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Elements towards Next-Generation Knowledge Representations and Product Modeling Techniques

Planning Meeting for Product, Lifecycle Management, and Systems Engineering Models May 20, 2003 NIST • Gaithersburg MD (via telecon). Elements towards Next-Generation Knowledge Representations and Product Modeling Techniques. Russell.Peak@marc.gatech.edu http://itimes.marc.gatech.edu/

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Elements towards Next-Generation Knowledge Representations and Product Modeling Techniques

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  1. Planning Meeting for Product, Lifecycle Management, and Systems Engineering Models May 20, 2003 NIST • Gaithersburg MD (via telecon) Elements towards Next-Generation Knowledge Representations and Product Modeling Techniques Russell.Peak@marc.gatech.eduhttp://itimes.marc.gatech.edu/ http://eislab.gatech.edu/projects/

  2. Contents Purpose: Help identify comparison factorsand encourage thinking about next-generation needs • Multiple views in a knowledge representation • Declarative thinking • Object graph view of model interoperability • Some factors for comparing knowledge representations • Leveraging multiple standards • Managing computing environments via systems engineering methods • Elevated terminology & thinking See backup slides for other examples & references

  3. Contents • Multiple views in a knowledge representation • Human-sensible & computer-sensible • Graphical, lexical, application-oriented • Declarative thinking • Multi-directional (non-causal) • With derivable lower-level procedural approaches • Object graph view of model interoperability • Leveraging multiple standards • Managing computing environments via systems engineering methods • Elevated terminology & thinking Examples from Constrained Objects (COBs) & CAD-CAE Integration

  4. COB Structure: Graphical Forms Tutorial: Triangle Primitive a. Shape Schematic-S c. Constraint Schematic-S b. Relations-S Basic Constraint Schematic-S Notation d. Subsystem-S (for reuse by other COBs) Aside: This is a “usage view” in AP210 terminology (vs. the above “design views”)

  5. COB Structure (cont.): Lexical Form Tutorial: Triangle Primitive e. Lexical COB Structure (COS) COBtriangle SUBTYPE_OF geometric_shape; base, b : REAL; height, h : REAL; diagonal, d : REAL; area, A : REAL; RELATIONS r1 : "<area> == 0.5 * <base> * <height>"; r2 : "<diagonal>**2 == <base>**2 + <height>**2"; END_COB; for reference: c. Constraint Schematic-S

  6. Example COB InstanceTutorial: Triangle Primitive example 1, state 2.1 . . . state 2.1 (solved): INSTANCE_OF triangle; base : 2.0; height : 9.0; area : 9.0; diagonal : 9.22; END_INSTANCE; Constraint Schematic-I Lexical COB Instance (COI) example 1, state 1.1 state 1.0 (unsolved): INSTANCE_OF triangle; base : 2.0; height : 3.0; area : ?; diagonal : ?; END_INSTANCE; state 1.1 (solved): INSTANCE_OF triangle; base : 2.0; height : 3.0; area : 3.0; diagonal : 3.60; END_INSTANCE; Basic Constraint Schematic-I Notation

  7. Multi-Directional I/O (non-causal)Tutorial: Triangle Primitive Constraint Schematic-I Lexical COB Instance (COI) example 1, state 2.1 state 2.1 (solved): INSTANCE_OF triangle; base : 2.0; height : 9.0; area : 9.0; diagonal : 9.22; END_INSTANCE; state 3.0 (unsolved): INSTANCE_OF triangle; base : 2.0; height : ?; area : 6.0; diagonal : ?; END_INSTANCE; state 3.1 (solved): INSTANCE_OF triangle; base : 2.0; height : 6.0; area : 6.0; diagonal : 6.32; END_INSTANCE; example 1, state 3.1

  8. COBs as Building Blocks Tutorial: Triangular Prism COB Structure a. Shape Schematic-S c. Constraint Schematic-S b. Relations-S e. Lexical COB Structure (COS) d. Subsystem-S (for reuse by other COBs) COBtriangular_prism SUBTYPE_OF geometric_shape; length, l : REAL; cross-section : triangle; volume, V : REAL; RELATIONS r1 : "<volume> == <cross-section.area> * <length>"; END_COB;

