases. Quality 

design specifies the part quality metrics by identifying features critical to fit and function first. The output 

is a list of features associated with tolerances and datum.

Product and manufacturing information (PMI) conveys information such as GD&T, 3D annotation (text), 

surface finish, and material specifications. CAD systems that support STEP AP 242 Edition 2 [26]now have 

the capability to generate, save, or transmit tolerance information in a standard format.

The level of inspection required for any given feature is dictated by the risk of non-conformance. 

Depending on the industry sector, design risk is driven by performance, safety, cost, and fit. 

Manufacturing and inspection risks are dictated by the capability of the fabrication, inspection, and 

assembly systems. The measurement range, work volume, and precision of inspection resources dictate

the ability to handle the part tolerance defined by the PMI.

Quality Planning

Quality planning uses the features and the corresponding characteristic tolerances to assign nominal 

measurement points for the inspection device to probe (number and location of feature points to 

measure) based on type of feature and the specific characteristic tolerance. With the continued growth 

of product complexity and variety, there is a constant demand for more sophisticated planning.

Our research emphasizes the use of standards to facilitate interoperable quality information exchange.

Quality planning systems import the product model data with PMI, then import Resources and Rules 

information and generate/export Plans (whats and hows). Figure 11 shows the interplay of the various

quality components in inspection/quality planning.
15

Figure 11 Inspection Planning

• Characteristic – a quality requirement such as dimension, tolerance, finish, material, or assembly, 

used in an inspection process. The importance of GD&T has already been explained. The finish 

and material impact quality but are out of scope for this document. Figure 7 illustrates the 

hierarchical relationship of characteristics to texture, geometric, and dimensional tolerancing.

• Part Feature – defines a manufacturable feature.

• Resources – objects that perform or facilitate measurements. Clearly if a resource does not have 

the capability to measure to enough precision, then it is not a candidate resource.

o Probes – inspection of a given part requires a sensor to measure a particular feature. 

Different probes may be more suitable to different inspection features, and the matching 

of probe to feature/characteristic quality is part of the planning effort. Further, probe 

operation may be human hand-held or require a coordinate measuring machine. Also 

important when comparing probes, is measuring accuracy, precision, uncertainty, 

repeatability, and the gaging force specifications. These specifications should be matched 

to the requirements of the measuring and inspection operation.

o Instrument – may require a probe or be a gauge or coordinate measuring machine (CMM). 

Choosing the right CMM for any application requires considering several factors, 

including, the size, weight, and shape of the parts to be measured so that a CMM with 

Inspection

Planning

Inspection

Algorithms

Setup

Finish Material

Resources

Probes Instrument

Part

Features

Configuration

&

Capability

Rules

Characteristics

GD&T

Fixtures,

Etc.
16

the appropriate configuration, i.e., fixed or moving bridge, horizontal arm, cantilever, 

gantry, or portable is selected during planning.

• Rules – a specification of the type of measurement activity that should be carried out, given a 

condition context. For example, depending on the rules, an inspection device can measure 9, 15, 

or 1000 cylinder points to assess the quality. Rules are used to define the appropriate inspection 

procedures given the quality context.

• Setup – describes how the measurement components, such as part, measurement device, and 

inspection fixtures are to be physically organized.

Quality Results

Quality measurement performs part inspection, in which the actual measurement points are saved, with 

each actual point corresponding to the planned nominal point. Quality results represent parts as 

collections of production knowledge, which includes design, manufacturing, and inspection data. For 

example, a hole can be expressed with geometric design data for the hole location, diameter, and depth. 

A hole can also be associated with GD&T data to ascribe the tolerance of the hole location, diameter, and 

depth as well as relationships to other features.

Quality results provides a mechanism to record measurement data generated from one or more 

inspections in a digital (computer-readable) format for all GD&T elements. Elements include but are not 

limited to assembly information, part information, features, characteristics, nominal data, actual data 

along with traceability information that is collected during the inspection process such as operator 

identifier, and inspection environment attributes. 

