Author: eya14001

The World’s First “Manufactory 4.0” Has Been Opened in Hartford, CT


Stanley Black and Decker recently became the first company internationally to open up their brand new “Manufactory 4.0” at Constitution Plaza in Hartford, CT. This 23,000 square-foot center will serve as a state-of-the-art advanced manufacturing center and training center for the company’s international industry 4.0 “smart factory” initiatives. This new facility will also help to highlight Stanley Blacker and Decker’s success with integrating industry 4.0 practices into their company’s policies, such as how they have improved communication between humans and automated technology, used interconnected systems to improve the collaboration between plants and solve problems, and utilize big data analytics to improve productivity and efficiency world-wide. Operated by a team of experts in the field, the facility not only aims to showcase the company’s continued work with integrating technologies and practices of the future into their current manufacturing technologies, but also to provide an interactive space where young professionals and younger children can see what types of career opportunities are available in advanced manufacturing.


Based in New Britain, CT, Stanley Blacker and Decker operates and maintains approximately 30 manufacturing facilities across the US, and more than 100 facilities worldwide. Out of these locations, three facilities have been designated as “lighthouse factories”, which have been working to partially implement industry 4.0 techniques into their facilities and into other technologies, such as manufacturing execution systems (MES), artificial intelligence systems, 3-D printing, and virtual reality. Now, with the opening of the new Manufactury 4.0 facility, the company plans to integrate another 25 “lighthouse facilities” into their global base by the end of the 2019 year.


The opening of the Manufactory will make Hartford the epicenter of the company’s industry 4.0 efforts. Not only will this help to integrate smart factory 4.0 technologies into Stanley Black and Decker locations world-wide, but it will also help to catalyze Connecticut’s evolution into the leading market for advanced manufacturing in the United States. A Manufactory is described as a training center, that utilizes industry 4.0 technologies such as digital thread, digital twin, IoT, ARVR and big data analytics to help connect manufacturing facilities world-wide and integrate new and innovative industrial technologies into the workforce on a united front.  Through their virtual reality techniques, the center will work to assist other facilities world-wide in adopting leading edge-technologies and ensuring that the global workforce is adequately prepared for the new industry. One of the highlights of this facility is the demo shop floor, where the all the technologies and equipment are part of the facility’s digital thread. These fully specified machines, along with are used to train operators and mechanics around the world.


Dr. Bollas and Dr. Thompson with the University of Connecticut UTC Institute of Advanced Systems Engineering attended the Stanley Black & Decker (SB&D) Manufactory Grand Opening last Thursday in Hartford with a contingent of faculty and staff from the University of Connecticut. SB&D’s CEO, Hartford’s Mayor, and Connecticut Governor Lamont all attended and spoke at the opening. This news clip captures Dr. Thompson discussing the capabilities of Infosys technologies with an Infosys representative. The Infosys technology monitors a parts orientation process and determines raw material, process, or machine errors and defects, real-time, with an intuitive, graphical user interface. The machine operator has the ability to determine problems with a better data set and with built-in AI that aids in the diagnosis of errors and faults. This data can then be used to better design the parts, improve the process and machine, or resolve supply chain quality issues.


The SB&D Grand Opening was a culmination and celebration of a multiyear effort to launch the Industry 4.0 initiative at SB&D.  The manufactory space’s purpose at this one-of-a-kind facility is to implement new augmented reality, virtual reality, AI, IoT, and Big Data technologies to support its development of digital threads and digital twins and improve product and manufacturing capabilities and efficiencies that coincides with the work being done at UTC IASE. The Institute for Advanced Systems Engineering looks forward to the opportunity to support them in this effort and to partnering with them to help make industry 4.0 a reality.

The CTIN4SPIRE Seminar Series Hosts Dr. Wolf Wadehn


On April 18th, Dr. Wolf Wadehn spoke at the weekly seminar series for the CTIN4SPIRE program. Dr. Wadehn is the Director of Engineering at TRUMPF, Inc. in Farmington, CT, where his work focuses on establishing his company’s US subsidiary as a global competence center for remote service tools, machine connectivity, and data analytics. Dr. Wadehn studied mechanical engineering at the University of Munich and Stuttgart and completed his Ph.D. thesis on adaptive structures. In 2005, he started as an engineer for TRUMPF, where he worked on numerical calculations for 5-axis laser machines until he started his management position in 2015.