  9. Example COB InstanceTutorial: Triangular Prism Constraint Schematic-I Lexical COB Instance (COI) example 1, state 1.1 state 1.0 (unsolved): INSTANCE_OF triangular_prism; cross-section.base : 2.0; cross-section.height : 3.0; length : 5.0; volume : ?; END_INSTANCE; state 1.1 (solved): INSTANCE_OF triangular_prism; cross-section.base : 2.0; cross-section.height : 3.0; cross-section.area : 3.0; length : 5.0; volume : 15.0; END_INSTANCE; Basic Constraint Schematic-I Notation

  10. COB Modeling Languages & Views Structure Level (Template) Instance Level

  11. Contents • Multiple views in a knowledge representation • Declarative thinking • Object graph view of model interoperability • Include connections with lower-level models & COTS tools • Facilitate solution management & reasoning control • Some factors for comparing knowledge representations • Leveraging multiple standards • Managing computing environments via systems engineering methods • Elevated terminology & thinking Examples from Constrained Objects (COBs) & CAD-CAE Integration

  12. Constrained Object Panorama for Multi-Fidelity CAD-CAE InteroperabilityFlap Link Benchmark Example

  13. Flexible High Diversity Design-Analysis Integration Phases 1-3 Airframe Examples:“Bike Frame” / Flap Support Inboard Beam Design Tools Modular, Reusable Template Libraries Analysis Modules (CBAMs) of Diverse Feature:Mode, & Fidelity MCAD Tools CATIA v4, v5 XaiTools Analysis Tools 1.5D General Math Mathematica In-HouseCodes Lug:Axial/Oblique; Ultimate/Shear Image API (CATGEO); VBScript Analyzable Product Model XaiTools 1.5D Fitting:Bending/Shear Materials DB FEA Elfini* MATDB-like 3D Assembly: Ultimate/FailSafe/Fatigue* Fasteners DB FASTDB-like * = Item not yet available in toolkit (all others have working examples)

  14. Usage of a COB-based Analysis TemplateCAD-CAE Interoperability during Lug Strength Analysis CAD-CAE Associativity (idealization usage) Geometry Material Models Solution Tool Interaction Boundary Condition Objects (links to other analyses) Model-based Documentation Requirements

  15. Convergence of Representations Software Development (algorithms …) Database Techniques (data structure, storage …) Flow Charts ER OMT EER STEP Express UML Constrained Object - like Representations COBs, OCL, ... Constraint graphs Objects Rules Artificial Intelligence & Knowledge-Based Techniques (structure combined with algorithms/relations/behavior)

  16. Contents • Multiple views in a knowledge representation • Declarative thinking • Object graph view of model interoperability • Include connections with lower-level models & COTS tools • Facilitate solution management & reasoning control • Some factors for comparing knowledge representations • Leveraging multiple standards • Managing computing environments via systems engineering methods • Elevated terminology & thinking

  17. Dimensions of AssociativitySome Knowledge Representation Comparison Factors Associativity = Relations among objects • Operand representation: a, b • Type: numeric, logical, string, …, general object • Human-sensible vs. computer-sensible • Computer-sensible: Flattened vs. object/feature-oriented • Other facets: security, units, uncertainty, maturity, version history, (un)known/withheld, … • Relation representation: r1, r2 • Relation type: Math formula, geometric constraint, computable algorithm, computer system (e.g., FEA tool), higher order constraint, arbitrary human process, ... r1 System Y a a System X System Z r2 b electrical circuits analogy

  18. Dimensions of Associativity (cont.) • Relation representation (continued) • Explicit vs. implicit vs. unrecognized vs. unknown • Human-sensible vs. computer-sensible • Computer-sensible: Dumb string vs. smart string vs. object/feature-oriented relation • Level: instance, template (schema, structure), adaptable template • Other facets: priority, (in)active, plus similar facets as operands • Relation directionality • Uni-directional vs. multi-directional vs. iteratively multi-directional • Relation duration • Continuous (“live”) vs. event-controlled • Relation granularity • Coarse vs. fine (macro vs. micro) • Associativity graph type • Declarative vs. procedural • Cyclic vs. acyclic • Variable vs. fixed topology

