Knowledge Presentation based on Multi-dimension Model

for Measuring Planning in Digital Manufacturing

Xiaoqing Tang and Zhehan Chen

School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China

Keywords: Process Model, Measurement Plan, Knowledge Presentation, Digital Manufacturing.

Abstract: Digital measurement technology has been widely employed in product manufacturing process. In general, a

measuring process is planned based on human knowledge in planning strategies, measuring regulations, de-

vices and instruments, measuring operations and historical data. Knowledge-supported measuring planning

makes the process formatted, and enables manufacturers to improve product quality and reduce manufactur-

ing cost. Therefore, accumulating, presenting and modeling the measuring data, information and expertise

knowledge from engineering sectors, which can provide a foundation for discovering and reusing

knowledge of measuring process, are crucial for planning and optimizing a measurement plan. In order to

improve measurement plans based on expertise knowledge, a general measurement space (GMS) model of

measuring process is proposed. The model makes the attributes in three dimensions to describe and classify

multi-source and heterogeneous knowledge in the measuring process. The methodology for integrating and

expressing measuring process knowledge is then discussed, in order to support the storage, management and

analysis of structured knowledge data based on programs. Finally, the GMS’s characteristics matrix is con-

structed, providing a feasible way to evaluate measurement plans based on measuring process knowledge.

1 INTRODUCTION

Digital measurement technologies have been in-

creasingly and widely employed, which provide

more efficient and highly precise approaches for

inspection and quality assurance in digital manufac-

turing process, and thus have drawn significant at-

tention from manufacturers. (Jody et al., 2011;

Muelaner and Maropoulos, 2010; Du and Chen,

2011; Wang et al., 2011; Liu et al., 2013). Digital

metrologies are based on laser tracker, photogram-

metry, iGPS and other digital measurement instru-

ments; their applications not only improves the

forms of measuring and inspecting, but also brings a

new principle: measurement is not just an operation

for geometrical dimensions inspection, but becomes

the eyes of entire production process for digital data

transferring, collecting and quality assurance.

In comparison to the traditional measurement

approaches, digital metrologies have the attributes in

both progressiveness and complexity (Peggs et al.,

2009); in order to meet the requirements of meas-

urement accuracy, measuring time and total cost for

a given task, it is necessary to plan the measuring

process based on the human knowledge in planning

strategies, measurement instruments, historical data

and measuring operations, followed by making out a

measurement plan for guiding the measuring process.

The most important factor during measurement

planning is accuracy consisting of trueness and pre-

cision. Trueness reflects the systematic errors and

precision reflects the random errors (DE-DIN,

2003). For applications of digital metrologies, meas-

urement plan is significantly important for ensuring

the accuracy, validity and creditability of measuring

results that is output by the measuring process.

Measurement planning based on the historical

knowledge is aimed at ensuring the measurability of

measuring process, which is determined by a num-

ber of manifold factors involved in the process. The

relations between measurability and process factors

are the foundation of measurement plan decision,

and they are embedded in historical data of measur-

ing process. Therefore, knowledge discovery, collec-

tion and expression of measuring process are critical

for measurement planning in digital manufacturing.

However, there is a lack of research on how to inte-

grate the complex historical information from meas-

uring process, and to build up the relationship be-

tween measurability and process factors; and thus

Tang X. and Chen Z..

Knowledge Presentation based on Multi-dimension Model for Measuring Planning in Digital Manufacturing .

DOI: 10.5220/0004514100670073

In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge

Management and Information Sharing (KDIR-2013), pages 67-73

ISBN: 978-989-8565-75-4

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

measurement planning without the support of

knowledge is still non-formatted and uncertain.

In order to integrate, express and take full reuse

of historical knowledge for measurement planning, a

multi-dimensional measuring process model based

on general measurement space (GMS) is proposed; a

methodology, which is based on the GMS model, for

formally integrating and expressing different meas-

uring process knowledge is presented and discussed;

finally, the GMS’s characteristics matrix is con-

structed, providing a feasible way of evaluating and

optimizing measurement plans based on measuring

process knowledge in digital manufacturing.

