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U.S. Leadership in Adoption of Artificial Intelligence in Advanced Manufacturing
Mike Molnar | Director | NIST - Office of Advanced Manufacturing
Artificial Intelligence (AI) technologies promise to be the most powerful tools in generations for expanding knowledge, increasing prosperity, and enriching the human experience. The technologies will be the foundation of the innovation economy and a source of enormous power for countries that harness them. As AI moves from an elite niche science to a mainstream tool, engineering will be as important as scientific breakthroughs. Many of the most important real-world impacts will come from figuring out how to employ existing AI algorithms and systems, some more than a decade old.
The U.S. R&D priorities for federal investment in AI and strategic importance of AI and machine learning in advanced manufacturing are summarized in several government reports. Given the complexity of the issues, the characteristics of the manufacturing industry, and the broadly scoped definition and spectrum of AI possibilities, a comprehensive symposium comprised of a series of three workshops was conducted under the auspices of the National Science and Technology Council, Subcommittee on Advanced Manufacturing. A summary of findings of this symposium will be discussed with respect to manufacturing competitiveness and realizing resilient manufacturing ecosystems through AI. Furthermore, examples of current research at NIST related to AI standards and implementation of AI in manufacturing will be provided.
Jan de Nijs | Tech Fellow for Enterprise Digital Production | Lockheed Martin
Surprisingly, many large corporations are struggling to create viable and profitable advanced analytics solutions using data produced in the Life Cycle chain of products. The problem is not the volume of data: with the proliferation of connected devices, we are now awash in Life Cycle data. Rather, the problem is that data created by Life Cycle Producers (sources) almost always lacks meta-data information that can be used to create a usable digital thread (missing semantics connectors). In other words, it is very hard to develop generalized solutions that can autonomously interpret Life Cycle data and create insights across the product Life Cycle.
This presentation explains the standards-based approach Lockheed Martin is taking to address this issue. The solution centers around a digital thread that has been defined around specific meta-data tags that allow for an automatic link back to the model-based engineering requirements, and an ecosystem of internationally accepted standards. By implementing this solution across the complete Product Life Cycle, analytics solutions can be created that have the potential to become prescriptive, or even cognitive (“the factory runs itself!”).
Why is this applicable? Manufacturing has a lot of trouble retaining skilled data scientists. The problem is that within manufacturing, data scientists are being used as data janitors (manually cleaning up data), instead of enabling them to do what they are good at: creating big data analytics solutions.
Brian Valentine | Technical Project Manager | U.S. Department of Energy
US Department of Energy contributions to AI Applications in Advanced Manufacturing and Future Directions
The US DOE, through its extensive network of laboratories and research institutions, has invested heavily in AI ever since AI was recognized as a distinct scientific and engineering discipline. Six years ago, the DOE established an Office of Artificial Intelligence to integrate DOE laboratory capabilities in AI with Department of Energy core missions. Among those missions is the advancement of US manufacturing capabilities.
The Advanced Manufacturing Office within DOE’s Office of Energy Efficiency and Renewable Energy has supported numerous efforts to apply AI to many Advanced Manufacturing areas, including materials discovery, process design and development, and manufacturing automation. Some of these contributions will be reviewed, and new directions in Advanced Manufacturing outlined.
A Brief Case Study: Enabling Digital Cognition to Survive in a World of Exponential Data
Ron Norris | Director Operations Innovation | Georgia Pacific
It’s not just biology vs. digital. It’s your digital intelligence vs. your competitor’s digital intelligence. When we consider that data is projected to grow exponentially over the next few years, how we make decisions while creating knowledge and value at the rate that data grows is becoming the next hurdle for business survival. Developing new dashboards may feel like transformation, but dashboards still require someone to interpret and then act on the data being analyzed… and that is done at the speed of biology. As soon as tomorrow, the strategic advantage will include the ability to make objective decisions based on the shared knowledge and collective intelligence of the entire enterprise – at speeds and accuracies that are orders of magnitudes higher than anything previously possible.
Achieving Digital Transformation by Improving Quality through AI automation
Mike Hollinger | CTO of Vision and Sensor AI/ML | IBM
This talk will feature Ford’s process in implementing and training IBM’s Maximo Visual Inspection platform. The Maximo Visual Inspection solution supported Ford’s “no fault forward” initiative with in-station process control and quality remediation at point of installation or assembly. This kind of continuous process improvement is helping Ford lower repair and warranty costs, and improve customer satisfaction, while helping employees play a role in bringing technical innovation to the floor.
