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Advanced Manufacturing

Advanced Manufacturing. Stuart Slattery(ORNL), Vincent Paquit (ORNL) and Jim Belak (LLNL) Co-Leads Themes: Decision Intelligence Data Workforce. Manufacturing decisions span many time scales. Digital thread/twin. AM Melt Pool, SM Adaptive Tooling.

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Advanced Manufacturing

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  1. Advanced Manufacturing Stuart Slattery(ORNL), Vincent Paquit(ORNL) and Jim Belak(LLNL) Co-Leads Themes: Decision Intelligence Data Workforce

  2. Manufacturing decisions span many time scales Digital thread/twin AM Melt Pool, SM Adaptive Tooling Design space intent, manufacturability Lifetime/sustainability Seconds Months Milliseconds Hours Years AM Layer-by-Layer defect mitigation Part-in-field tracking, maintenance, fabrication feedback When do we collect data and what do we collect?

  3. Intelligent Manufacturing • Advanced Manufacturing • Submitted by AM Team • Breakthrough needed in the development of feedback loops to adaptively control subtractive and additive manufacturing processes • Realtime feedback based on intent (function, quality, cost, etc.) • Decisions include path selection, intensity of laser, defect correction strategy • Define proper instrumentation for machines as the primary data source • Compute needed at the sensors simplify the process control decision process • Timescale of decision making on the order of seconds or less • Current approaches have limited use of data from instrumented machines • Current pipelines lack bandwidth to process all of the available data • Machine instrumentation is limited; unclear what data is most important • We expect AI to be applied at the sensor and in the process control • AI could be used to both augment the process via decisions and as a passive observer in a bootstrapping process (next slide)

  4. Bootstrapping the Intelligent Machine • Advanced Manufacturing • Submitted by AM Team • Automate the entire learning pipeline • Goal: Submit the same design spec to multiple manufacturers and get the same result from each • Combine sensor modalities • Incorporate data from a fleet of machines instead of a single machine • Develop an open framework with plug-and-play module capabilities • We need curated data sets to enable this approach (AMBench) • AI techniques to learn by observing • We want machines that learn (e.g. reinforcement learning for machines) • AI will be deployed on distributed systems (edge to enterprise)

  5. Design Space Intent • Advanced Manufacturing • Submitted by AM Team • Enabling a broader design space through machine-driven rapid simulation workflows to meet an engineering specification • Functional/implicit design • Probe a wide range of physics, geometries, materials in a large optimization space • Include constraints based on cost, quality, lifetime, aesthetics, manufacturability, recyclability • Incorporate feedback from digital twins and part-in-field data • Incorporate data from high-fidelity HPC simulations • Topology optimization is not routine • Workforce not trained in functional design – design engineers are used to making the decisions • AI approaches applied to generative design, inverse design

  6. Decision Provenance • Advanced Manufacturing • Submitted by AM Team • Pedigree of decisions (both made by machines and people) • Document the context of decisions (the known and unknown data) • Ontology/taxonomy for search (i.e. keywords) • Version control • A characterization of the data available to address the challenge • Known unknowns, assumptions, criteria, contingencies • Current manufacturing approaches record the state of the machine • We have data but we can’t correlate it with the decisions that were made • Decision provenance is a requirement for the application of AI to manufacturing

  7. Trusted Manufacturing • Advanced Manufacturing • Submitted by AM Team • How do we establish trust in the data, software, firmware, and hardware we use in intelligent manufacturing? • Automated detection of and recovery from malicious activity targeting the machines themselves or the integrity of the manufactured product • Inference of cyber-physical attacks, counterfeit prevention (block-chain approach) • “Born-qualified” • We want a check-sum or a fingerprint for the manufacturing process • Some companies have approaches to this problem but there are no universal standards • Examples include ProSTEP (secure AM platform), Industrial Internet Consortium (chain-of-trust for vehicle software updates) • Need a more comprehensive, secure digital trust system • Trust and security often an afterthought • Trust is an essential requirement for the application of AI in manufacturing • Intelligent manufacturing includes checks for quality – consider this built-in validation for building trust

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