2 RELATED WORKS

Digital measurement has become one of the critical

parts in product manufacturing. As a typical applica-

tion form, instruments such as laser trackers, iGPS

are largely used to measure the position and orienta-

tion of complex components during fixtures calibra-

tion and components alignment by Boeing, Airbus,

Rolls-Royce and other manufacturers (Yu et al.,

2009; Jamshidi et al., 2010; Jayaweera et al., 2010).

Widely employments accumulate a large amount of

process information for knowledge integration.

In order to optimize the measurement plan of

measuring process, the majority of the research fo-

cuses on precision analysis of digital instruments.

Jamshidi analyzed the relation between precision

and physical structure of iGPS, and compared it with

laser tracker (Muelaner et al., 2009; Maisano et al.,

2008; Maisano et al., 2009); Muelaner proposed a

mathematical model for evaluating the measurement

capability of different instruments, which is able to

support instruments selection for measurement plan-

ning (Muelaner et al., 2010); on the basis of princi-

ple analysis, Du conducted research on how the pre-

cision of iGPS measurement field is affected by its

deployment types (Du et al., 2012); Wang investi-

gated the error sources of large scale measurement

system and proposed a method for uncertainty eval-

uation (Wang et al., 2013). Their research usually

established the link between precision and parame-

ters of special instrument, but was only a part of the

measuring process and not enough for measurement

planning.

For knowledge management of measuring pro-

cess, Maropoulos firstly proposed the concept of

metrology process model; then, Chen used a meas-

urement field model with input and output to express

digital measuring process, analyzed its attributes and

discussed the evaluation method (Chen and Du,

2012); subsequently, a measurement data model

based on key measurement characteristics was given

for unifying, storing and managing the measuring

process data in product assembly (Chen et al., 2012).

In summary, the research and applications of dig-

ital metrologies are still in their infancy; the lack of

investigations on measuring process model and

knowledge integration causes that, a large amount of

historical information and knowledge of measuring

process haven’t been fully discovered, managed and

reused. Knowledge modeling and expressing of

measuring process have significances for measure-

ment planning and the development of digital me-

trologies and in manufacturing.

3 MEASURING PROCESS

MODELING

Measuring process consists of four stages: demands

analysis, process planning, data collection and result

output; in order to describe the knowledge in meas-

uring process, firstly, based on object-oriented mod-

eling method, measurement space is considered as

an object with functions of receiving demands, exe-

cuting commands and outputting result; therefore,

measuring process can be described by behaviours

of measurement space, and knowledge in the process

can be described by attributes of measurement

space.

Traditional measurement space is considered to

be a three-dimensional geometrical space; in order to

embed the manifold elements, general measurement

space (GMS) model with three dimensions and ten

attributes is proposed as the model of measuring

process, as shown in Figure 1; it integrates five key

elements in measuring process: people, machine,

material, method and environment.

Attributes of GMS are classified into three di-

mensions: 1) Physical dimension includes basic el-

ements of measuring process such as instrument,

target and environment; 2) Process dimension in-

cludes additional elements for implementing the

behaviours and functions of measuring process, such

as plan, operator and algorithm; 3) Characteristics

dimension includes key characteristics of measuring

process, such as accuracy, cost, time and range,

these characteristics are determined by attributes of

physical and process dimensions. Based on the GMS

model, measuring process can be described as: de-

mands inputting, GMS constructing, characteristics

evaluating and result outputting.

KDIR2013-InternationalConferenceonKnowledgeDiscoveryandInformationRetrieval

Figure 1: Definition of general measurement space.

3.1 Attributes in Physical Dimension

In physical dimension, instrument attribute is used to

describe information and knowledge of digital

measurements, such as laser tracker, photogramme-

try, iGPS, and laser radar and so on. Measurement

targets of GMS are usually comprised of optical

target points (OTPs) on the surfaces of different fea-

tures and structures. Environment attribute is used to

describe temperature, humidity, air pressure and

other factors that influence on the measuring pro-

cess. Contents of attributes in physical dimension

are shown in Figure 2.

