Model Based Enterprise and Enabling Quality Inter ... - GPDIS

Model Based Enterprise 

and Enabling Quality 

Inter-operable Data 

Exchange 

Thomas Hedberg, P.E. 

Honeywell - Aerospace 

BOEING is a trademark of Boeing Management Company 

Copyright © 2012 Honeywell International Inc. 

Do not copy without express permission of Honeywell. 

Honeywell-ThomasHedberg-MBEandInteroperableDataExchange-DX-Open.pptx | 1

Honeywell Aerospace 

Global Product Data Interoperability Summit | 2012 

Honeywell. 

AEROSPACE FACTS 

• Headquartered in Phoenix, Ariz. 

• 38,000 employees worldwide 

• Nearly 100 worldwide manufacturing 

and service sites 

• Revenue of $10.7 billion in 2010 

STRENGTHS 

• Global leader in the aviation industry 

• Developing innovative safety products 

• Driving modernization of global air 

traffic management 

• Revolutionizing combat technology 

• Committed to improving operational 

efficiencies 





Presentation Agenda 


Honeywell. 

• Model Based Definition, Technical Data 

Package, and Product Data Quality 

• Quality Exchange in the Product Life 

Cycle 

• Gap Analysis 






Honeywell. 

A Simple Definition… 

MODEL BASED DEFINITION, 

TECHNICAL DATA PACKAGE, AND 

PRODUCT DATA QUALITY 





Typical Design Definition 


Honeywell. 





A Systems Approach: Enhancing the Model Definition 


Honeywell. 





Today’s Common TDP 


• Majority of current 

industry processes still 

deliver 2D drawings only 

Honeywell. 

• Some 3D data provided, 

but as reference only 

• In most cases of 

moderate to complex 

systems, Supply Chain 

has to create a 3D 

model from the 2D data 

to support the 

Manufacturing 

Requirements 





The TDP of Tomorrow 7 


Honeywell. 

• Information package that 

may contain any content 

and metadata necessary 

for a product to be 

defined 5 

• Contains all elements 

needed for definition of the 

product 

• Elements are components 

of the TDP that range from 

Models / Drawings to 

Specifications & 

Associated Lists 





Product Data Quality: What Is It 


Honeywell. 

• What is Data Quality 

• Data that is consistently meeting all knowledge worker and endcustomer 

expectations in all quality characteristics of the 

information products and services required to accomplish the 

enterprise mission (internal knowledge worker) or personal 

objectives (end customer) 1 

• Product Data Quality (PDQ) is a Data Model 4 

• Must have a framework to support classification of quality defect, 

measured value, tolerance, validation results, etc. 

• Product Data Quality is an element of Product Data Management 

• Predefined criteria and tolerances enable PDQ validation 

• The definitions of the criteria must be independent of any product 

data 4 





PDQ: Interoperability 


• Interoperability characterizes the ability of models to 

accurately represent objects in all environments 11 

Honeywell. 

• An interoperability error makes a model unusable when 

interfacing with various CAx tools 11 

• Error improvement is typically a manual process to reach the 

intended quality level 4 

• Repair: partial restoration for improving invalid data 

• Rework: disposing and remaking the whole data set 

• A 1999 NIST report estimates the economic cost of bad 

interoperability in the US Automotive Industry is $1.4 billion 

per year adjusted for inflation to 2012 10 





PDQ: Error Classification 


Honeywell. 

• PDQ Errors can be classified in to 2 categories 11 

• Shape Information: surface discontinuity, self-intersecting curves 

• Non-Shape Information: loss of color, attributes, and annotations 

• Shape Information Errors are critical errors because only 

shape information can be transformed between any CAx 

system using neutral formats like STEP 8 

• PDQ of Shape Information must deal with the limitations of 

interoperability of the shape information 11 





PDQ: Usage 4 


Honeywell. 

1. Product data requirements used to define and communicate the PDQ 

criterion with related tolerance and accuracy 

2. PDQ declaration for the model data 

3. PDQ verification and validation results 

4. PDQ defect information including healing 





PDQ: Data Model 


Honeywell. 

