From Carnegie Mellon to Berkshire Grey

          Matthew T. Mason, Professor, Carnegie Mellon University

 

Abstract:

When I first joined Carnegie Mellon in 1982, a mentor (Allen Newell) said that he had simplified his intellectual life by avoiding entanglements with industry.  I found that idea appealing, and followed that model for over 30 years.  It wasn't too difficult, because of my innate skepticism toward most visions of the next commercial robotics revolution.  That all changed in the course of a two-hour meeting with Tom Wagner, when he laid out his vision of a robotic transformation of materials handling, and a company (Berkshire Grey) that could make it happen.  Berkshire Grey emerged from stealth mode several months ago, so that I can now talk about Wagner's vision, Berkshire Grey's accomplishments, and the interaction of academia and industry.

Biography:

Matt Mason earned his PhD in Computer Science and Artificial Intelligence from MIT in 1982. He has worked in robotics for over forty years. For most of that time Matt has been a Professor of Robotics and Computer Science at Carnegie Mellon University (CMU). He was Director of the Robotics Institute from 2004 to 2014.

Since 2014, Matt has spent most of his time at Berkshire Grey, where he is the Chief Scientist. Berkshire Grey is a Boston-based company that produces innovative materials-handling solutions for eCommerce and logistics.

Matt is a Fellow of the AAAI and the IEEE.  He won the IEEE R&A Pioneer Award, and the IEEE Technical Field Award in Robotics and Automation.

 

 

          The operational butterfly effect: How IoT data + AI help deliver on the promise

          of 4IR

          Stephan Biller, Chief Innovation Officer & VP IBM Watson IoT

 

Abstract:

The challenges in industrial settings have remained the same for the last 50 years. Operations leaders are being asked to deliver more across KPIs—such as throughput, costs, efficiency, quality, and sustainability—and asked to deliver incremental improvement every year.

The Fourth Industrial Revolution (4IR) has brought much promise: more sensors, more connectivity, more data, better controls, more tools and methods to make sense of the data, and a greater understanding of consequences. But for all its promise, results have been stubbornly slow. Assets are increasingly complex, inter-dependencies are difficult to understand and real-time externalities are impacting these systems--often with unforeseen consequences.

The confluence of AI and advanced analytics, however, can get business leaders the transformational results they desire. These technologies can break through data silos and analyze your operations holistically to provide deeper insights that humans and advanced controls can act on. And it can all happen in real-time. AI and advanced Analytics can help you better visualize the butterfly effect, understand how decisions about maintenance of one asset will impact another, and help business leaders to obtain the agility their businesses so desperately need .

Biography:

Dr. Stephan Biller is the Vice President for Offering Management at IBM Watson IoT. He leads IBM's Industry 4.0 solution suites. These solutions aid clients to drive operational excellence from assets, factories & supply chains to the operations of oil fields and power plants using Big Data, Artificial Intelligence and Analytics. Prior to joining IBM in 2017, he served as General Electric’s Chief Manufacturing Scientist & Manufacturing Technology Leader founding GE’s Brilliant Factory initiative as well as its Additive Manufacturing software strategy. Earlier positions include General Motors Fellow & Global Manager for Sustainable Manufacturing Systems. He has co-authored a book on sustainable production along with 80+ publications and 11 patents. He was recognized by the Society of Manufacturing Engineers as a key thought leader in Industry 4.0, Digital Twin, and AI for Manufacturing. Dr. Biller holds a Ph.D. in Industrial Engineering from Northwestern University, USA, an MBA in Finance and Strategy from University of Michigan, USA, and an Dipl.-Ing. degree in electrical engineering from RWTH Aachen, Germany.

 

 
  

 

 

 

 

 

 

          Smart Manufacturing Ecosystem with Industry 4.0 Technologies

          MengChu Zhou, Distinguished Professor, New Jersey Institute of Technology

 

Abstract:

Industry 4.0 intends to address a fast-changing and challenging manufacturing environment with diverse demands, short order lead-time and product life cycle, limited capacities, and highly complex process technologies. A smart manufacturing ecosystem integrated with Industry 4.0 technologies, such as advanced automation, AI, machine learning, big data analytics, and Internet of Things, is capable of performing real-time monitoring and optimization of manufacturing processes in various aspects from high level strategic resource and production planning down to real-time equipment-level smart dispatching and predictive maintenance. By fully using real-time data and AI, the ecosystem is able to help manufacturers shorten production and R&D processes, increase production capacity, reduce production cost, guarantee product quality, and improve product yield. It is suitable to help not only high-tech industries such as semiconductor wafer fabrication, but also conventional labor-intensive sectors. This talk illustrates the transformation and improvement of manufacturing activities by using Industry 4.0 technologies through real-life application examples from the semiconductor manufacturing sector.