  19. Contents • Multiple views in a knowledge representation • Declarative thinking • Object graph view of model interoperability • Leveraging multiple standards • Managing computing environments via systems engineering methods • Including versioning & configuration mgt. of meta-models, standards, and tools • Elevated terminology & thinking

  20. Tool-Product Model Schema Relationships in aStandards-Based Engineering Framework Electrical CAD Tools Mechanical CAD Tools Systems Engineering Tools Eagle Pro/E Doors Traditional Tools MentorGraphics CATIA Slate … AP210 AP203, AP214 AP233 • Smart Product Model • Building Blocks • Models & meta-models • International standards • Industry specs • Corporate standards • Local customizations • Modeling technologies: • Express, UML, XML, COBs, … AP210 AP2xx XaiToolsPWA-B LKSoft, … STEP-Book AP210, SDAI-Edit, STI AP210 Viewer, ... XaiToolsPWA-B pgef EPM, LKSoft, STI, … Gap-Filling Tools PWB Stackup Tool, … Engineering Framework Tool Instance Browser/Editor

  21. Primary Technologies for Schema-based Engineering Frameworks Based on Engineering Framework Interest Group (EFWIG) emails from steve.waterbury@gsfc.nasa.gov(dated July 13, 2002 wrt PGPDM directions) and David Leal (dated November 26, 2002).

  22. Contents • Multiple views in a knowledge representation • Declarative thinking • Object graph view of model interoperability • Leveraging multiple standards • Managing computing environments via systems engineering methods • Elevated terminology & thinking

  23. Needed Shifts in Engineering Thinking Information/Knowledge-basedModeling Viewpoint Traditional Viewpoint • Math-based models of physical behavior • Learn mathematics as a modeling language • Information models of physical objects • Includes math-based models of physical behavior, but in their richer context • Learn information representation as another type of modeling language Note: Information models have their roots in modern mathematics (e.g. set theory).

  24. Needed Shifts in Engineering Thinking (cont.) Information/Knowledge-basedModeling Viewpoint Traditional Computing Viewpoint • Tool usage • Data / files • Data exchange • Translators • Single tools • Drawings & documents • Calculations • Model creation & interaction (using tools) - knowledge capture • Information models &knowledge representations (objects) • Model connection, associativity, interoperability (often via equality relations) • Interfaces • Integrated submodels • Views (submodels) connected to their richer models • Usage of model operations Objects (having structure and operations) that are interrelated.

  25. Summary Purpose: Help identify comparison factorsand encourage thinking about next-generation needs • Multiple views in a knowledge representation • Declarative thinking • Object graph view of model interoperability • Some factors for comparing knowledge representations • Leveraging multiple standards • Managing computing environments via systems engineering methods • Elevated terminology & thinking See backup slides for other examples & references

  26. Other Slides for Reference

  27. Procedural vs. Declarative Knowledge Representations Procedural Representation Traditional programming: C, C++, Java, ... Declarative Representation Math solvers: Maple, Mathematica, ... function definition: area area(base,height) return (0.5 * base * height); relation definition: r1 r1(base,height,area): area :=: 0.5 * base * height; state 1 (function usage) b := 2, h := 3; A := area(b,h); status: A := 3; state 1 (relation usage) f :=: new instance of r1(b,h,A); b=2 b A A=3 area r1 b :=: 2, h :=: 3, A :=: ?; status: A :=: 3 h=3 h b=2 A=3 A r1 b=2 state 2 (value change) h := 9; status: A := 3; A=3 h=3 intent state 2 (value change) h :=: 9; status: A :=: 9 b=2 A=9 h=9 r1 b=2 A := area(b,h); status’: A := 9; h=9 A=9 area h=9 state 3 (I/O change) A := 6; status: h := 9; state 3 (I/O change) h :=: ?, A :=: 6; status: h :=: 6 b=2 b=2 A=6 A=6 ? intent r1 h=6 h=9 h b A = 1/2 bh How does one compute h given A, b ?