• Numerical presentation of product data is directly linked to 

product representation 4 

• Product Data Quality is synonymous to Product Quality 

• Product Data contains the shape information, features, 

GD&T, and other metadata created and used in the 

product life cycle 

• Product Data must be validated before and after exchange 

• Certification data which include the diagnostic results 

ensure the data quality of the model 11 





PDQ: Certification 11 


Honeywell. 

• The exchange of PDQ Certification data avoids the need 

for tedious diagnostic procedures 

• Certification data is also useful for modifying the product 

model data because the certification data contains the error 

information 

• Contains Information about: 

• Product Model Data 

• Diagnostics Criteria 

• Diagnostics Algorithms 

• Error Information 






Honeywell. 

Supporting the Integrated Supply Chain 

QUALITY EXCHANGE IN THE 

PRODUCT LIFE CYCLE 





The Full Model Based Lifecycle 


Collaboration Supported 

by Product Data Management 

Full Digital 

Validation 

Manufacturing 

Model Based Mfg 

Quality 

Model Based Inspect 

Honeywell. 

Engineering 

Analysis/Simulation 

Digital Design 

Tech. Pubs. 

(Interface with TDP) 

Customer Support 

MRO 

= Requirement 

= Verify & Validate 






Tour: Digital Design / MBd 2 


Honeywell. 

• Engineering 

develops a model 

based definition of a 

part 

• Annotations that 

visually display 

design intent 

included (i.e. 

dimensions, GD&T, 

datums, etc.) 

An “Intelligent Model” 





Tour: Digital Validation 2 


Full Digital Validation 

Honeywell. 

• Technical Validators 

can save time by 

focusing on technical 

checks 

• Resultant model has 

reduced variation of 

standards 

application 

• PDQ Certification 

data ensures the 

model and derivative 

neutral data can be 

trusted by 

downstream users 





The Exchange to the External Supply Chain 


Collaboration Supported 

by Product Data Management 

Manufacturing 

Quality 


Honeywell. 


Full Digital 

Validation 

Engineering 

Analysis/Simulation 


Tech. Pubs. 

(Interface with TDP) 

Customer Support 

MRO 

= Requirement 

= Verify & Validate 





Tour: MBm & MBi 2 




Honeywell. 

• Off machine Programming 

and other activities consume 

the model Full re-use of 

model and semantic 

annotations 

• Integrated solution manages 

changes more efficiently and 

ensures production ready 

programs 

• PDQ Certification data 

guarantees an increase in 

quality while reducing cycletime 

and risk by setting a 

model quality baseline 






Honeywell. 

Strengths and Weaknesses 

GAP ANALYSIS 





Format Standards 3 : Too Many to Support 


Honeywell. 

Mechanical Design 

1 Primary Standards Organization 

Converging on STEP AP242 & AP209 

Electrical Design 

Multiple Organizations 

Major Vendor Influence 

STEP 

AP203 

STEP 

AP214 

ANSI/I 

CP- 

NC-349 

EDIF 

RS 274 

(multiples) 

GerberP 

IPC 

D-350 

FIRE 

9000 

STEP 

AP210 

FAB 

IPC 

(multiples) 

D-356/A 

STEP 

AP242 

STEP 

AP209 

ELOG 

IDF & 

IDX 

PDF 

Verilog 

ODB++ 

Custom 

XML & 

TXT 

VHDL 





Process Standards: Need Them Now with Support 


• Standards Needed to Understand… 

…Which Type of Formats to Use When 

…How To Present Information 

…How to Interpret Information 

Honeywell. 

