Artificial Intelligence and Machine Learning for Manufacturing

20 and 21 September 2023 | Online

R​ecordings Now Available * * Recordings are only available to those who registered for the event at this time.

Artificial Intelligence (AI) and Machine Learning (ML) seem almost tailor-made for the manufacturing industry. Optimizing processes, learning from previous experience, predicting when parts will break down or need maintenance, anomaly detection, and finding efficiencies in existing processes allow manufacturers to see real business benefits, both financially and in terms of sustainability.

But like every immature technology, AI and ML are experiencing growing pains after their initial burst onto the scene. There is no doubting the impact and potential for AI and ML to transform traditional engineering modeling and simulation. Still, that transformation needs to be informed – we cannot simply let loose and see what is next.

This cutting-edge NAFEMS seminar brings together industry, academia, and software providers to engage in the next stage of that process – generating real value from AI and ML.

We will explore the synergies and differences between machine learning and traditional engineering processes, look at how artificial intelligence is already being used in simulation, how it can be used more widely while retaining credibility, and hear from some of the most active players in the AI/ML field on how we can move forward.

Agenda

W​e have the following confirmed presentations so far, with more currently being peer reviewed by our committee.

(Click on the presentation's title to view an abstract.)

Day 1 - Wednesday 20 September

11:00
Welcome & Introduction
Mahmood Tabaddor - NAFEMS Americas Steering Committee Member

11:15
Driven Analytics for Assembly Process Discovery and Benchmarking at Volkswagen AG
Christine Rese - Volkswagen AG

11:45
AI and Machine Learning Enabling Manufacturing Process and Supply Chain Transformation
Larry Sweet - Advanced Robotics for Manufacturing (ARM) Institute

Break: 30 Minutes

12:45
AI, Machine Learning and Deep Learning in Nuclear Manufacturing for ITER components
Maria Ortiz De Zuniga - F4E (Fusion for Energy)

Case Studies 1 (Parallel Presentations; Stages 1-2)

1:15
Physics-Based vs. Data-Driven Methods to Accelerate Battery Test Cycles
Dr. Richard Ahlfeld, Monolith AI (Stage 1)

1:15
Three Practical AI Use Cases for Manufacturing Processes
Remi Duquette, Maya HTT (Stage 2)

1:45
Brief Preview of Day 1
Mahmood Tabaddor - NAFEMS Americas Steering Committee Member

Day 2 - Thursday 21 September

10:55
Brief Welcome
Mahmood Tabaddor - NAFEMS Americas Steering Committee Member (Stage 1)

11:00
Multilayered Large Language Models Strategies for Generating Time Series Simulation Data
Jon Chun, Kenyon College – Co-Founder, AI for the Humanities Curriculum and AI Digital Collaboratory

Case Studies 2 (Parallel Presentations; Stages 1-2)

11:30
Hybrid Digital Twin for Monitoring and Tuning Gas Treatment Unit
Laurent Chec & Bruno Trebucq, Datadvance SAS & Bruno Trebucq, CGI France (Stage 1)

Deep Learning for Manufacturing: an Application to the Rheology Process
Pierre Baqué (Stage 2) - Neural Concept

Main Program; Stage 1

12:00
Scientific Machine Learning in Industrial and Manufacturing Pipelines
Marta D’Elia - Pasteur Labs

Break: 30 Minutes

Case Studies 3 (Parallel Presentations; Stages 1-2)

1:00
Building Digital Twins at Scale
Sandeep Urankar - Rescale

1:00
Automatic Defect Classification (ADC) solution using Data-Centric Artificial Intelligence (AI) for outgoing quality inspections in the semiconductor industry
Quinn Killough, Onder Anilturk - Landing AI and NXP (Stage 2)

1:30
Enabling IT/OT Integration using Digital Twins of Business Processes
Hannes Waclawek -Salzburg University of Applied Sciences

2:00
Close
Mahmood Tabaddor - NAFEMS Americas Steering Committee Member

Background

In 2021, NAFEMS started a series to track the impact of advanced AI technologies on simulation technologies. Following a path similar to traditional engineering simulation, AI technologies are riding a wave of popularity with a haphazard record of value. These are simply the growing pains of a technology that is becoming an essential part of the product engineering toolkit and is helping push simulation technologies to deliver value throughout the product lifecycle and value chain.

This seminar series aims to help members hear directly about real-world and practical applications from industry experts. Previous seminars covered topics ranging from physics-informed machine learning to digital twins. In this, the 3^rd seminar of the series, the focus is on how AI technologies generate value for manufacturing and operations through various real-world use cases.

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