Digital simulations play an increasing role in product lifecycle management (PLM) processes and simulation data management (SDM) based on the PLM XML protocol, which is a key interface with computer-aided engineering (CAE) applications. We offer a framework for aligning SDM with the overall product development process to shorten lead times and optimize output.

1. An Integrated Simulation Tool Framework
for Process Data Management
By taking a methodical approach to process data management,
automotive, aerospace, medical and discrete manufacturing companies
can create a collaborative design environment that optimizes the
product development process and accelerates time to market.
Executive Summary
Engineers worldwide use various design and ana-
lytic tools to conduct digital simulations that play
a vital role from product concept through valida-
tion. These tools perform complex simulations or
analyses and are typically integrated in a common
product information management framework to
accelerate product lifecycle management (PLM)
processes, and ensure faster and more complete
design changes.
Simulated data management can help make a
reliable design process even more reliable and
comply with global regulations, thereby providing
a virtual laboratory to meet safety standards and
resolve complex testing scenarios.
Thus, simulation data management (SDM) pro-
vides a multidisciplinary approach for evaluation,
design verification, validation, risk management
and data analysis.
This white paper elaborates the challenges faced
in traditional SDM and explains the integration of
PLM systems with computer-aided engineering
(CAE) applications using the PLM XML protocol to
support a simulation-driven product development
approach. This framework bridges the product
lifecycle with digital simulation tools interact-
ing with each other on all design changes, thus
improving quality and reducing the lead time of
simulation engineers and the costs incurred
throughout all product development phases.
The paper also shares our engagement experi-
ence in simulation data management through
an implementation at a global automotive major.
It also explains how the SDM framework can be
applied in the medical device industry.
The Need for Simulation in Product
Development
In recent years, the need to satisfy diverse con-
sumer demands has forced manufacturers to
create better, safer and greener products. This
has inevitably impacted all phases of product
development, increasing the complexity at the
design, validation and manufacturing stages. The
use and importance of simulation has been ele-
vated as a result.
• Cognizant 20-20 Insights
cognizant 20-20 insights | november 2015

2. The demand for models with more variations and
options, that satisfy tighter regulations and that
are prepared at shorter time-to-market inter-
simulation environment.
• Migrating data from a computer-aided design
(CAD) environment to a computer-aided engi-
neering (CAE) environment.
• Lost simulation process information due to
organizational attrition.
• Complicated interaction between design,
simulation and field/prototype testing
engineers due to the lack of a well-laid-out
process framework.
• Security breaches: Loss or theft due to sharing
of simulation data through mails and folders.
• Lack of a single source of truth available from
which design engineers can make decisions.
Concept of CAE-PLM Integration
Industry leaders manage various aspects of a
complex product from start to end using PLM
tools. These tools manage complex design data
and the design process across the product devel-
opment lifecycle using bill of materials (BOM) and
structure management, CAD data management
and work flow management. Similar to design
data management, the PLM and CAE worlds are
developing frameworks to align CAE processes
and data into the PLM framework.
CAE processes typically gather product data
related to simulations; this data contains infor-
mation about product structure, its CAD model,
cognizant 20-20 insights 2
Simulation-Driven Product Development Cycle
FIgure 1
The demand for
models with more
variations and options,
that satisfy tighter
regulations and
that are prepared at
shorter time-to-market
intervals drives the
need for an increased
number of credible
simulations delivered
in shorter times and at
reduced cost.
CONCEPT & DESIGN PHYSICAL PROTOTYPE
PRODUCTION
Virtual
Prototype
Design Analysis
Optimization
Simulation-Driven
Product Development
Simulation
vals drives the need for an
increased number of credible
simulations delivered in short-
er times and at reduced cost.
Among the challenges product
development engineers face in
simulation:
• Dealing with the significant
and increasing amounts
of diverse data generated
throughout various phases.
Loose simulation data
management leads to error-
prone procedures that delay
crucial design decisions.
• An uphill task in searching
for and finding relevant
data and configuring the