To assess the quality, the measurement data for a manufactured part and the corresponding GD&T 

specifications are compared against the nominal values. Good parts maintain a fit and form within the 

specified GD&T tolerance limits. Tolerancing is widely used in industry to define the allowable variation 

of discrete parts from their ideal shape.

Often the quality of the product must include traceability reporting to satisfy regulatory compliance.

Traceability offers information about the circumstances of a quality measurement process, for example, 

the date, time, product instance, machinery, measurements, among others. 

Quality Analysis

Manufacturing systems are by their nature imperfect. Inadequate control of process variability can lead 

to scrap, rework, and repair. Assuming a stable manufacturing environment, close control of production 

quality through frequent measurement and adjustment to process errors is desirable. Quality analysis 

compares the product measured dimensions to the nominal dimensions and based on the allowed 

tolerance will report the nature of the errors, sometimes over multiple parts, associated with each 

measured feature.

A major part of quality analysis is statistical process control (SPC), which as its name suggestsis a statistical 

method of quality assessment. SPC uses techniques pioneered by Walter A. Shewhart at Bell Laboratories 

in the early 1920s [27]. SPC aims to detect quality (and by implication process) variation at any point of 
17

time. In the context of the quality life cycle, SPC uses statistical methods to analyze multiple sequential 

quality reports. SPC uses statistical methods to observe the quality of the performance of the 

manufacturing or design process (as measured by the variability) in order to detect significant variations 

before they result in sub-standard production. 

Also as the name suggests, in SPC, sufficiently large numbers of samples have to be included in the analysis 

to render statistically meaningful results. The number of samples required could depend on several 

factors, such as the desirable/required confidence/reliability levels of the SPC results, the availability of

the samples, and the time given for the SPC study.

These SPC techniques are based on measuring process output and using statistical methods to detect 

excessive variation. Results of the statistical methods are then summarized visually by control charts. SPC 

interpretation is based on assessing the variation in the studied process. It is expected that there will be 

some variation in a process. However, control chart rules should detect poor quality. It is then the onus 

of either or both the product design and manufacturing process to determine the source(s) and root cause

of the variation; and remediate the cause.

SPC uses upper and lower control limits based on statistical standard deviation (i.e., σ) distances from the 

mean. A centerline bisects the upper and lower control limits. Some of the typical SPC rules, from the 

Western Electric set of rules [28], are typified by: 1) detection of any single data point falls outside the 3σ 

limit from the centerline 2) two out of three consecutive points fall beyond the 2σ limit, on the same side 

of the centerline; 3) four out of five consecutive points fall beyond the 1σ limit, on the same side of the 

centerline; and 4) nine consecutive points fall on the same side of the centerline. SPC has other general 

performance metrics to detect erroneous error patterns (with or without associated control chart rule) 

including: 1) stratification - variation small relative to control limits; 2) mixture - tendency to avoid the 

centerline; 3) systematic - negative autocorrelation—a long series of observations that alternate highlow-high-low; 4) repetition - tendency of one chart to follow the same pattern as its predecessor; and 5) 

trends - sustained drift or shift in the long-term mean. Worst cases are that these statistical errors are 

large enough to render the manufacturing process incapable of producing the parts with required quality.

However, it is the association of bad quality to the source of the problem that can be difficult to resolve. 

Since most quality assessment uses established SPC algorithms, these techniques are for determining a 

problem, rather than resolving the problem. Further, much of the quality assessment and production

feedback is manual. Instead, the use of advanced software technologies, such as machine learning or big data 

analysis, should be able to catalog and then analyze the results to produce feedback in an end-to-end quality 

chain. Further, the use of more modern technique should be researched to see if these technologies are 

amenable to not only detecting but associating problems to root causes.

Figure 12 shows the relationship among the various components in the analysis of quality information on 

the upstream and downstream product lifecycle. It is the rejection of parts based on quality and the 

automation of the constituent steps in dealing with the remediation that provides the clear cut benefits. 