3.2 Attributes in Process Dimension

Attributes of plan, operator and algorithm in process

dimension determine the operation mechanism of

GMS. Process plan file is used to describe processes,

steps of measuring process; a measuring step is

mainly comprised of geometrical features and OTPs.

Additionally, referred standards and specifications

and product model are also included in the plan file.

Operator attribute distinguishes different workers by

their work number, skills, technical levels and other

features, technical level of operator will affect the

measurement results to a certain extent. Algorithms,

such as auto measuring algorithm, abnormal point

judgment algorithm, data fusion algorithm and so

on, are called from the algorithm database; different

algorithms which implement the same function will

lead to different result. Contents of attributes in pro-

cess dimension are shown in Figure 3.

3.3 Attributes in Characteristics

Dimension

In characteristics dimension, accuracy describes the

measured systematic error and random error of any

point in GMS, systematic error of measuring process

can be removed from the final result, while random

error will be given out with the final result in the

form of uncertainty or precision. Measurement cost

mainly consists of utilization cost, deployment cost

and operating cost: 1) utilization cost can be calcu-

lated in terms of the selected measurement system’s

value and activity depreciation; 2) deployment cost

is arisen from by the setting-up and deployment of

the system in real manufacturing and assembly envi-

ronments; 3) operating cost is introduced by real

measurement operations. Measurement time is the

total time consumed in the measuring process for

completing a single measurement task. At the pre-

sent stage, measurement accuracy is the most im-

portant characteristic that has received extensive

attention during the digital measuring process.

4 KNOWLEDGE PRESENTATION

The GMS model has classified and described the

multi-source knowledge in measuring process. On

the foundation of this, it is necessary to integrate and

express knowledge in a structured form, which is

easy to be used by program for knowledge-based

reasoning, and then realizing the management and

reuse of knowledge in measuring process for meas-

urement planning.

4.1 Knowledge in Physical Dimension

Knowledge in physical dimension includes instru-

ment knowledge, target knowledge and environment

knowledge, which can be transformed to structured

information. Through analyzing their properties and

relations among those properties, an Entity-Relation

diagram is built to give the information model of

knowledge in physical dimension, as shown in Fig-

ure 4.

KnowledgePresentationbasedonMulti-dimensionModelforMeasuringPlanninginDigitalManufacturing

Figure 2: Attributes in physical dimension of GMS.

Figure 3: Attributes in process dimension of GMS.

Knowledge in

physical dimension

Instrument

database

Target

Environment

Contain

Call

Model

Manufacturer Type Number Name precison Calibration cycle Calibration date

Cost

Purchase date

Temperature

Humidity

Air pressure

time

Number

Nominal X

Nominal Y

Nominal Z Actual X Actual Y Actual Z Uncertainty Is abnormal

Is valid

Figure 4: Entity-Relation diagram of knowledge in physical dimension.

KDIR2013-InternationalConferenceonKnowledgeDiscoveryandInformationRetrieval

Figure 5: Unified information model of knowledge in process dimension.

Entity of instrument has properties of manufacturer,

model, type, number, name, precision, calibration

cycle, calibration date, cost, purchase date and oth-

ers; it is contained in both instrument database and

knowledge in physical dimension. Entity of target

has properties of number, nominal value of its coor-

dinate, actual value of its coordinate, uncertainty, is

abnormal, is valid and others, it is also contained in

knowledge in physical dimension. Entity of envi-

ronment has properties of temperature, humidity, air

pressure, time and others, it is contained in

knowledge in physical dimension; the time proper-

ties is used to record the measuring time of other

properties of environment.

4.2 Knowledge in Process Dimension

Knowledge about process plan, operator and algo-

rithm are the main contents in process dimension.

Manufacturers are usually skilled to build valid da-

tabases for storing, managing and calling resources

of their employee and algorithm, because of that

those information is easy to be transformed to struc-

tured data. By contrast, a process plan file is usually

complex and mixed document, which contains lots

of structured and non-structured information. In or-

der to unify those information in process dimension

for knowledge discovering and reusing, a unified

information model based on standards of XML, I++

and DMIS is proposed, the model uses a tree struc-

ture and embeds the information of operator and

algorithm into itself, as shown in Figure 5.