  28. XML UML Objectives Contributions Chip Package Stress Analysis Template • Develop better methods of capturing engineering knowledge that : • Are independent of vendor-specific CAD/CAE/SE tools • Support both easy-to-use human-sensible views and robust computer-sensible formulations in a unified manner • Handle a diversity of product domains, simulation disciplines, solution methods, and leverage disparate vendor tools • Apply these capabilities in a variety of sponsor-relevant test scenarios: • Proposed candidates are templates and custom capabilities for design, analysis, and systems engineering To Scholarship: • Develop richer understanding of modeling (including idealizations and multiple levels of abstraction) and representation methods To Industry: • Better designs via increased analysis intensity • Increased automation and model consistency • Increased modularity and reusability • Increased corporate memory via better knowledge capture Constrained Objects: A Knowledge Representation for Design, Analysis, and Systems Engineering Interoperability Constrained Object (COB) Formulations Approach and Status Resources Needed • Approach: • Extend and apply the constrained object (COB) representation and related methodology based on positive results to date • Expand within international efforts like the OMG UML for Systems Engineering work to broaden applicability and impact • Status: • Current generation capabilities have been successfully demonstrated in diverse environments (circuit boards, electronic chip packages, airframes) with sponsors including NASA, Rockwell Collins, Shinko (a major supplier to Intel), and Boeing. • Templates for chip package thermal analysis are in production usage at Shinko with over 75% reduction in modeling effort (deformation/stress templates are soon to follow) • Support for 1-3 students depending on project scope • Sponsor involvement to provide domain knowledge and facilitate pilot usage Students: Manas Bajaj, Injoong Kim, Greg Mocko Faculty: Russell Peak COB-based Airframe Analysis Template • Additional Information: 1. http://eislab.gatech.edu/projects/ 2. Response to OMG UML for Systems Engineering RFI:http://eislab.gatech.edu/tmp/omg-se-33e/ 3. Characterizing Fine-Grained Associativity Gaps: A Preliminary Study of CAD-E Model Interoperabilityhttp://eislab.gatech.edu/pubs/conferences/2003-asme-detc-cie-peak/ Russell.Peak@marc.gatech.edu -- 2003-05-12

  29. COB-based Libraries ofAnalysis Building Blocks (ABBs) Continuum ABBs Extensional Rod Material Model ABB 1D Linear Elastic Model modular re-usage Torsional Rod

  30. Flap Link ExampleParametric Design Description Extended Constraint Graph COB Structure (COS)

  31. Representing External Tools as COB RelationsParametric FEA Model FEA Tool

  32. Constrained Object (COB) RepresentationCurrent Technical Capabilities -Generation 2 • Capabilities & features: • Various forms: computable lexical forms, graphical forms, etc. • Enables both computer automation and human comprehension • Sub/supertypes, basic aggregates, multi-fidelity objects • Multi-directionality (I/O changes) • Reuses external programs as white box relations • Advanced associativity added to COTS frameworks & wrappers • Analysis module/template applications (XAI/MRA): • Analysis template languages • Product model idealizations • Explicit associativity relations with design models & other analyses • White box reuse of existing tools (e.g., FEA, in-house codes) • Reusable, adaptable analysis building blocks • Synthesis (sizing) and verification (analysis)

  33. Constrained Objects (cont.) Representation Characteristics & Advantages - Gen. 2 • Overall characteristics • Declarative knowledge representation (non-causal) • Combining object & constraint graph techniques • COBs = (STEP EXPRESS subset) + (constraint graph concepts & views) • Advantages over traditional analysis representations • Greater solution control • Richer semantics (e.g., equations wrapped in engineering context) • Unified views of diverse capabilities (tool-independent) • Capture of reusable knowledge • Enhanced development of complex analysis models • Toolkit status (XaiTools v0.4) • Basic framework, single user-oriented, file-based

