ADOPTING DIGITAL TWIN FOR INTEGRATED DESIGN AND MANUFACTURING TO REDUCE PROCESS TIME

Nathan W. Hartman, Ed.D. Dauch Family Professor of Advanced Manufacturing and Department Head Director, Digital Enterprise Center Co-Director, Indiana Manufacturing Compe@@veness Center Jan-Anders Mansson, Ph.D. Dis@nguished Professor of Materials & Chemical Engineering Director, Manufacturing Design Laboratory Co-Director, Indiana Manufacturing Compe@@veness Center ADOPTING DIGITAL TWIN FOR INTEGRATED DESIGN AND MANUFACTURING TO REDUCE PROCESS TIME

What is a digital enterprise? A digital enterprise changes the way people work and how they use information Human/ machine interface Cybersecurity layers Loca=on detec=on technologies Digital Product DefiniHon Customer data capture Mobile technologies Cloud compu=ng Sensors and data gathering Addi=ve and tradi=onal manufacturing

Ongoing industrial challenges Driving product lifecycle data with high fidelity representa=ons Increasing product complexity Difficult to hire new workers with requisite knowledge Securing digital product and process data through the enterprise Supply chain transforma=on Mobility, Collabora=on, and Interfaces

A challenge for manufacturing

The next industrial revolution Mechanization, mass production, automation, virtualization hnp://saphanatutorial.com/industry-4-0/ Digitaliza=on and connec=vity

PLM a key element to a digital enterprise Singularity unique informa=on Correspondence 1:1 rela=onship Cohesion follow physics Traceability capture changes over =me Reflec=vity capture the state changes Cued Availability informa=on on demand, in context

The collaboration journey Yesterday Communica=ons oxen in serial fashion Collabora=on meant face-toface communica=on You trusted the data because you trusted the person that generated the data

The old communications medium The paper thread

What should go into a model-based definition? Implicit and explicit information must be included Historically, drawings contained both implicit and explicit informa=on. Context was important for understanding. However, CAD tools require explicit defini=on of informa=on.

Shape definition and visual clarity Many people simply use annotated CAD models as a proxy for a drawing Geometry defini=on Dimensional informa=on Design intent clarity Explicit informa=on

But our technology sometimes fails us The digital product defini=on forms the core of how product informa=on is moved through this sociotechnical system. However, s=ll sequen=al Dynamic model re-purposing s=ll lacking MBD must move beyond shape Lifecycle loop s=ll not connected LOTAR Service/MRO Manufacturing Models Analysis Models Needs HW/SW Design Model Physical Alloca=on Model Requirements Func=onal Alloca=on Model

And if this is to become reality Tomorrow The 3D digital definihon becomes the conduit in a standards-based communica=on process. The product model is the basis for a secure, authoritahve source of product defini=on. Recycle Service Manufacturing Design You come to trust the process that generates product data (because the person may be unknown).

We must have a more complete communication A complete MBD supports lifecycle communication SHAPE BEHAVIOR CONTEXT HUMAN TO HUMAN MACHINE TO HUMAN HUMAN TO MACHINE MACHINE TO MACHINE

How is the model structured? Singular representation vs. multiple, connected representations Singular Representa=on Context Behavior Shape OR Mul=ple Connected Representa=ons Shape geometry topology logic constraints For many people, it is a maner of whether they are an author or a consumer. MBD is fundamental to the future of digital manufacturing, but it is more than a proxy for a drawing. Behavior materials process dim./tol. physics Context assembly machining in use re=rement

Our representations must match our context The evolution of representations Geometry Behavior CAD based Drawing based Context Lifecycle based Virtual environment based MBx based Specifica=ons Interface requirements System design Behavior Analysis & Trade-off Structure Test plans Requirements ATC Authorize Direct taxiway Pilot Request to proceed Initiate power-up Report Status Initiate Taxi Airplane Power-up Executed cmds Diagram based Document based

And we must capture behavioral intentionality Physics-based modeling Through surrogate meta-models create tools that can be used to inform decisions, in real =me, for shop floor use.

So we can track information flow Design Flow Manufacturing Flow Seam Layer Rolls Stack Layers Processing Parameters: Layer Orientations à Seaming Time à TP Tape Usage Seam Layer Rolls Tape Seamer Stack Layers Processing Parameters: Layer Orientations à Seaming Time à TP Tape Usage Consolidate Layered Preform GLOBE Pre-form Manufacturing Process Tape Seamer Consolidate Layered Preform GLOBE Pre-form Manufacturing Process Cut to Part Bounding Box Dimensions Cut to Part Bounding Box Dimensions Output: 48in x 48in blank Layup / Consolidation Press Preform Transfer Transfer to storage Transfer Net Shape Preform Finished Part Transfer to storage Net-Shape Trimming Post-processing Final Trimming Net-Shape Trimming Scrap Post-processing Final Trimming Scrap Transfer Preheat Preform Intermediate Processing Transfer Transfer Processing Parameters: Pre-heat Temp à Oven Time Press Force à Press Cost Material Parameters: Fiber Volume, Polymer Volume, à Material Costs Processing Parameters: Injection Temperature, Injection Pressure: à Mold/Press/Utilities Fill Time, Pack & Cool Time à Cycle Time Transfer / Clean Mold Preheat Preform Intermediate Processing Transfer Processing Parameters: Pre-heat Temp à Oven Time Press Force à Press Cost Form Preform & Processing Parameters: Injection Injection Temperature, Mold Injection Pressure: à Mold/Press/Utilities Fill Time, Pack & Cool Time à Cycle Time Transfer / Clean Mold Transfer Demol d Transfer Demol d Load Preform into Mold Close / Press Cycle Evacuat e Mold Injection Filling Packing & Cooling Open Mold Load Preform into Mold Close / Press Cycle Evacuat e Mold Injection Filling Packing & Cooling Open Mold Injection Molding Process Injection Molding Process