In his talk entitled “Smart Factories: Industry 4.0 in Sheet Metal Manufacturing”, Dr. Wadehn explained that his intention of his presentation was to disprove that manufacturing is “slimy and greasy” and only requires mechanical engineers. TRUMPF has been a family owned business since 1923, has 73 subsidiaries, and is comprised of 13,420 employees. The company specializes in using advanced machining and special laser systems for cutting, punching, bending, welding, marking, and other industrial applications. He described the company’s core competencies, such as innovative practices utilizing laser technologies and prioritizing “speedy” innovation, to segway into current technologies in the industry and what specialized smart factories of the future may look like. He handed out samples of laser cut material to the class, and explained the process of using a laser machine to perform specific tasks, such as cutting a material like the sample, which contained intricate small features. The sheet metal cutting machine will need to operate continuously throughout the process, and if it is not running continuously, manufacturing companies can lose thousands of dollars each day or each week, so uptime is crucial.


He finalized the talk by describing how smart factories can be used to maximize efficiency of production and to minimize cost, and outlined the equipment and technologies that can be found in a smart factory. Dr. Wadehn explained seven (7) components of a smart factory: 1. Connectivity and transparency, 2. Full control of equipment, 3. Inventory management, 4. Full transparency on order status, 5. No searching times, 6. Automated transport with AGV, and 7. Remote support through experts. He used these components to illustrate how the current production practices can be improved upon. He also mentioned how smart factories create a smart material flow, which eliminates the possibility of any parts going missing.


Dr. Wadehn closed the presentation by showing an example factory of the future and described what a customer’s smart manufacturing project would look like. He explained that smart factories use innovative technologies, such as AGV systems, predictive maintenance, and advanced software to increase the efficiency of manufacturing. In addition, the integrative concepts found in these factories provide jobs for more than just mechanical engineers; this type of work requires an integrative team of engineers and non-engineers from many disciplines to help the manufacturing process run smoothly.


The CTIN4SPIRE program will be hosting two more talks this semester on Thursday April 25th  and Thursday May 2nd. To view the CTIN4SPIRE speaker series calendar and to find out more about the series click here.

Inspiring Industry 4.0 Through University-Industry Collaboration


The Connecticut Industry 4.0 Synergistic Platform for Innovation-Rich Education (CT IN4SPIRE) Program is an integrative state-wide initiative, wherein industrial experts will leverage the state’s higher-education and industrial resources to help train and prepare the future workforce and promote growth of Connecticut’s Economy. The purpose of the CTIN4SPIRE program is to utilize Connecticut’s academic and industrial resources to help prepare students, entrepreneurs, and industry professionals for the “Industry 4.0” innovations which will soon be the leading force in the near future. Participants in the program will learn how to apply the knowledge and concepts they have taken from this initiative, along with advanced digital technologies, to improve the state’s current industrial capabilities. The program will establish a revolutionary new course hosted by the University of Connecticut, where professionals in the field will instruct a weekly seminar series, focusing on new Industry 4.0 concepts and technologies. Each seminar will be live-streamed to each of the partner sites, to help connect participating companies, sites, and individuals together on one platform. The program will also feature two workshops per semester, focusing on real-time applications of I4.0 technologies and concepts, and challenge the schools, entrepreneurs, and workforce professionals to work together in project teams to tackle current obstacles in the industry. A panel of judges will select the best projects and innovative concepts, to be developed the following semester. Students will by design and prototype their solutions using UConn facilities and the Connecticut Center for Advanced Technology. Other project goals are to help train students in the real-world applications of advanced Industry 4.0 technologies, to catalyze industrial relationships between students and professionals, to incentivize and motivate students to join the revolutionary and innovative 21st century workforce, and to bring a sizeable change to innovation in CT’s industrial base.