• ASME Y14.41 (Digital Product Definition), MIL-STD-31000 

(Standard Practice: Technical Data Package), NAS 9300 

(LOTAR) 

• Three examples where standards have been released or soon to be 

released and more support needed from solution providers 

• More Direction from Industry Needed 

• With Funding 

• Support of Transferring Knowledge vs. Information 

• Communication and Participation 





PDQ Criteria Definition 


• PDQ criteria can be released prior to any design activities 

• PDQ criteria is product data independent; the criteria becomes the 

product data’s quality level requirement 

• Mitigates the risk of data quality defects and economic loss from 

repair & rework 4 

• Need common manufacturing industries criteria 

• PDQ criteria varies from industry to industry 11 

• Criteria needs to be flexible and easily extendable from industry to 

other industries 4 

• Current PDQ criteria definition is ambiguous 

• Most criteria are defined in plain language; ambiguity exists in 

measurement methods 11 

• Industry and academic technical reports have suggested criteria 

described in a formal and programming language 

Honeywell. 





PDQ: Industry & Vendor Support 


Honeywell. 

• Industry has started to take notice that PDQ information is 

useful for long term archiving and retrieval 

• PDQ information and certification data will guarantee the consistency 

of the archived and retrieved data 4 

• Significant amounts of research has been conducted in the 

area of identifying defects and repairing them 4 

• More research is needed in the area of the root causes of errors 

• National Institute of Science and Technology (NIST) is ramping up 

further research in the area of PDQ 

• Commercial tools are available, but algorithms are proprietary 

and closed 

• Closed algorithms cannot have their accuracy independently verified 11 





PDQ: Standards 


Honeywell. 

• More investigation and research is needed in PDQ 

standards such as ISO 10303-59 (STEP Part 59) 9 

• The STEP (ISO 10303) standard provides the ability to integrate 

resource models into an application protocol 8 

• STEP Part 59 tries to create a limited open framework and 

scheme for managing the quality of product data 8 

• Currently, Part 59 generally deals with shape information 8 

• For MBE to gain more general acceptance within industry, 

STEP Part 59 will need to comprehensively address shape 

and non-shape information 





Collaborative Product Development 


Honeywell. 

• Highly effective environment where PEOPLE from different 

ORGANIZATIONS can rapidly collaborate around sets of 

DATA 6 

The MBD, TDP, & PDQ 

strategies are Enablers 

to the CPD Environment 








Exchange 







References 


Honeywell. 

[1] International Association for Information and Data Quality. Iq/dq glossary. Web Site, June 2010. 

[2] Thomas Hedberg, Karen Kontos, and John Russell. Engineering collaboration in a cad neutral world. In Global Product Data 

Interoperability Summit, November 2011. 

[3] Thomas Hedberg, Karen Kontos, and John Russell. Creating the intelligent nx model: Driving engineering design, development, and 

delivery. In Siemens PLM Connection 2012, May 2012. 

[4] Yoshihito et. al. Kikuchi. Pdq (product data quality): Representation of data quality for product data and specifically for shape data. 

Journal of computing and information science in engineering, January 2010. 

[5] Joshua Lubell. Metadata for long term preservation of product data. In Proceedings of International Symposium on XML for the Long 

Haul: Issues in the Long-term Preservation of XML, August 2010. 

[6] X.G. Ming and Wen Feng Lu. A framework of implementation of collaborative product service in virtual enterprise. Innovation in 

Manufacturing Systems and Technology, January 2003. 

[7] US Department of Defense. Standard practice: Technical data packages, November 2009. MIL-STD-31000. 

[8] International Standard Organization. Industrial automation systems and integration – product data representation and exchange – part 

1: Overview and fundamental principles, 1994. ISO 10303-1:1994. 

[9] International Standard Organization. Industrial automation systems and integration – product data representation and exchange – part 

59: Integrated generic resource – quality of product shape data, May 2008. ISO 10303-59:2008. 

[10] NIST Strategic Planning and Economic Assessment Office. Interoperability cost analysis of the us automotive supply chain. Planning 

Report 99-1, National Institute of Standards and Technology, 1999. 

[11] Fumiki Tanaka and Takeshi Kishinami. Step-based quality diagnosis of shape data of product models for collaborative e-engineering. 

Computers in Industry, 57(3):245 – 260, 2006. 








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