And connect our digital twin to our physical world Cost Contribu=ons ($/part) $80 $70 $60 $50 $40 $30 $20 $10 $0 Tape Slitter MultiLayer Consolidation Press Output: 48in x 48in blank Net Shape Preform Finished Part Total Labor ($/p) Total Material ($/p) Equipment Opera=on ($/p) Cycle =me (min) Scrap Scrap Form Preform & Injection Mold 25 20 15 10 5 0 Cycle Time (minutes)

Through a connected infrastructure. PRODUCTION TRACKER COST & TIME TRACKER

The digital enterprise supply chain Leveraging supplier and process data to ensure capacity User Interfaces Intelligent machines Analy=cs and Interfaces Geometry Costs Finishing Materials Produc=on Plans Digital Product Data Distributed sensing SoXware Connec=vity Data Warehouse Collec=on and Integra=on of Data Customer Feedback Close the loop Valida=on and Tes=ng/QC Digital valida=on & verifica=on Accuracy and fidelity Fleet Management and U=liza=on Delivery verifica=on Monitoring and adjustment Produc=on Floor Integra=on Intelligent, integrated equipment Predic=ve capacity Raw Materials Traceability Usage Capability across the enterprise Adapted from Kinnet, J. Crea@ng a Digital Supply Chain: Monsanto s Journey, October 2015.

Architecture of the Digital Twin MODEL-BASED DEFINITION MulHple Connected RepresentaHons Context Behavior Shape Future Today Shape geometry topology logic constraints Behavior materials process dim./tol. physics DIGITAL THREAD MBD + IT architecture + Connec=vity Context assembly machining in use re=rement Interfaces Standards Requirements DIGITAL TWIN Product Line Model 1 Model 2 Model N Subsystem Component Temporal, lifecycle-based levels of a model-based definihon

So where do we start? Adapted from Jennifer Herron, Action Engineering Strategy Establish enterprise strategy Roadmap for slow, steady and accurate Evaluate ExisHng Digital Models Drawing vs. Model-Centric Connected vs. stand-alone Analysis vs. test results Supply chain capacity Build Value ProposiHon: Current state process mapping Future state process mapping to enable reduced cost Technology evalua=ons Evaluate Suppliers Establish an Ecosystem Readiness Determine gaps to implement technologies and data models Execute Methodically Measure against current state Report oxen Business Value OPPORTUNITY LOW Criteria are undefined, out of date, or both MEDIUM Criteria are defined, under change control, and repeatable Maturity & Capability HIGH Criteria are defined, under change control, repeatable, measurable, and con=nuously improving.

And how do we get there? Adapted from NSC, Curtis Brown DE Capability Maturity: Technology, Tools, and Standards are being assessed for their business value. DE Readiness Maturity: Data models are being proven for implementa=on in a relevant process environment. DE AdopHon Maturity: Technologies are being deployed in an opera=onal environment. DE AdapHve Maturity: Adop=on of technologies are being scaled and applied to new but similar areas. PULL PUSH MBE Maturity Levels Capability Assessment Tools, technology, and standards Adop=on for deployment Readiness for implementa=on Adap=ve for scalability Business Value Processes People & organiza=ons

A shift in the focus on jobs A person born today can expect to live to be 100-years old. Their careers will be 60 to 70 years long forcing them to not only change jobs but to change careers. This aligns with our college s tag line: How to prepare graduates for jobs that do not exist. The second is a shix in skill requirements. Demand for skills of the head (cogni=ve), have dominated those of the hands (technical) and to a lesser extent, those of the heart (social) over the past 300 years. In the future, those skills shixs are about to go into reverse.

Parallel Revolutions MechanizaHon ElectrificaHon AutomaHon DigitalizaHon EducaHon 1.0 EducaHon 2.0 EducaHon 3.0 EducaHon 4.0 ApprenHceship Manual/Industrial Arts Technology EducaHon Design & Systems Thinking/Maker movements Regardless of the era, the educahonal revoluhon connected to manufacturing has always had a focus on the tools and techniques of the day, to enable the design and produchon of something.

Workforce Education for Industry 4.0 Built upon the old literacies of reading, wri=ng and mathema=cs. New literacies include: Data literacy: read, analyze and apply informa=on Technological literacy: coding and engineering principles Human literacy: humani=es, communica=on and design Higher order mental skills mindsets and ways of thinking about the world. Systems Thinking: the ability to view an enterprise, machine or subject holis=cally, making connec=ons between different func=ons in an integra=ve way. Entrepreneurship: applies the crea=ve mind to the economic and social sphere. Cultural Agility: how to operate dexly in a varied global environment. CriHcal Thinking: the habit of disciplined, ra=onal analysis and judgement.

Takeaways Adopt technology to help manage complexity, track cost, and enable decisions. Transforming digital data authoring and consuming processes and prac=ces to achieve a digital enterprise is a culture change. Develop common digital data models, and stop individually clerking data Data architecture and compu=ng infrastructure are key enablers to adop=ng digital enterprise methods and tools. Data packaging and delivery can make or break adop=on. Our representa=ons must match our context. Educa=on of the exis@ng and future workforce is important for achieving technical competence and culture change.