The one-credit seminar course, offered as ENGR3195/ENGR5300/OPIM4895, is entitled “Industry 4.0 and Manufacturing Ingenuity” and focuses on providing students with the experience and exposure to new and emerging technologies while fostering a strong mindset of innovation and entrepreneurship in students. The course will introduce students to technologies such as automation, cyber physical systems, informatics and advanced manufacturing, and simultaneously provide students with coordinated mentorship from faculty and industry experts. The course is centered on weekly seminars and workshops where students can apply their new knowledge to current challenges in the field. Seminars will feature invited industry professionals who will introduce cutting edge technologies and challenges they face in the field. The workshops support students’ abilities to define solutions and ideas to solve these real-world challenges, where they can integrate Industry 4.0 technologies and concepts to solve industrial problems. The speaker schedule can be found below:




UTC-IASE Welcomes New Associated Faculty Member Dr. Hongyi Xu



Please join the UTC Institute for Advanced Systems Engineering in welcoming Dr. Hongyi Xu, who has recently joined the Department of Mechanical Engineering as an Assistant Professor. Prior to joining UConn, he worked at Ford in their Research and Advanced Engineering Department from 2014 to 2019. At Ford, he worked on many projects such as structure optimization for vehicle lightweighting, integrated computational material engineering of carbon fiber composites, lithium-ion battery impact safety, fuel cell membrane analysis, and the design of mesostructured systems for additive manufacturing. He received his B.S in Mechanical Engineering from Northeastern University in China, a M.S in Mechanical Engineering from Tsinghua University in China, and a Ph.D. in Mechanical Engineering from Northwestern University in the US.

   His current research focuses on developing design optimization for the analysis and design of heterogeneous microstructural materials. His research interests also include Design for Additive Manufacturing and data mining-enhanced multi-disciplinary optimization. He is currently working on two research projects. The first project is titled “Microstructure modeling for lithium-ion battery materials”, and the second is titled “Design and uncertainty quantification of additive manufactured mesostructured-structure system”. In the past 4 years, Dr. Xu has worked on several government and private industry sponsored projects, including two Department of Energy funded projects. The first project focused on Integrated computational materials engineering for the development of carbon fiber composites of lightweight vehicles. The second grant worked towards the development and validation of a simulation tool to predict the combined structural, electrical, electrochemical and thermal responses of automotive batteries.

   In May of 2019, Dr. Xu will be traveling to Beijing to attend the World Congress of Structural and Multidisciplinary Optimization. There, he will be presenting on “Mesostructure Optimization for Additive Manufacturing Based on Multi-fidelity Modeling and Particle Swarm Optimization Algorithm”. In August of 2019, he will be attending the ASME 2019 International Design Engineering Technical Conference & Computers and Information in Engineering Conference, where he will be presenting on “Multi-Fidelity Variance and Sensitivity Estimators and Adaptive High-Fidelity DOE for the Design of Mesostructure-Structure Systems”.

   Dr. Xu has already had two papers published in the early months of 2019. He was one of the authors of a paper titled “Failure of chopped carbon fiber sheet molding compound (SMC) composites under uniaxial tensile loading: Computational prediction and experimental analysis”, which was published in Composites Part A: Applied Science and Manufacturing. He also was a co-author along with Liu Zhao on a paper titled “Control variate multi-fidelity estimators for the variance and sensitivity analysis of mesostructured-structure systems”, published in ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering. Along with his papers already published, he also has two other papers that are currently under review. The first is titled “Modified multi-scale finite element method with the nodes linkage technique for predicting elastic property of metamaterials”, which in under review in Frontiers of Mechanical Engineering. The second paper is titled “Stochastic 3D Microstructure Reconstruction and Mechanical Modeling of Anisotropic Battery Separators”, under review in the Journal of Power Sources.

UConn IASE recognized for their work with Sustainable CT Award Winner



Members from the UConn Institute for Advanced Systems Engineering (IASE) recently supported the Town of Fairfield’s application for recognition in the Sustainable Connecticut Program that earned them a top-level honor in the program.


