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 SummaryEngineers 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 DevelopmentIn 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.

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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 IntegrationIndustry 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,

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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

VirtualPrototype

Design AnalysisOptimization

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

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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 FrameworkTraditional 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 XMLImport

PLM XMLExport

PLM XMLImport

Preprocessing

Preprocessing

CAE Pre-/Post-Processor

PLM XML

Environment

Process

Figure 2

• 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 IndustryGlobal 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

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Traditional vs. Next-Generation CAE Data Management

Figure 3

Traditional Data Management

Next GenerationCAE Data Management

Thousands ofSimulation Data

Data Reuse

ModelCAD

Pre-Process Solve Reports

ModelCAD

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.

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Components of Integrated Framework

Figure 4

GlobalDashboard

Secured Data Sharing

ManufacturingProcess Data

HPC

FE Model Data

Product LifecycleManagement

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

SolveNastran, MSC Fatigue

Reports/PlotsGeneration Animation

CAE StructureManagement

WorkflowManagement

Connector InformationManagement

PLM XML

PLM XML

MCF

CAE ANALYST

PRE-PROCESS

SOLVE

POST-PROCESS

Simulation Data Management Framework Overview

Figure 5

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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.

IntegratedSDM Framework

Knowledge ManagementRepetitive & reproducible

simulation data captured inthe framework.

EfficiencyImproved efficiency of designers & simulation engineers.

TimeReduced time to market; reducedtime to performsimulations.

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 ForwardWhile 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

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(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.

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 Teaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfaction, technology innovation, deep industry and business process expertise, and a global, collaborative workforce that embodies the future of work. With over 100 development and delivery centers worldwide and approximately 219,300 employees as of September 30, 2015, Cognizant is a member of the NASDAQ-100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among the top performing and fastest growing companies in the world. Visit us online at www.cognizant.com or follow us on Twitter: Cognizant.

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About the AuthorsMuralikrishna 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.