The terminology used is as follows. C&R refers to the cause and remedy. ECR refers to an engineering 

change request. PR refers to a problem report. There are some aspects of the quality information models 
18

that have been standardized, but complete standardization (and use of these electronic standards) to 

provide the necessary quality, manufacturing, and product information is not available at this time. This 

is the rationale for the mix of manual and automated modes. 

Figure 12 Quality Analysis and Feedback

Rules describe the best practices to be used in a production of the given product or assembly. Resources 

describe the hardware and software assets and processes used for manufacturing of a part. Results deal 

with recording of part measurements. Product and PMI include product definition and tolerancing 

information. Analysis deals with multiple part inspections and generally include statistics (such as those 

in SPC). Analysis is dependent on algorithms for comparisons and interpretation of the rules, resources, 

plans, and results. Acceptance of a statistical error depends on the underlying algorithms used to identify 

quality. For example, SPC control charts have some performance metrics for the evaluation of production. 

Analysis that yields non-compliance with SPC algorithms for trends, zones, and standard deviation result 

in rejection of parts. The rejection of parts leads to the cause and remediation steps. In Figure 12, Cause

and Remediation Rules are assumed to exist, in order for the cause for quality non-compliance to be 

identified. If a matching C&R is found, then a problem report is issued detailing the production deficiency, 

the cause, and remediation. A C&R is generic in nature and the C&R needs to be mapped into an ECR for 

either the engineering design or manufacturing units or both. 

Quality

Results

ANALYSIS

Causes / 

Remedies (C&R)

Definitions

End

Yes

No

Disposition 

Causes / 

Remedies

Disposition 

PRs

Generate 

ECR

Engineering C&R 

Correlations

PMI

Product

Design

Manufacturing

No

Process

Resources

Process

Rules

Accept

PRs
19

An ECR is used to describe a suggested enhancement or problem with a product. Within the context of a 

quality lifecycle, an ECR originates from detected failure within quality analysis that can be remediated by 

an engineering change. Such failures include non-conforming part and assembly tolerances, repeatable 

part deficiencies, product enhancement ideas, and other various sources. The ECR could propose

improvements with product components or assemblies, or the ECR could address manufacturing 

problems by specifying changes to the manufacturing process or equipment.

Currently, most of the remediation is achieved through the use of human knowledge, given the experience 

and understanding to interpret the quality problems. It is hoped that through the use of computer 

analysis, such as machine learning or big data techniques, the computer would understand and interpret 

quality problems and then initiate the ECR. This would imply that the computer can be taught the 

knowledge, patterns, and interpretations through viable machine learning techniques.

Quality Information Framework (QIF) 

The Quality Information Framework (QIF) is an ANSI standard sponsored by the Dimensional Metrology 

Standards Consortium (DMSC) that defines an integrated set of Extensible Markup Language (XML)

information models to enable the effective exchange of metrology data throughout the entire 

manufacturing quality measurement process – from product definition to inspection planning to 

execution to analysis and reporting. QIF handles feature-based dimensional metrology, quality 

measurement planning, first article inspection, and discrete quality measurement. QIF is gaining attention

as an important quality technology [29] [30] [31] [32] [33] [34].

The Extensible Markup Language (XML) is a metalanguage, that is, a language used to create other XML 

languages. XML is typically used for defining a language to exchange data across the internet. With XML, 

the contextual meaning of the data can be represented to create a more application-specific language.

The XML structure is a tree of elements containing branches of XML elements. An XML element is 

delimited by a start tag (ex: <ElementName>) and an end tag (</ElementName >) with CDATA being the nonmarkup character data (not XML tag related) between the tags. Elements can also have attributes, which

are name-value pairs inside start tag (e.g.,: < PartDefinition id="partDefinition1">). The structure of XML is 

fundamentally tree oriented (a graph without any cycles). In QIF, the cross linking of information is done 

using unique identifiers (as shown by the “id” attribute).