The Sustainable Fairfield Task Force (SFTF) provides support for projects and initiatives that help Fairfield maintain the growth and health of its environment, ensure a proper and economical use of the town’s natural resources, and promote a high quality of life for all residents. Multiple municipal departments contribute to the SFTF, each of which focus on different aspects of Sustainable CT initiatives. The UConn team was primarily responsible for providing building energy benchmarking technical support to the Fairfield effort.


The UConn team was led by Dr. Amy Thompson, an Associate Professor-in-Residence of Systems Engineering at the IASE. Two undergraduate students, Ian Beattie, a senior in Environmental Engineering, and Emma Atkinson, a senior in Biomedical Engineering contributed as well. The UConn students collected building energy information and data from Fairfield and United Illuminating and used the EPA’s Portfolio Manager tool to provide building energy assessments for each of Fairfield’s 22 municipal buildings and 16 school buildings. The building energy benchmarking component was a key component in the Sustainable CT judging process. United Illuminating provided funding to the UConn IASE for faculty and student support of the benchmarking effort and also provided the real-time data exchange system that made the benchmarking possible. “United Illuminating believes that supporting engineering students in this project to learn more about building energy efficiency may encourage them to enter careers in energy engineering. The project allows students to see first-hand real issues and solutions for lowering building energy consumption in municipal and school buildings and the project provides a valuable technical resource to communities like Fairfield,” says Sheri Borrelli, United Illuminating.


The Sustainable CT Initiative, which began last October, awards certifications to towns and communities across Connecticut that achieve high standards across a variety of sustainability accomplishments. The program gives awards in three categories based on the amount of points received. The highest ranking is a Gold Certification, the second is the Silver Certification, and the last is the Bronze Certification. The town of Fairfield earned tops honors for the program, being one of only five towns in the state to receive a Silver Certification. In addition, Fairfield also received the highest ranking in the Silver category, deeming the town the “Most Sustainable Town in Connecticut”. Some of the most notable accomplishments that were cited from the Fairfield application were building awareness of the town’s history, assessing potential town-wide impacts of climate change, supporting projects such as recycling, composting, green space preservation, and complete streets policies, and increasing the use of solar energy systems across the town.


An awards ceremony took place at the Fairfield Museum and History Center on January 18th, 2019, which celebrated the award and honored everyone who contributed to not only the award, but making Fairfield a sustainable place to live and work. UConn students left the ceremony thinking, “this is a great place to live.”


UTC-IASE Faculty Spotlight: Dr. Xu Chen


This week’s faculty spotlight is on Professor Xu Chen, who is an assistant professor in the Department of Mechanical Engineering. Dr. Chen received his M.S. and Ph.D. degrees in Mechanical Engineering from the University of California, Berkeley in 2010 and 2013, respectively, and his Bachelor’s degree from Tsinghua University, China in 2008.  He is a recipient of the National Science Foundation CAREER award, the Young Investigator Award and the Best Paper Award from ISCIE / ASME International Symposium on Flexible Automation, the 2017 Best Vibrations Paper Award from the ASME Dynamic Systems and Control Division, the 2017 UConn University Teaching Fellow Award Nominee, and the 2012 Chinese Government Award for Outstanding Students Abroad.


Professor Chen is the principle investigator for the Machine, Automation and Control Systems Laboratory (MACS) through the Department of Mechanical Engineering. The overall research goal of his lab is to seek better understanding and engineering of the systematic interplay between data, systems and controls in machines and automation processes. For instance, fast situational awareness and agile response is imperative to advancing system operation in this information age. To reach such capabilities, Prof. Chen’s team exploits approaches to reliably and quickly combine all data from heterogeneous sources in a feedback control system. An example of such is a project conducted from Dr. Chen’s smart manufacturing research called “Model-Based Sparse Information Recovery by Collaborative Sensor Management”. This project provides a novel approach to collect dense information from a group of collaborative sensors at a significantly reduced computation burden and in real time. The result is particularly impactful for applications such as imagining-based automation, where vision data take time to collect and complex elaborations must be performed to extract information from the raw data. More broadly, this work relates to the overarching challenge of making full use of data to infer and respond to fast evolving situations in decentralized environments, and provides a pathway to better integrate multiple data-intensive sources.