Achieving Federated Predictive Maintenance - a data engineering roadmap
Dr. Amir Kashani | Director, AI & Digital Product Development | Stanley Black & Decker
Sundeep Kumar | Head of Products and Big Data Services | Sigmoid
Recent research has shown that automobile vehicle model rollback is a multi billion cost every year. IoT and AI-driven analytics have transformed how companies approach equipment creation, maintenance and production line management. Stanley Black & Decker, has thousands of machines installed across Automobile companies plants to automate processes such as welding and bolting. This session will address Real time flagging of issues with the help of near real time dashboards; Ability to slice and dice the data at various levels to understand fault pattern; Programmatically identify faulty parts and prevent them from being added to the finished vehicle
Adopting Industrial AI for Smarter Manufacturing
Tara Thimmanaik | Systems Architect | Intel Corp.
In this session, Tara will address why it’s not just about getting AI training right, it’s also about taking the environment in which the AI performs into consideration. How efficient data driven manufacturing can help reduce labor costs, increase quality, and maximize profit, especially with the ongoing labor shortage in the industrial sector. It will also explore Barriers to adoption, and how to navigate them and why an ecosystem of easily scalable, end-to end solutions are essential to successful implementation of AI for smarte manufacturing
Sundeep Kumar has over a decade of experience in the data & analytics industry and has worked with global IT service giants such Mu Sigma, Cognizant Technology, and Infosys. Sundeep has dedicated his career to using data analytics to improve supply chain management through process automation & excellence. At Sigmoid, he has led multiple MLOps and AI related projects in the manufacturing sector to help drive better business strategy.
Leveraging Industrial AI Toward Zero-Downtime, Zero-Defects Manufacturing
Dr. Mo Abuali | CEO & Managing Partner | IoTco
Isaac Bennett | Vice President of IT & Digital Transformation | SAF-Holland
A decrease in unplanned downtime or a few percentages of scrap reduction can yield millions of dollars in savings for manufacturers. Does your organization have the necessary organizational and technical maturity to embark on your Industry 4.0 and predictive analytics journey?
This presentation will educate the attendees on the “digital tools of the trade” to realize Industry 4.0 in a systematic approach, with focus on the business case ROI, real time connectivity, and predictive AI-enabled technologies for Maintenance 4.0 and Quality 4.0.
Isaac Bennett | Vice President of IT & Digital Transformation | SAF-Holland
Formerly with automotive tier suppliers like Detroit Manufacturing Systems (DMS) and Maxion Wheels as their Global IT Innovation Manager. He has worked in the Manufacturing IT field for over 20 years, where he led the automotive IT departments global digital transformation initiative, helping plants around the world implement smart manufacturing projects.
AI & Manufacturing: A Broader Perspective
Dan Isaacs | CTO | Digital Twin Consortium
Stephen Mellor | CTO | Industrial Internet Consortium
Mitch Tseng | Co-Chair | Industrial Internet Consortium
Jim Novack | CEO | DYNATEC
Digital Twin Consortium drives the awareness, adoption, interoperability, and development of digital twin technology. Through a collaborative partnership with industry, academia, and government expertise, the Consortium is dedicated to the overall development of digital twins. We accelerate the market by propelling innovation and guiding outcomes for technology end-users.
The Industrial Internet Consortium's mission is to deliver transformative business value to industry, organizations, and society by accelerating adoption of a trustworthy Internet of Things. Since its founding in 2014, the IIC has helped build a technical foundation for the Industrial IoT. We work to help organizations take advantage of IoT technology and achieve positive outcomes. We are focused on driving technology innovation that fosters business transformation. Our goal is to help our members get the best return on their IoT investment.
Attendees will learn several best practices for how to strategically patent innovations arising from AI in manufacturing technologies, including industrial Internet of Things (iIoT), digital twins, and software patents. Your intellectual property (IP) strategy can bolster your competitive advantage—whether your invention relates to applied AI to improve a product being manufactured, improving IoT cybersecurity of a connected smart factory, or a machine learning model that is trained on proprietary diagnostic data to predict maintenance troubles. Moreover, building a strong IP framework for your manufacturing technologies is critical to avoiding landmines in the patent landscape. Manufacturing companies can face attacks from numerous directions when integrating AI and software/IoT technologies into their facilities. This session will also explain ways to minimize the risk of a patent lawsuit. Attends will learn a holistic framework to implement an IP strategy from both offensive and defensive positions.
Using AI To Unlock the True Potential of Today’s Workforce
Chris Kuntz | VP of Strategic Operations | Augmentir
Today’s manufacturing landscape is a dynamic mix of workforce challenges (including those brought on by Covid and the Great Resignation) and innovative solutions –many of which are powered by AI technology.This session will begin with a look at some dynamics in the manufacturing landscape and then focus on ways that technology (such as AI and connected worker solutions that recognize the variability in today’s workforce) are empowering workers by giving them tools and resources that will set them up for success.