QIF is based on XML, and uses terminology and semantics from the inspection world to represent the 

various elements in the QIF specification. The QIF information models are contained in files written in the 

XML Schema Definitions (XSD). The QIF XSD Version 2.0 models consists of six application schema files 

QIFRules, QIFResults, QIFPlans, QIFProduct, QIFStatistics, and QIFMeasurementResources bundled into a 

QIF Document. QIF also includes a library of XSD schema files containing information items used by all QIF 

applications (Auxiliary, Characteristics, Expressions, Features, GenericExpressions, Geometry,

IntermediatesPMI, Primitives, PrimitivesPD, PrimitivesPMI, Statistics, Topology, Traceability, Units, and 

Visualization ). Figure 13 shows a high level perspective of the relationship among the different IF 

information models. At the core of the QIF architecture is the reusable QIF library which contains 

definitions and components that are referenced by the application areas. Around the QIF library core,
20

Figure 13 shows the six QIF application area information models, Model-Based Design (MBD) which is 

equivalent to QIFProduct, Plans, Resources, Rules, Results, and Statistics. The “QIF Execution” model is, in 

the current version of QIF, a placeholder for future standardization and is now handled by the Dimensional 

Measuring Interface Standard (DMIS) standard [35]. The order of generation of QIF data generally 

proceeds clockwise around the diagram, beginning with QIF MBD and ending with QIF Statistics. Of note, 

users of the QIF information model are not required to implement the entire model.

Figure 13 QIF 2 ‘Life Saver’ Architecture

The flow of QIF data starts with generation of CAD + PMI data exported as QIF Model Based Design (MBD)

application data. Quality planning systems import the MBD and generate Plans (whats), then import 

Resources and Rules information and export Plans (whats and hows). Programming systems import Plans 

to generate Dimensional Measurement Equipment (DME) specific programs, or general instructions to 

guide inspection. Dimensional measurement equipment executes programs and evaluates characteristics 

of a single manufactured part or assembly and exports the measurements as Results. Analysis systems, 

typically performing statistical process control, import single parts Results and generate analysis of 

multiple part batches as QIF Statistics data.

QIF is intended to handle both lossless feedforward information translation, and cater to the ability to 

provide feedback integration to the product lifecycle in a unified and universal XML format. Currently, the 

translation from a CAD model based definition into the QIF format has been approached, and commercial 

products are available and standard processes are under development. 

One goal of QIF is to satisfy the input specification requirements of GD&T. Another goal of QIF is to satisfy 

the requirements derived from output results of quality assessment standards, such as AS9100, 

specifically AP9102A – First Article Inspection Report [36]. Users of the QIF information model are not 

Model-Based

Definition

(CADz,

STEP AP242)

QIFStatistics

QIF-Results

QIFRules

Whats Hows

QIF-Plans

QIFExecution

(DMIS)

QIFResources QIF-MBD

(PDPMI)

QIF

Library

XML Schema
21

required to implement the entire model. Any of the six application models may be used singly for 

exchange of quality data between software systems.

QIF Software Development

QIF is an extensive standard and fully defines most areas of quality information, including: quality 

measurement plans, measurement results, measurement rules, measurement resources, and results 

analysis. Currently, QIF has no competing standard or representation to represent measurement results, 

so QIF can be exploited in this area and offer many benefits. Reporting, recording, and analyzing QIF 

measurement results offer some promising opportunities.

AS9102 is the Society of Manufacturing Engineers (SME) Aerospace Quality Standard, known as, First 

Article Inspection Report(FAIR). The purpose of the First Article Inspection is to provide objective evidence 

that all engineering design and specification requirements are properly understood, accounted for, 

verified, and documented. The purpose of AS9102 standard is to provide a consistent documentation 

requirement for aerospace components First Article Inspection. QIF can be used to generate AS9102

reports and specific information for filling out a FAIR may be extracted from a QIF 2.0 XML instance file.

The QIF standards process was especially committed to prove that QIF was sufficient in supplying FAIR

information. The application of QIF to produce FAIR reports was demonstrated at the 2014 International 

Manufacturing Technology Show (IMTS) [37].