During the summer of 2018, Dr. Chen and his laboratory team traveled to three flagship conferences in the fields of controls, automation, and 3D printing: The American Control Conference at Milwaukee in June, the International Symposium on Flexible Automation at Kanazawa, Japan in July, and the Annual international Solid Freedom Fabrication at Austin, Texas in August. The three published papers from Prof. Chen’s team discussed smart controls approaches for critically needed quality assurance of additive manufacturing (AM), a nascent manufacturing technology that offers untapped potential in a wide range of products for the energy, aerospace, automotive, healthcare and biomedical industries. In particular, the focused powder bed fusion process is increasingly preferred in applications ranging from advanced jet-engine components to custom-designed medical implants. Prof. Chen’s research looks into the convoluted thermomechanical interactions in the multi-physics multi-scale manufacturing process, and has been generating award-winning, internationally recognized results to enable substantially higher accuracy and greater reproducibility in AM. For instance, the recent paper titled “Synthesis and Analysis of Multirate Repetitive Control for Fractional-Order Periodic Disturbance Rejection in Powder Bed Fusion” was featured in the proceedings of the 2018 International Symposium on Flexible Automation, where this paper written by PhD Student, Dan Wang, and Dr. Chen, won the “Best Paper Award In Theory”.

UTC-IASE Faculty Spotlight: Dr. Ashwin Dani


This week’s UTC-IASE faculty spotlight is on Dr. Ashwin Dani, who is an assistant professor in the Department of Electrical and Computer Engineering. He received his B.S in Mechanical Engineering from the University of Pune in India, and then went on to receive a Ph.D in Mechanical and Aerospace Engineering from the University of Florida. He began his career in academia as a post-doctoral research associate at the University of Illinois at Urbana-Champaign in 2011, and then moved to the University of Connecticut in 2013 and currently works as an assistant professor.


Dr. Dani is the PI for the Robotics and Controls Lab, which focuses on solving various estimation and control challenges in engineering domains such as robotics, automation, industrial and biomedical applications. His research falls into areas such as (1) Model building from data using machine learning, (2) Human-Robot collaboration and safety issues in manufacturing environments, (2) GPS-denied navigation of unmanned aerial systems and improved autonomy, (4) sensor data fusion, and (5) estimation and control for neuro-prosthesis. He has also done previous work in areas such as estimation and control theory, robotics, autonomous navigation, localization and mapping, and vision-based control.


This summer, Dr. Dani attending the American Control Conference in Milwaukee, where a paper titled “Learning Stable Nonlinear Dynamical Systems with External Inputs using Gaussian Mixture Models” was presented by the UTC-IASE Fellow Iman Salehi. Iman Salehi, a UTC-Fellowship graduate student, worked under the counsel of Dr. Dani this summer. Salehi’s project is titled “Physics Informed Machine Learning Project”, which is researching how to develop models for heat exchangers using machine learning that embeds dynamical system properties. The efforts in this project bring a paradigmatic change to how model building using machine learning is looked at to include physical properties of the system. This paper presented a data-driven modeling method with convergence guarantees embedded in machine learning. This paper is closely related to the “Physics-Informed Machine Learning Project”.


Professor Dani worked with UTC this summer on a project titled “Physics Informed Machine Learning” and “Wire Harness Assembly using Robots”, with a goal to develop methodologies and architectures for data-driven model learning, containing dynamical system properties coming from physics of the system embedded in machine learning. In terms of research publications, Dr. Dani and his lab worked on extending the conference manuscripts presented over the summer, to journal manuscripts, for projects  “Physics Informed Machine Learning”.

Dr. Abhishek Dutta’s Microcircuit Shows Promise to Improve Biobots


UTC IASE faculty member, Dr. Abhishek Dutta, was recently highlighted by the university, in recognition of his work on biological control systems and bio-robotics. Dr. Dutta, who is an assistant professor of Electrical and Computer Engineering, is using his specialization in control systems, design and optimization and cybernetics, to build a hardwired biological insect that can be used to precisely control the insect’s motion.  Since then, the “UConn neuro controller” has been trending in hundreds of news outlets worldwide engaging as many discussions on social media.