Don’t get left behind waiting for AI/ML to mature
Rony Kubat | Co-Founder & CTO | Tulip
The digital transformation that includes AI/ML as a competitive advantage is the key to growth and scalability, yet the industry continues to see hesitance as manufacturers hold back implementing these technologies. For those whose core competency is executing in a physical world and extracting productivity from their workforce and machines, digital transformation can be overwhelming. IT and OT are still divided, and manufacturers continue to be on the fence regarding affordability, technological maturity and ease of execution.
Dr. Torsten Becker | Managing Director | BESTgroup Consulting GmbH
Many companies struggle with production planning and scheduling. Many companies cannot generate the production schedules in their IT systems. ERP system or manufacturing execution systems fail. Many companies still use Excel spreadsheets for planning the production in the near term. This solution is based on manual work and the knowledge of production foremen.
New artificial intelligence tools have now been integrated into many different systems to optimize. The key solutions and technologies will be introduced shortly. Based on the introduction of key capabilities of these new solutions, the focus is on the approach to implement these solutions. How can these solutions be selected and tested to ensure that the problems can be solved. How can production benefit from this solution and how can all employees convinced that the new solution will work.
Niro Osiroff | Co-Founder & CTO | Vanti Analytics
In this session, Niro, Vanti’s Co-Founder and CTO, will demonstrate, through a real-world example, how Vanti’s adaptive AI image-based defect detection model helped a leading electronics manufacturer overcome all obstacles to implement a stable, reliable, self-optimizing model, and immune to data drift.
The panel will discuss will be an open discussion focusing on topics including;
How AI is changing the manufacturing landscape
Technologies & best practices enabling production & supply chain efficiencies
Challenges facing todays manufacturers
Lean & advanced manufacturing
Cyber security and ransomware attacks
Lisa Masciantonio | Chief Workforce Officer | ARM Institute’s
Lisa Masciantonio is the Chief Workforce Officer for the Advanced Robotics for Manufacturing (ARM) Institute. She joined the ARM Institute in May 2017 as the Director of Membership and Outreach. She moved to the position of Chief Workforce Officer in 2019 and she is responsible for driving the Education & Workforce Development vision for ARM in conjunction with the ARM membership, the federal and state government partners, and other expert stakeholders.
The Industrial Metaverse and the Future of Work
Konrad Konarski | Chair | AI Innovation Consortium
Adam Berg | Director Learning Solutions | TechnipFMC
Guillermo Romero | Manufacturing Manager | Vallourec
William Aiken | PhD Student | University of Ottawa
Can Pu | Researcher | Facebook Augmented Reality labs
Mike Burgess | Division President | Pace Industries
Fred Allman | Sr Director, Public Sector | NVIDIA
Chris Murthwaite | Biologics Operations IT Lead | AstraZeneca
Chris is part of AstraZeneca’s Operations IT Leadership Team. He leads the team who are responsible for building and delivering the integrated roadmap to digitally transform AstraZeneca’s Biologics organisation worldwide and support its ever-increasing portfolio growth. As part of his role, Chris is also accountable for the IT delivery to support vaccines, biologics partnering activities and the operational scale–up of cell therapy. Originally from the UK, Chris is now based at the Gaithersburg, Maryland campus, which is one of AstraZeneca’s global R&D centres.
Curtis Richardson | Technical Fellow | Spirit Aerosystems
Curtis is a Technical Fellow at Spirit AeroSystems and leads the Smart Manufacturing segment of Spirit’s Distinctive Capability global corporate strategy encompassing research, technology, and capital investments as well as mergers and acquisitions. With a MSc in Industrial & Manufacturing Engineering and 25 years of experience with automating aerospace manufacturing, Curtis has been an active voice for robotics and advanced manufacturing including his role as co-chair of the Stakeholder Executive Council for the Advanced Robotics for Manufacturing (ARM) Institute.
Mahi Duggirala | Digital Manufacturing, Technology Strategy Development | Toyota North America
Mahi is a passionate Technologist and an accomplished executive with 20+ yrs. His experience lies in leading technical & operational teams, developing multi-disciplinary products & systems, and managing complex & challenging projects from ideation to successful delivery.
He has worked in both large global enterprise and start-up companies with deep technical, architectural, and product management knowledge and broad business exposure to cross-functional areas like strategy, technology partnerships & co-innovation alliances development. Mahi has a proven track record in driving engineering & operational excellence and customer success with a focus on agility and outcome-driven execution.
Kevin Kump | OT Technical Lead | Cyolo
Kevin brings more than 20 years of overall IT Security and compliance experience and over 10 years of cybersecurity, governance and critical infrastructure experience working in the energy, medical, manufacturing, transportation and FedRAMP realms, (to name just a few). Kevin is also a member of the FBI InfraGard program and have started security training programs at the local college level for up and coming students in the IT and IT Security fields.