In exploratory research at the National Institute of Standards and Technology (NIST), QIF has been applied 

to a variety of applications. CodeSynthesis and the Xerces XML/XSD parser have been used to develop 

several QIF software programs useful in end-to-end quality management. These include parsing the QIF 

XML into native C++ format, serializing the C++ native format into QIF XML, generating Structured Query 

Language (SQL) [38] data tables from the QIF XSD, and storing QIF XML file instances into the SQL data 

bases. Figure 14 shows the block diagram of various components that were used in QIF software 

development. This QIF software development includes:

1. First, CodeSynthesis was downloaded, installed, and then used for C++ code generation. This C++ 

code was compiled and linked with Visual Studio 2010, and the application using the 

CodeSynthesis and Document Object Model (DOM) parsing of QIF was developed. Using the QIF 

explanation of XML First Article Inspection Reporting, Xerces XPath was used to generate FAIR 

report 1, and then more specialized code was developed to read and generate FAIR Report 3. The 

C++ application based on the CodeSynthesis output the FAIR report in HyperText Markup 

Language (HTML) that if opened by Google Chrome was able to produce a clean Portable 

Document Format (PDF) output.

2. The generated C++ code was also tested by writing QIF results into the native C++ representation, 

and then serializing the results into QIF XML.

3. Generation of SQL data definition language (DDL) was done to generate SQL data base tables. This 

software development was aimed at archiving the QIF XML. Although XML is a fine exchange 

language, saving the files into a data base is imperative for archival purposes. Given that XSD 
22

defines all the types, it seemed natural to map this specification into type and table SQL DDL and 

the read the QIF XML and insert this into the database using the XSD parsing knowledge. 

SQL DDL is compatible with many open source data base providers,such as MySql and PostgreSQl. 

However, it was deemed of utmost importance that SQL tables support arrays within the fields. 

SQL has included this part of the standard since 2004, but some solutions (e.g., MySql) do not 

support arrays. The code was based on the fact that Xerces parsed the QIF XSD, however, the 

documentation describing the XSD parsing was quite limited and the representation from the 

parsing was also complicated. For this reason a symbol table was used to unwind the Xerces XSD 

parsing into a simpler format. Further, many advanced features not considered relevant to 

mapping QIF XSD to SQL DDL were ignored. QIF XSD exercised many features of XSD, but those 

XSD features that were not part of QIF were considered out of scope.

4. Generation of SQL insertion DDL was developed. The SQL DDL and QIF XML were done after Xerces 

parsed the QIF XSD and also parsed the QIF XML. At some point with a better understanding of 

the Xerces toolkit, the steps to unwind and decipher the Xerces mapping will be unnecessary. 

5. The use of Open Database Connectivity (ODBC) was used to insert the QIF XML into the 

PostgreSQL database, instead of QIF DDL. ODBC is widely supported today, with drivers available 

for most platforms and most databases. Of note, the distinction between 64 and 32 bit ODBC 

caused some headaches. The main issue is insertion of QIF/XML requiring arrays or table entries. 

These insertions require an index from another preexisting table – so these insertions had to be 

completed before the “higher” level insertion was able to complete.
23

Figure 14 QIF XML Solution Space

The saving of multiple QIF XML files was considered important, so the saving of any QIF document into an

SQL data base was developed. The open source PostgreSQL data base was selected since it supports arrays 

within SQL table fields. PostgreSQL allows columns of a table to be defined as variable-length 

multidimensional arrays. XML depends on the Element tag, which is a container object designed to store 

hierarchical data structures in memory. The element tag can have any number of child subelements and 

can be a list or array. Because of this common XML element software pattern, the mapping of a nested 

hierarchical list into an SQL array is quite straightforward.

In spite of reliance on SQL array structures, the number of SQL tables generated from the QIF schema was 

quite large (i.e., 1408). Further, although QIF XML was parsed and added to the SQL QIF data base, it is 

not clear how easily it is to access multiply derived substitution groups from the XML.