The microcircuit is strapped to the roach, and then interfaces with wired micro-electrodes that connect into the antenna lobes. The signal is transmitted through a wireless device in the circuit and a receiver allows for the motion to be controlled through a ground station. The microcircuit sends tiny electrical currents through the wires into the neural tissue in the antenna lobe. Once the brain receives these impulses, it makes the insect believe that there is an obstacle away. Therefore, if an appropriate signal is received by the left antenna lobe, the insect will move to the right and vice-versa.

Professor Dutta is using the Madagascar Hissing Cockroach as the organism under study, where he has built a microcircuit that wires directly into the brain of the roach and uses the circuit as a neuro controller. The circuit incorporates a 9-axis inertial measurement unit that can detect the roach’s six degrees of free motion, its linear and rotational acceleration, as well as its compass heading. Other features that help with the performance of the circuit is a sensor to detect the ambient surrounding temperature, which will help map the correlation between the environmental conditions and the motion of the roach.

Although a couple of similar systems have been developed previously, this microcircuit stands out because it offers a multi-faceted approach for stimulating the nerve tissue, real-time feedback of the insect’s response to the electrical stimuli, and a greater degree of control of the insect’s motion, resulting in a more informed and precise control system. As the microcontroller and built-in potentiometer work in tandem, the operator is able to vary the output voltage, frequency, and cycle of stimuli delivered, which helps to produce a more robust response, while not causing damage to the roach.

This work shows great promise, with the possibility of human applications in the future. Microcircuit and micro-control systems, such as these, are the first step in developing  larger-scale controllers and circuits that can be used in industries like healthcare, automotive, and artificial intelligence. One proposed use is to use this technology to help control motor functions in organisms with spinal cord injury or paraplegic organisms. “Our microcircuit provides a sophisticated system for acquiring real-time data on an insect’s heading and acceleration, which allows us to extrapolate its trajectory,” says Dr. Dutta. “We believe this advanced closed loop, model-based system provides better control for precision maneuvering and overcomes some of the technical limitations currently plaguing today’s micro robots”. Dr. Dutta states that much more research is needed, although the current technology has great promise to lead to a new generation of products with even larger scale applications.

UTC IASE Faculty Spotlight: Dr. Peter Luh


This week’s faculty spotlight is on Dr. Peter Luh, who is the SNET Endowed Professor in the Electrical and Computer Engineering Department. He is affiliated with UTC IASE and Booth Engineering Center for Technology (BECAT). Dr. Luh received a Bachelors of Science in Electrical Engineering from the National Taiwan University in 1973. He then went on to receive a Master degree in Aeronautics and Astronautics from the Massachusetts Institute of Technology and a Ph.D in Applied Mathematics from Harvard University. Dr. Luh is a Board of Trustees Distinguished Professor, the SNET Professor of Communications and Information Technologies at the University of Connecticut, Storrs, and a Life Fellow of the Institute of Electrical and Electronics Engineers (IEEE).


Dr Luh is the Director of the Manufacturing Systems Laboratory in the Department of Electrical and Computer Engineering, which focuses on advancing information technologies and mathematical optimization techniques that are of strategic importance to society, including Intelligent Manufacturing Systems, Smart and Safe Buildings, Smart Grid, and decision-making under uncertain, distributed, or antagonistic environments. The research conducted in his lab has been supported by the NSF and other industry partners, such as UTC, Southern California Edison, ISO-New England, MISO, Alstom, Northeast Utilities, GE, and Toshiba, over the past 30+ years. His research has led to over 150 journal papers published, over 320 conference presentations, and 35 Ph.D students graduated.