3. cognizant 20-20 insights 3
as well as metadata that extends to simula-
tion scenarios, and evaluation of reports. Since
the bulk of this information already resides in
PLM systems, it is beneficial for the PLM tool
to communicate this to the CAE environment to
serve downstream processes. The motivation is
to accelerate the product development process,
increase the maturity of the CAE process and
enhance the impact of simulation throughout the
product development cycle.
Independent software vendors in the simulation
industry have developed frameworks with the
aim of maintaining process flow and data in PLM,
or in their own simulation environment.
CAE PLM Integration Framework
CAE PLM integration is facilitated through
PLM XML files, which define a protocol (or set
of XML schemas and associated services) that
enable open, high-content product lifecycle
data sharing to boost PLM interoperability. It
is open, published and compliant with the
Worldwide Web Consortium (W3C) XML schema
recommendations.
The PLM XML file and associated data serve as
input to the CAE application. In turn, the CAE
application performs all required preprocessing
actions based only on the information residing
inside the PLM XML file. Finally, the CAE applica-
tion reports the result back to the PLM system
through PLM XML. Integration through this pro-
tocol is an ongoing process, which will result in
an enhanced solution for CAE model prepara-
tion within a managed design and simulation
environment.
Simulation Data Management
Framework
Traditional data management practice is to main-
tain CAE files in shared drives. A simulation data
management framework overcomes challenges
when engineers adopt traditional practices such
as finding relevant CAE data on shared drives.
The framework has three major stakeholders —
the CAE model engineer, the analyst and the
design engineer. Importantly, the framework inte-
grates the CAD, CAE and PLM tools.
• CAD data from the PLM tool is migrated to CAE
tools using a migration framework.
• Preprocess and setup of finite element model
uses automated scripts and templates and
information residing in the PLM XML file.
• Stress and durability analysis is performed
after the data is loaded.
#
2
CAE-PLM XML Framework
PLM System
PLM XML Export
PLM XML
Import
PLM XML
Export
PLM XML
Import
Preprocessing
Preprocessing
CAE Pre-/Post-Processor
PLM XML
Environment
Process
Figure 2

4. • Post-processing of output includes generation
of reports, plots and animation.
• Reports and relevant information are
integrated with PLM using the PLM XML file.
SDM Framework Challenges
Several gaps typically emerge when using this
approach, such as CAD to CAE interoperability
issues, cross-discipline (analysis) barriers and
process management issues. These gaps can be
overcome by:
• Automating data exchange between geometry
modelers, preprocessors and different analysis
disciplines.
• Coordinating data exchange capabilities
across multiple sites, including vendors.
• “Channelizing” triggers for design changes
resubmit analysis and receive analysis reports.
SDM in the Medical Device Industry
Global regulatory agencies recommend that the
medical device industry performs CAE analysis
and provide them with their reports to support
approvals. (For more on U.S. FDA recommenda-
tions on the need for simulation in the medical
device Industry refer to this draft guidance.)
Computer-aided modeling and analysis enables
device manufactur-
ers to use different
clinical trial methods
and experiments on
various test stages
in a virtual labora-
tory. It is therefore
possible for a device
or biomedical implant
manufacturer to secure
test data in its own
workspace and use it
for benchmarking, revise it based on the cus-
tomer requirements, transfer the design swiftly
from one platform (CAD) to another (CAE) using
lighter interface data communications (which
enables engineers to make design changes dig-
itally), verify and validate the changes, and
generate reports based on the feasibility studies.
SDM helps device manufacturers to overcome
cognizant 20-20 insights 4
Traditional vs. Next-Generation CAE Data Management
Figure 3
Traditional Data
Management
Next Generation
CAE Data Management
Thousands of
Simulation Data
Data Reuse
Model
CAD
Pre-
Process Solve Reports
Model
CAD
Pre-
Process Solve
Post-
Process Reports
Post-
Process
Global regulatory
agencies recommend
that the medical device
industry performs CAE
analysis and provide
them with their reports
to support approvals.

5. 5cognizant 20-20 insights
Components of Integrated Framework
Figure 4
Global
Dashboard
Secured
Data Sharing
Manufacturing
Process Data
HPC
FE Model Data
Product Lifecycle
Management
CAD Data
Reports Generation &
Animation
challenges in design and CAE processes effec-
tively, thereby:
• Offering a more collaborative design process,
thus reducing design and simulation data loss.
• Reducing CAE build time and thereby reducing
time to market and staying ahead of competitors.
• Optimizing model design conforming with
regulatory mandates.
• Recording expert knowledge and decisions for
repeatability of best practices and providing a
standardized and automated CAE process flow.
• Enabling rapid verification with virtual models
and compare with physical tests.
• Reducing cost with accelerated simulation
solutions.
PLM Environment Simulation Environment
BOM Management
CAE Model
Development
Meshing, Load
& Constraints
Solve
Nastran, MSC Fatigue
Reports/Plots
Generation Animation
CAE Structure
Management
Workflow
Management
Connector Information
Management
PLM XML
PLM XML
MCF
CAE ANALYST
PRE-
PROCESS
SOLVE
POST-
PROCESS
Simulation Data Management Framework Overview
Figure 5