The existence of QIF alone does not guarantee success. Hopefully, helpful open source examples will assist 

manufacturers to understand the benefits of QIF and desire technology and solutions based on QIF. 

There are many open source software projects that would help industrial partners understand and use 

QIF. These include:

1. Software to translate measurement output from one or more native commercial software quality

systems into QIF 2.0 XML. Once proprietary quality measurements are translated into QIF XML, it 

QIF XSD

Xerces

XML/XSD

Parser/Serializer 

C++

Generated Code

CodeSynthesis

w/ Xerces

QIF XML

Parse w/ C++

Generated Code

FAIR 1&3

PostgreSQL

Tables

QIF XSD

C++ Software 

Development

Xerces

XML/XSD

Parser/Serializer 

PostgreSQL

Table Insertion

QIF XML

C++ Software 

Development
24

is then straightforward to bundle and transmit the output into as an MTConnect “asset” [39]. 

Once in this QIFXML format, the data could be stored into the SQL data base. 

2. The use of QIF XML to represent part features and quality inspections could be used to record 

defective part GD&T measurements that correlate to a known (or several known) causes and then 

used to train a machine learning program to identify the cause of inspection deviations.

3. Regenerate QIF XML files from the SQL data base entries.

QIF Enterprise Integration

Recently, there has been interest in applying the web-enabled, real-time networking concepts to the 

communication of quality data in a standard XML format. Historically, the representation of quality data 

has been customized for the particular phase of manufacturing, including, design tolerances, 

measurement planning, inspection, recording/reporting, traceability logging, and statistical process 

control (SPC). The proliferation of data formats in the quality lifecycle is counterproductive, and Quality 

Information Framework (QIF) unifies the disparate quality related information models. With a common 

quality model, it is now cost-effective to disseminate quality information throughout the enterprise.

Further, it is difficult to improve products and systems if they cannot be accurately measured and 

quantitatively characterized. In order to reduce costs, increase interoperability, and maximize enterprise 

integration, the MTConnect standards have been developed to “open” machine tools and factory floor 

devices for the manufacturing industry [40]. MTConnect is based upon prevalent web technology and 

provides free software development kits to minimize technical and economic barriers to MTConnect 

adoption.

Preliminary integration of the QIF quality standard and MTConnect has been done on a computer numeric 

control (CNC) system which shows the benefits of such information [39]. The lack of quality standards has 

led to time-consuming and inefficient production quality assessment with numerous opportunities for 

error and misinterpretation. Indeed, the use of MTConnect eases the integration of quality results into 

production processes with its use of XML and Internet communication web technologies. The role of 

MTConnect, inspection, and SPC was explored by Atluru and Deshpande [41]. A standalone SPC tool was

developed that communicated with an on-machine probe and analyzed the inspection data to carry out a 

statistical analysis and then distribute the real-time results via an MTConnect network.

Summary

This paper reviewed the product life cycle and the role of quality in the design and manufacture of 

products. An emphasis was placed on the “digital thread” for pervasive networking of production 

information, especially quality. Of interest was the Quality Information Framework (QIF), which is an ANSI 

standard that defines an integrated set of XML information models to enable the effective exchange of 

metrology data throughout the entire manufacturing quality measurement process – from product 

definition to inspection planning to execution to analysis and reporting. The QIF library and QIF 

applications also provide generic templates for the user to instantiate for their own implementations, thus 

enhancing the code reuse and consistency for the implementations. The ongoing efforts of integrating 
25

MTConnect provide a mechanism to further expand integrated manufacturing into the design and 

manufacturing processes.

The migration of product data management from 2D paper and drawing based to standardized digital 3D 

model based is the goal of the digital thread project at NIST [42]. It is hoped that the benefits of an alldigital pervasive format will be apparent to manufacturers, in spite of perceived detrimental 

consequences of making the transition.

Disclaimer

Commercial equipment and software, many of which are either registered or trademarked, are identified 

in order to adequately specify certain procedures. In no case does such identification imply 

recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply 

that the materials or equipment identified are necessarily the best available for the purpose.

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