Review the accomplishments and aspirations of the 20 most influential academics identified by Smart Manufacturing magazine this year, and you may spot some patterns in the data—no artificial intelligence required.
As one of the professors profiled on the next 10 pages put it, they are the “pioneers and dreamers” exploring opportunities and creating new ones in smart manufacturing. One dreams so big that he wants to enable distributed manufacturing on the moon. Others want to put their work and discoveries to use on earth—and have worked with legacy equipment owners to help them join Industry 4.0. Another opened the doors of her metal additive manufacturing lab to companies interested in trying out the advanced technology. Three of the honorees worked in industry before joining academia, giving them valuable perspective. One started an online company to help parents pick the right STEM-related products for kids, ensuring the children build a solid foundation in the hard sciences. And, speaking of the younger generation, almost all of the honorees list among their top achievements educating and shaping the next cohort of “pioneers and dreamers.”
When mass customization via smart manufacturing is realized, that will be the end of blisters from new shoes, itchy plaster casts and seeing someone else wearing your outfit, Kwok said. His remark may be somewhat lighthearted, but this professor’s attitude toward smart manufacturing and democratizing customization is serious.
“I have been working on automating the customization process since 2009, and my accomplishments are twofold: computational design methods to customize products efficiently, and advanced manufacturing technologies to deal with changes of every product effectively,” he said. As a researcher, Kwok has committed to generate new fundamental knowledge on necessary design and manufacturing techniques for smart manufacturing. He has proposed a new concept of information reuse for mass customization. Additionally, he has published scientific articles on several new tools for smart manufacturing that he helped develop. These include layered depth-normal image, DesignForFab, and cross-parameterization.
As for translating academic work into industrial applications, Kwok said he is directly helping manufacturers who want to employ smart manufacturing but don’t know how.
Vlasea’s focus is on metal additive manufacturing, with a sub-focus on binder jetting and powder bed fusion. She trains students in metal AM and is the co-research director of the Multi-Scale Additive Manufacturing Lab at Waterloo, where industry can explore a wide range of metal additive manufacturing tech and digital manufacturing. “In the field of metal additive manufacturing, industry often finds it challenging to explore technology adoption on their own,” she said. “This is in part due to the high costs required to set up and run equipment, as well as the need to have the right trained personnel to successfully execute R&D.”
She collaborates closely with industry while pursuing research to address the high material and process-optimization costs associated with metal AM, as well as causes of the high rate of part failure and gaps in signal and data analytics. In 2015, she designed an open platform laser powder bed fusion test bed at the National Institute of Standards and Technology.
Vlasea is an active volunteer with SME and her university’s chapter of Women in Engineering.
Ragai worked in industry 23 years before joining academia. With his unique experience, Ragai established one of the first Design-for-Manufacturing platforms. It tracks manufacturing processes’ data and correlates the data to the initial state of a part. The resulting information—usually about internal defects, dimensional accuracy and mechanical behaviors—is then integrated into a design tool for stress analysis and fatigue-life prediction. “It proved to be successful in minimizing defects and repair costs, as well as increasing simulation accuracy and hence product reliability,” he said.
One of his current research interests is to integrate legacy equipment into Industry 4.0, using the least invasive techniques possible. He’s an active member of SME and the American Society of Mechanical Engineers. His advice to students is to research different aspects of manufacturing. “Many new graduates continue researching the same topics without exploring/identifying other areas that would complete the puzzle,” he said. “Either work in industry for some years to grasp the bigger picture or team up with other members from the academic community with different areas of expertise.
While Tao has a list of his research interests related to smart manufacturing and Industry 4.0, the digital twin is the most prevalent. He explores and constructs the digital twin theory and technology system, and recently founded the publication platform “Digital Twin” with Taylor & Francis Group. He published books on design manufacturing. In 2017, he and scholars from 12 universities jointly initiated a yearly meeting called Conference on digital twin and smart manufacturing service. Last year, it attracted more than 300 academics and manufacturers from 70 universities, institutes and manufacturers. The fifth conference is scheduled for July, in Shanghai. In order to improve the efficiency and stability of manufacturing service collaboration, he established smart manufacturing service collaboration theories, which consolidated the theoretical foundations of the smart manufacturing service system. Yet to be done? “There are no criteria for digital twin modeling and data fusion, and it lacks theoretical support,” he said. “Therefore, a set of theories and methods about the digital twin’s precise modeling and data fusion, as well as model and data-driven digital twin operation, are expected to be established in the future.”