  34. An Introduction to X-Analysis Integration (XAI)Short Course Outline Part 1: Constrained Objects (COBs) Primer • Nomenclature Part 2: Multi-Representation Architecture (MRA) Primer • Analysis Integration Challenges • Overview of COB-based XAI • Ubiquitization Methodology Part 3: Example Applications • Airframe Structural Analysis (Boeing) • Circuit Board Thermomechanical Analysis (DoD: ProAM; JPL/NASA) • Chip Package Thermal Analysis (Shinko) • Summary Part 4: Advanced Topics & Current Research

  35. Techniques for Complex System Representation & Model Interoperability (CAD-CAE) http://eislab.gatech.edu/research/ a. Multi-Representation Architecture (MRA) b. Explicit Design-Analysis Associativity c. Analysis Module Creation Methodology

  36. Circuit Board Design-Analysis IntegrationElectronic Packaging Examples: PWA/B Pro AM Design Tools Modular, Reusable Template Libraries Analysis Modules (CBAMs) of Diverse Mode & Fidelity ECAD Tools Mentor Graphics, Accel* Analysis Tools XaiTools PWA-B General Math Mathematica STEP AP210‡ GenCAM**, PDIF* 1D, 2D, 3D Solder Joint Deformation* FEAAnsys PWB Stackup Tool XaiTools PWA-B Analyzable Product Model PWB Warpage XaiToolsPWA-B 1D, 2D Laminates DB PTH Deformation & Fatigue** Materials DB 1D, 2D

  37. Iterative Design & Analysis PWB Stackup Design & Warpage Analysis Pro AM 1 Oz. Cu 3 x 1080 2 Oz. Cu Tetra GF 1 Oz. Cu 2 x 2116 1 Oz. Cu Tetra GF 2 Oz. Cu 3 x 1080 1 Oz. Cu PWB Stackup Design Tool 1D Thermal Bending Model Quick Formula-based Check Layup Re-design PWB Warpage Modules Analyzable Product Model 2D Plane Strain Model Detailed FEA Check

  38. PWB Warpage Modulesa.k.a. CBAMs: COB-based analysis templates PWB Thermal Bending Model (1D formula-based CBAM) Usage of Rich Product Models APM PWB Plane Strain Model (2D FEA-based CBAM)

  39. Example Chip Package Products Source: www.shinko.co.jp Quad Flat Packs (QFPs) Plastic Ball Grid Array (PBGA) Packages Wafer Level Package (WLP) Glass-to-Metal Seals System-in-Package (SIP)

  40. Flexible High Diversity Design-Analysis Integration Electronic Packaging Examples: Chip Packages/Mounting Shinko Electric Project: Phase 1 (production usage) Design Tools Modular, Reusable Template Libraries Analysis Modules (CBAMs) of Diverse Behavior & Fidelity Prelim/APM Design Tool Analysis Tools XaiTools ChipPackage XaiTools ChipPackage General Math Mathematica FEAAnsys Thermal Resistance Analyzable Product Model 3D XaiTools PWB DB Materials DB* ThermalStress EBGA, PBGA, QFP Basic 3D** Basic Documentation Automation AuthoringMS Excel ** = Demonstration module

  41. Typical Issues: Knowledge Representation,Inter-Model Associativity (Model Interoperability) No explicit fine-grained CAD-CAE associativity little automation inconsistency little knowledge capture CAD Model bulkhead assembly attach point CAE Model channel fitting analysis material properties detailed design geometry idealized analysisgeometry analysis results

  42. Flexible High Diversity Design-Analysis Integration Phases 1-3 Airframe Examples:“Bike Frame” / Flap Support Inboard Beam Design Tools Modular, Reusable Template Libraries Analysis Modules (CBAMs) of Diverse Feature:Mode, & Fidelity MCAD Tools CATIA v4, v5 XaiTools Analysis Tools 1.5D General Math Mathematica In-HouseCodes Lug:Axial/Oblique; Ultimate/Shear Image API (CATGEO); VBScript Analyzable Product Model XaiTools 1.5D Fitting:Bending/Shear Materials DB FEA Elfini* MATDB-like 3D Assembly: Ultimate/FailSafe/Fatigue* Fasteners DB FASTDB-like * = Item not yet available in toolkit (all others have working examples)