Biography:

MengChu Zhou received his B.S. degree in Control Engineering from Nanjing University of Science and Technology, Nanjing, China in 1983, M.S. degree in Automatic Control from Beijing Institute of Technology, Beijing, China in 1986, and Ph. D. degree in Computer and Systems Engineering from Rensselaer Polytechnic Institute, Troy, NY in 1990. He joined New Jersey Institute of Technology (NJIT), Newark, NJ in 1990, and is now a Distinguished Professor of Electrical and Computer Engineering. His research interests are in Petri nets, intelligent automation, Internet of Things, big data, web services, and intelligent transportation. He has over 800 publications including 12 books, 460+ journal papers (360+ in IEEE transactions), 12 patents and 28 book-chapters. He is the founding Editor of IEEE Press Book Series on Systems Science and Engineering and Editor-in-Chief of IEEE/CAA Journal of Automatica Sinica. He is a recipient of Humboldt Research Award for US Senior Scientists from Alexander von Humboldt Foundation, Franklin V. Taylor Memorial Award and the Norbert Wiener Award from IEEE Systems, Man and Cybernetics Society. He has been among most highly cited scholars for years and ranked top one in the field of engineering worldwide in 2012 by Web of Science/Thomson Reuters and now Clarivate Analytics. His work has been cited for over 30,000 with his H-index being 85 according to Google Scholar. He is a life member of Chinese Association for Science and Technology-USA and served as its President in 1999. He is VP for Conferences and Meetings, IEEE Systems, Man and Cybernetics Society. He is a Fellow of The Institute of Electrical and Electronics Engineers (IEEE), International Federation of Automatic Control (IFAC), American Association for the Advancement of Science (AAAS) and Chinese Association of Automation (CAA).

 

 

 

 

 

 

 

 

 

 

          Building Automation: Where is it Today and Where it Should be

          Srinivas Katipamula, Staff Scientist, Pacific Northwest National Laboratory

 

Abstract:

In early 1880s, building automation (BA) was born with the invention of a simple thermostat to control the boiler room temperature. Over the next century, building automation evolved from all pneumatic to digital controls for managing commercial building comfort systems. However, the potential of building automation has not been fully leveraged or realized. While sophisticated building automation systems (BASs) are used in large (>10,000 m2) buildings to manage systems, many buildings are not properly commissioned, operated, or maintained. Furthermore, over 85% of the commercial buildings stock in the U.S. does not use a BAS.

In the U.S., if existing BAS are used effectively and low-cost BASs are deployed in commercial buildings that currently do not have BAS, almost 30% of commercial building energy consumption can be eliminated. Further, the increased—and desired—use of clean renewable energy from wind, solar and other sources can likely be optimized with intelligent electricity load management within buildings, which will help ensure, for example, that buildings quickly and methodically reduce energy consumption at times when wind or solar generation suddenly drops off and the grid struggles to make up the deficit.

This presentation will highlight the evaluation of building automation, the current state building automation, where building automation should be and how building automation will allow seamless integration of buildings with the grid. The presentation will also show how building automation can be brought into the future by making building systems self-configuring, self-commissioning, self-learning, self-diagnosing, self-healing, and self-transacting–leading to a self-aware building state. To accomplish these advances in intelligent building automation, development of low-cost BASs for commercial building stock; automated fault detection and diagnostics, automated commissioning, and self-correcting and fault tolerant controls algorithms for building systems; and open and standard control protocol for homes are necessary.

Biography:

Dr. Katipamula is a Staff Scientist at Pacific Northwest National Laboratory (PNNL). For more than 25 years, his career has focused on research, development and deployment of methods and tools related to building automation, automated fault detection and diagnostics (AFDD), as well as automated self-correcting controls for building systems. He also has advanced transactive controls and integration of building operations with the power grid to deliver energy efficiency and improved grid reliability. Prior to joining PNNL, Dr. Katipamula led the Analytics Group at Enron Energy Services, and previously he was a research scientist in the Energy Systems Laboratory at Texas A&M University. At PNNL, Dr. Katipamula leads research projects in advanced building controls, AFDD, building operations and building-grid integration. His major responsibilities include development of transaction-based controls for commercial and residential buildings to make them more grid responsive. Dr. Katipamula is a Fellow of the American Society for Heating, Refrigeration and Air Conditioning Engineers (ASHRAE) and the American Society of Mechanical Engineers (ASME).