Kong’s Smart Manufacturing Analytics Research and Technology (SMART) lab has developed multiple smart AM platforms for metals and polymers. These platforms use multi-physics and heterogenous sensors to collect data in real-time for online process monitoring and control. Based on these platforms, the lab has used advanced data analytics and machine learning to implement a number of online, in-situ, sensor-based methods to detect and mitigate the onset of AM process defects. Kong and colleagues have also created methods using offline measurement data for quality inspection of AM parts. Additionally, they devised a layer-wise spatial porosity model to quantify the porosity distribution based on CT scan data. Their most recent achievement is the creation of a novel methodology that fuses multiple-image data streams with various spatial-temporal resolutions. That methodology is making it possible to collect data in an affordable way—rather than amassing it via expensive sensors. His research has been sponsored by federal agencies and industry. Kong has received several large-scale grants supporting smart manufacturing education, and he has updated and created smart manufacturing-related curriculum at Virginia Tech.
Sutherland wants sensors to do more. In smart manufacturing, sensors can collect data on real-time key performance indicators, such as process throughput. But sensed data can also provide people making decisions with information related to environmentally relevant performance measures, such as energy consumption, resource use and waste creation, he said. “This data can be processed through AI algorithms to provide insights into how to reduce waste and increase energy and resource efficiency at the operation, shop floor, and enterprise levels—all focused on moving manufacturing toward the goal of sustainability,” he said.
Sutherland and his students have focused on collaborating with companies to create innovations related to sustainable manufacturing that avoid waste and better use material resources and energy. “As we move into the future, we want to use smart manufacturing technologies as a means to help our industry partners excel in terms of both traditional productivity and environmentally oriented performance measures,” he said. A member of SME longer than 30 years, Sutherland said his involvement in the North American Manufacturing Research Institution of SME has been a major part of his career.
Wu developed cloud computing-based system architecture and data-driven predictive modeling tools that enable smart manufacturing. “The predictive modeling tools we developed can predict the mechanical properties of 3D printed parts, cell viability in bioprinting and melt-pool temperature in metal additive manufacturing,” he said. He wants to develop novel, physics-informed machine learning algorithms that combine machine learning and physical laws that govern manufacturing processes. In the future, the knowledge extracted by physics-guided AI techniques will enable machines equipped with low-cost sensors to make real-time decisions automatically, he predicted. He also developed novel fog-computing-based, real-time monitoring systems that detect faults in manufacturing equipment and machines. Wu is a highly cited researcher in smart manufacturing, with more than 3,300 citations, according to Google Scholar. He is associate editor of two top-ranked smart manufacturing journals. He is on the editorial board of another journal. And he has guest-edited special editions related to Industry 4.0. In addition, he has organized symposiums, workshops and special sessions on Industry 4.0, cloud manufacturing and machine learning-enabled smart manufacturing.
Among her top career achievements, Sikora counts making students aware of good jobs in manufacturing that they did not know existed. She is doing the same, and more, with another top achievement: helping to lead development of a $50-million manufacturing engineering technology building at her school. “Our vision was to not only offer the usual training but to go many steps further, offering training to students who may have never ever heard of some of the equipment we put in the building,” she said.
For example, students have access to several high-end microscopes, including a scanning electron microscope. “That will enable our students to look for production problems and learn the associated analysis to solve those problems,” she said. That’s not all Sikora has achieved. This fall, a completely revamped curriculum that she helped write will be implemented.
She advises potential academics to work in a factory along with getting a degree. “You’ll have a great career on either path. But if you learn to make things first, you’ll be a better teacher,” she said. “And you’ll have lots of stories to tell.”
To make better robots for enhancing productivity and safety in smart manufacturing, Howard looks at how people move and act. “My focus has been on designing methods for safe human-robot collaboration,” she said. “This involves the creation of methods to model human kinematics and behaviors, as well as developing learning methods based on human inputs.” Her most recent work concerns the interactions between humans and autonomous machines. In 2017, for example, she published a paper titled “Effect of robot performance on human–robot trust in time-critical situations,” which explored the long-term impact a robot’s performance can have on user trust.