Research-wise, he has made pioneering contributions to 1). Intelligent manufacturing for efficient production and on-time product delivery; 2). Smart grid for efficient generator coordination and renewable energy integration; and 3). Smart building for energy efficiency and safety. The overall goals of his research focus on efficiency, greenness and sustainability. Behind these three areas of focus/goals is a revolutionary approach to overcome the notorious combinatorial complexity of mixed-integer optimization problems (involving both discrete and continuous decision variables). The keys include an innovative way to formulate problems in a “separable” and “tightened” form, and an advanced price-based decomposition and coordination methodology. They represent two major breakthroughs and new directions in formulating and solving such important but difficult optimization problems. These exciting results were developed together with his graduate students and colleagues, in particular, Assistant Research Professors Mikhail Bragin and Bing Yan, and Associate Professor Peng Zhang.


Dr. Luh received the IEEE Robotics and Automation Society (RAS) Pioneering Award in 2013 for his contributions to the development of near-optimal and efficient planning, scheduling, and coordination methodologies for manufacturing and power systems. Beyond journal papers and conference presentations, Dr. Luh has delivered 140 invited talks around the world. His current research is supported by grants and contracts from the NSF, United Technologies Corporation, ISO-New England, MISO, ABB, Brookhaven National Laboratory, and UConn Provost Academic Plan.


In terms of his teaching career, Dr. Luh is a distinguished and accomplished professor who regularly teaches systems-related courses. Among his 35 Ph.D. students graduated, three received the School of Engineering’s Distinguished Engineering Alumni Award, one is a member of Chinese Academy of Sciences, and one served as a National Communication Commissioner in Taiwan. Professor Luh also has participated many times in the School of Engineering’s week-long daVinci Projects, which are designed for high school mathematics, science teachers, and counselors. During his time here at UConn, he has served as the director of BECAT (1997-2004), and the Head of the Electrical and Computer Engineering Department (2006-2009). He has served on various committees within the university, such as the Faculty Review Board (1997-1999), the Research Advisory Council (1998-1999), the University Academic Vision Committee (2013-2014), and the Center and Institute Review Committee (2017-2019).


Professor Luh has also played a vital role in IEEE. He served as the Editor-in-Chief of IEEE Transactions on Robotics and Automation (1999-2003), the founding EiC of IEEE Transaction on Automation Science and Engineering (2003-2007), RAS Vice President for Publications (2008-2011), and received the IEEE RAS George Saridis Leadership Awards for his exceptional vision and leadership in strengthening and advancing automation. He is currently the Chair of IEEE Technical Activities Board Periodicals Committee for the 2018-2019 term, and oversees 190 IEEE journals and magazines.

Dr. Xu Chen, PhD student Dan Wang awarded the ISFA Best Paper Award


UTC-IASE Professor Xu Chen and student Dan Wang received the Best Paper Award at the 2018 International Symposium on Flexible Automation (ISFA). The ISFA was initiated in 1986 under the co-sponsorship of the American Society of Mechanical Engineering (ASME) and the Institute of Systems, Control, and Information Engineers (ISCIE) in Japan. The symposium focuses on automation technologies that are essential to meet the increasing requirements of modern manufacturing and other related fields, such as dynamical systems, robotics, logistics, medical systems, and healthcare systems. The 2018 symposium was held in Kanazawa japan from July 15th, to July 19th. Each year, the symposium recognizes the two best papers appearing in the proceedings and presented at the symposium. One award emphasizes the paper’s contribution to theory, and the other award emphasized significant or innovative applications/practices. Criteria for selection include the quality of the written and oral presentation, the technical contribution, timeliness, and practicality. Each awardee is given a certificate and an honorarium of $1,000.


The paper, titled “Synthesis and Analysis of Multirate Repetitive Control for Fractional-order Periodic Disturbance Rejection in Powder Bed Fusion”, discusses control approaches to advance the quality of repetitive energy deposition in powder bed fusion (PBF) additive manufacturing. More specifically, it pertains to the repetitive deposition of the laser or electron beam energy. The paper addresses an intrinsic limitation in control schemes that can be used to leverage the periodicity of task patterns to significantly improve system performance. Some of the long-term impacts anticipated for this work include greater quality assurance of the manufactured parts, new capabilities for large-scale 3D printing of extreme materials, and smarter machines and automation in additive manufacturing processes.