4.3 Knowledge in Characteristics

Dimension

Accuracy, cost, time and range are four main charac-

teristics of GMS, and their value determine that if

the GMS meet the requirements of measurement

task. Data of these attributes is usually continuous,

and has some uncertainty and error. Therefore, a

method is discussed as follows, for transforming

those continuous data to discrete and structured data.

Step 1: Determine the full range of characteris-

tics’ value based on historical information and

knowledge in physical and process dimensions.

Step 2: Set the threshold value of different levels

based on experience and expert scoring method, and

then give out the levels with its range of characteris-

tics’ value.

Step 3: According to the levels, mapping the ac-

tual value of characteristics from original data to

level data, and form structured knowledge in charac-

teristics dimension. The flow is depicted in Figure 6.

Figure 6: Method of transforming characteristics

knowledge to structured data.

KnowledgePresentationbasedonMulti-dimensionModelforMeasuringPlanninginDigitalManufacturing

5 KNOWLEDGE-BASED

MEASUREMENT PLANNING

To make out a reasonable measurement plan based

on historical knowledge includes three steps:

1) Construct process model and integrate process

information for knowledge discovering and storing;

2) Reveal and present the mapping relationship

between characteristics and the basic attributes in

physical and process dimensions;

3) Determine the values of transfer factors from

basic attributes to characteristics based on historical

knowledge;

4) Calculate and evaluate the measurement capa-

bility of the GMS according to the given deploy-

ments, and provide guides and optimization deci-

sions for measurement planning.

The third and fourth sections have given out a

measuring process model and structured forms of

process knowledge. Thus, in this section, a charac-

teristics relation matrix (CRM) of GMS will be dis-

cussed for expressing the relations between charac-

teristics and basic attributes.

The CRM includes two matrixes: characteristics

value matrix and characteristics weights matrix, as

shown in Figure 7. In the value matrix, element a

reflects the influence of the jth basic attribute on the

ith characteristic, while in the weights matrix, ele-

ment v

is the weights of a

in all six a

, and





(1)

The final capability of the GMS is the sum of

four characteristics, and is defined as Measurement

Capability Index (MCI), which is derived as:

(2)

Where, the factor C

is the value of the ith char-

acteristic, and the factor w

is the weights of the ith

characteristic in MCI. For different measurement

tasks, the importance rank of four characteristics

may be not the same, as a result, the value of w

will

be decided in the actual process based on the specif-

ic demands.

On the basis of the CRM, the relationship be-

tween MCI and all basic attributes can be presented

as followed:

(3)

Equation (3) provides a way for calculating the

MCI based on the historical knowledge and actual

measurement plan prior to executing the measuring

process; for calculating one of those characteristics,

it is only required to focus on the referred line of the

CRM; taking the accuracy prediction as an typical

example, the relationship between accuracy and

basic attributes can be expressed as:

(4)

The relationship expressed by Equation (4) is the

foundation of accuracy prediction. Then, it is neces-

sary to determine the weights v

and value a

for

each pair of accuracy-attribute.

Figure 7: Characteristics relation matrix of GMS.

KDIR2013-InternationalConferenceonKnowledgeDiscoveryandInformationRetrieval

6 CONCLUSIONS

Research and applications of digital measurement

technologies have stimulated the development of

digital manufacturing technologies; in the meantime,

the methods of measuring planning become critical

problems to be resolved in measuring process.

Knowledge-supported measurement planning makes

not only the process formatted, and also for manu-

facturers to improve product quality and reduce

manufacturing cost.

A measuring process model was proposed with a

definition of general measurement space. The model

has three dimensions to classify and integrate meas-

uring process knowledge. Through analyzing the

contents and forms of different knowledge, the ap-

proaches of knowledge expression and management

have been discussed. Finally, a feasible way for

evaluating and optimizing measurement plan based

on measuring process knowledge was explored.

Future work will focus on historical data collect-

ing, database constructing and evaluation of charac-

teristics relation matrix, in order to realize

knowledge-based digital measurement planning.

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KnowledgePresentationbasedonMulti-dimensionModelforMeasuringPlanninginDigitalManufacturing