6. cognizant 20-20 insights 6
Quick Take
An Automaker Shifts Simulation Gears
Business Situation
A global automotive major with design and simulation centers across the
world found it difficult to manage larger simulation projects using shared
drives and e-mail.
Challenge
Its engineers primarily ran simulations with outdated design data. There
was no standard process for model build.
Solution
The company decided to adopt a framework for SDM using PLM XML files.
Benefits
As a result of adopting the new framework, the automotive major can now:
• Manage global teams doing separate simulation functions.
• Reduce time taken to gather input data and generate simulation results.
• Redeploy engineers in crucial functions rather than in repeatable work.
Integrated
SDM Framework
Knowledge Management
Repetitive & reproducible
simulation data captured in
the framework.
Efficiency
Improved efficiency of designers
& simulation engineers.
Time
Reduced time to
market; reduced
time to perform
simulations.
Traceable
Data traceability &
data mining.
Optimize
Optimize model design
conforming to
regulatory compliance.
Collaborate
Improved collaboration
between global design &
simulation teams.
• Enabling better traceability of pre- and post-
process data across global sites.
• Offering automated big data reporting
and “single source of the truth” dashboarding.
• Providing end-to-end traceability from require-
ments through prototyping.
• Increasing accuracy of digital simulation
results using corporate test procedures.
Going Forward
While the PLM XML framework is most widely
used across industry as a PLM data exchange
standard, it is also clear that other leading inde-
pendent software vendors such as Dassault
Systemes, MSC, Altair and ANSYS also offer
frameworks to manage simulation data in their
system’s environments.
Simulation Data Management Business Benefits
Figure 6

7. cognizant 20-20 insights 7
(For further study on other PLM/CAE frameworks,
refer to this Siemens white paper.)
In today’s fast-paced product development
environment, managing simulation process chal-
lenges across the lifecycle requires more than
just shared drives and Excel sheets.
Companies that hold onto traditional methods to
handle CAE processes and data should consider
migrating to an integrated framework that can
better address simulation data and processes.
SDM builds confidence in CAE data manage-
ment among all integrated units of global product
development companies seeking a unified plat-
form for virtual product testing, verification and
validation.
Product development companies that use CAE
processes should embrace an integrated SDM
framework by:
• Choosing a solution provider with expertise in
CAE and PLM domains.
• Choosing an SDM tool based on its flexibility
and an open data model.
• Building a plan to manage the migration in a
structured fashion.
• Preparing a cultural and organization change
plan.
References
• http://www.plm.automation.siemens.com.
• http://blogs.solidworks.com/solidworksblog/wp-content/uploads/sites/2/2014/07/simflowchart.jpg.
• Davy Monticolo, Julien Badin, Samuel Gomes, Eric Bonjour, Dominique Chamoret, “A meta-model for
knowledge configuration management to support collaborative engineering,” Computers in Industry,
66, (2015) 11-20.
• Mourtzis, D., Doukas, M., Bernidaki, D., “Simulation in Manufacturing: Review and Challenges,” Procedia
CIRP, 25, (2014) 213–229.
• Simon Frederick Königs, Grischa Beier, Asmus Figge, Rainer Stark, “Traceability in Systems Engi-
neering — Review of industrial practices, state-of-the-art technologies and new research solutions,”
Advanced Engineering Informatics, 26, (4) (2012) 924-940.
• Vijay Srinivasan, “An integration framework for product lifecycle management,” Computer-Aided
Design, 43, (5) (2011) 464–478.
• Dassault Systemes (2011), Simulation Lifecycle Management, CIM Data Inc. 1-13.
• Noel Leon, “The future of computer-aided innovation,” Computers in Industry, 60, (8) (2009) 539-550.
• X.G. Ming, J.Q. Yan, X.H. Wang, S.N. Li, W.F. Lu, Q.J. Peng, Y.S. Mad, “Collaborative process planning and
manufacturing in product lifecycle management,” Computers in Industry, 59, (2-3) (2008) 154-166.
• Sebastien Charles (2006), “CAD and FEA Integration in a Simulation Data Management Environment
Based on a Knowledge Based System,” TMCE, 1719-1730.

8. About Cognizant
Cognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business process out-
sourcing services, dedicated to helping the world’s leading companies build stronger businesses. Headquartered in
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and business process expertise, and a global, collaborative workforce that embodies the future of work. With over
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About the Authors
Muralikrishna AV is a Project Manager within Cognizant’s Engineering and Manufacturing business unit.
He has 11-plus years of experience building CAD to CAE and CAD to CAM data interoperability solu-
tions, product cost management, geometric search and knowledge-based engineering solutions. Murali
has vast experience in creating frameworks for automotive design verification and validation. He has
worked with automotive, high-tech and manufacturing companies providing solutions for their data
interoperability needs. Murali has authored technical papers in the field of CAD/CAM and CAE and has
a bachelor’s degree in mechanical engineering from the College of Engineering, Guindy. He can be
reached at Muralikrishna.AV@cognizant.com.
Muthu Kumar is a Senior Associate within Cognizant’s Engineering and Manufacturing business unit.
He has 11-plus years of experience in engineering design and analysis, emerging manufacturing solu-
tions, rapid prototyping, virtual product development and CAE PLM integration, and has worked in
various domains such as process control, automotive and biomedical. Muthu is also the author of several
technical publications, and his research interests include durability and safety engineering, advanced
parametric controls and optimization. He holds a master’s degree in mechanical engineering from the
College of Engineering, Guindy, and is currently working on a doctoral program in micro-component
failure analysis and crack propagation. Muthu can be reached at Muthu.Kumarh@cognizant.com.