  43. Explicit Capture of Idealizations (part-specific template adaptation in bike frame case) Idealized Features in CAE Model G2 Detailed Features/Parameters Tagged in CAD Model (CATIA) zf yf te yf yf xf xf zf b cavity3.base.minimum_thickness xf cavity3.width, w3 yf zf cavity 3 rib9 xf G1 rib8 Tension Fitting Analysis = t8,t 9 rib8.thickness rib9.thickness Often missing in today’s process Gi - Relations between idealized CAE parameters and detailed CAD parameters G1 : b = cavity3.inner_width + rib8.thickness/2 + rib9.thickness/2 G2 : te = cavity3.base.minimum_thickness

  44. Today’s Fitting Catalog Documentation from DM 6-81766 Design Manual Calculation Steps Categories of Idealized Fittings Angle Fitting Channel Fitting End Pad Bending Analysis Channel Fitting Bathtub Fitting

  45. Modular Fitting TemplatesObject-Oriented Hierarchy of Analysis Building Blocks (ABBs) + + a b f d = t min( t , t ) = = - R e R w wa wb = K f ( r , R , r , e ) p 2 = K f ( r , b , h ) 1 1 0 3 1 = K f ( t , t ) 2 e w P P = f C = f be 1 2 ht se p 2 r t e 0 e ABB: - independent of specific products - usable on many designs ABB * = Working Examples Specialized Analysis Body Specialized Analysis System Fitting Washer Body Fitting Bolt Body* Fitting Casing Body bolt washer Fitting System ABB casing load P Fitting End Pad ABB Fitting Wall ABB Open Wall Fitting Casing Body Channel Fitting Casing Body* Fitting End Pad Bending ABB Angle Fitting Casing Body Bathtub Fitting Casing Body Fitting End Pad Shear ABB* Open Wall Fitting End Pad Bending ABB Channel Fitting End Pad Bending ABB* = - C K ( 2 e t ) 1 3 b = C K K 1 1 2

  46. Channel Fitting System ABBs End Pad Bending Analysis End Pad Shear Analysis ABB = analysis building block

  47. “Bike Frame” Bulkhead Fitting Analysis TemplateUsing Constrained Object (COB) Knowledge/Info Representation = K f ( r , b , h ) 3 1 P P = f C = f be 1 2 ht se p 2 r t e 0 e

  48. Bike Frame Bulkhead Fitting AnalysisCOB-based Analysis Template - in XaiTools Focus Point of CAD-CAE Integration Detailed CAD data from CATIA Library data for materials & fasteners Idealized analysis features in APM Object-oriented spreadsheet Modular generic analysis templates (ABBs) Explicit multi-directional associativity between detailed CAD data & idealized analysis features

  49. Cost of Associativity GapsReference: http://eislab.gatech.edu/pubs/reports/EL004/ Initial Cost Estimate per Complex Product(only for manual maintenance costs of structural analysis problems) • Categories of Gap Costs • Associativity time & labor • - Manual maintenance • - Little re-use • - Lost knowledge • Inconsistencies • Limited analysis usage • - Fewer parts analyzed • - Fewer iterations per part • “Wrong” values • - Too conservative: Extra part costs and performance inefficiencies • - Too loose: Re-work, failures, law suits

  50. Information Capture Gaps:Content Coverage and Semantics Existing Tools Tool A1 Tool An ... Legend Content Coverage Gaps “dumb” information capture (only human-sensible, I.e., not computer-sensible) • Smart Product Model • Building Blocks • Models & meta-models • International standards • Industry specs • Corporate standards • Local customizations • Modeling technologies: • Express, UML, XML, COBs, … Content Semantic Gaps Example “dumb” figures

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