Howard recently left Georgia Institute of Technology to be dean of the College of Engineering at The Ohio State University. Prior to joining the faculty at Georgia Tech, Howard served as a senior robotics researcher and deputy manager in the Office of the Chief Scientist at NASA’s Jet Propulsion Laboratory at the California Institute of Technology, from 1993 to 2005. She also founded Zyrobotics. It is focused on helping parents find the right STEM-related products for their children—to ensure they have a solid foundation in the hard sciences.
Shih sees himself and his colleagues in academia as the “pioneers and dreamers to explore new opportunities in smart manufacturing.” One of the dreams he is working on is creating a distributed cyber manufacturing system that whittles to one day from weeks the time it takes to make orthotics and other personalized assistive devices. The day would include a physical exam, design, 3D printing and evaluation of the resulting device. “We need software and low-cost AM machines,” he said. “I am working on both in research.” Shih’s team has also developed devices that are in the pre-clinical stage for grinding calcified plaque from inside blood vessels and for cutting and removing blood clots that could lead to heart attacks and strokes. His research and education interests are in manufacturing, particularly in 3D printing of custom orthosis and prosthesis. He is also interested in developing soft, flexible assistive devices and robotics, clinical simulators and tissue-mimicking materials, drilling and grinding for industrial and medical applications, and novel medical devices. His advice for future academics? “Fundamentals are important, and it takes persistence and time to build fundamental knowledge,” he said.
To hear from Shih in this video, go to 1:16: https://tinyurl.com/smShih
Jun’s research is helping to solve an engineering problem hindering Industry 4.0: What to do about legacy machine tools that still have years of life left in them but were not designed to provide the data needed to improve operations.
“I am currently involved with supporting smart manufacturing research at small and medium enterprises,” he said. “A number of machines at these enterprises have been connected to the network and IoT devices, and sensors have been utilized to collect machine and process data. The real-time data and data analytics results are provided to the appropriate people at each company in order to help with their decisions.” Jun has also held workshops and demonstrations to educate operators, engineers and managers at small and medium-size factories on IoT and smart manufacturing. In addition, with a stethoscope-like sensor he invented, Jun automated the ability of longtime factory workers to tell, by listening to a machine tool in action, whether things are running smoothly. The sensor and AI determine a machine tool’s process status and detect any anomaly in the process.
To hear from Jun in this video, go to 15:06: https://tinyurl.com/smJun
Desai is a researcher and educator in cyber nano/bio manufacturing, additive manufacturing, multiscale modeling, biomanufacturing 4.0, product design and realization. He counts educating the next generation of the design and advanced manufacturing workforce among his biggest accomplishments. His research includes development of a smart cyber agent using artificial neural networks that has made it possible for designers, customers and manufacturers to dynamically allocate digital designs to different manufacturing techniques over the cyber network. Desai developed a cloud-based cyber Design-for-Manufacturing framework that includes automated feature extraction, material-process compatibility checks and analytic hierarchy process algorithm-based decision-making. Desai would like to create a national network of scientists, engineers and academicians to catalyze advanced manufacturing’s impact on practical applications. He is currently investigating smart manufacturing concepts within regenerative tissue engineering to create stem-cell based organs.
To watch a video of Desai, go to: https://tinyurl.com/smDesai
Hu has focused his research on assembly and manufacturing systems, including modeling and diagnosis of multi-stage assembly systems and development of smart-assembly and joining technologies. In 1995, he led an industry-university project on “intelligent resistance welding,” sponsored by the NIST Advanced Technology Program and several U.S. automakers and suppliers. The project developed neural-network-based techniques for predictive quality monitoring and control in resistance spot welding. He has subsequently expanded his smart manufacturing research to gas metal arc welding and ultrasonic welding, including welding lithium-ion batteries. Hu is a co-founder of a startup based on his research, and he has worked closely with several industry partners to enhance their manufacturing quality and productivity.
He believes manufacturing systems will grow to be smarter with the availability of large volumes of process and system data, high-speed communications and fast computing. He also believes in the role of humans in smart manufacturing—where consumers will play an increasingly important role in component design and fabrication so as to create products that fit individual needs and preferences.
With his diverse research portfolio, his commitment to research and teaching and the impact he has had opening up new avenues of research, Kumara could be called the father of smart manufacturing in the current era, said his nominator. “His most influential works, between 1989 and 2000, on using non-linear dynamics in complex systems, with specific reference to sensor-based, real-time monitoring, laid the foundations for real-time on-line quality control,” the person told Smart Manufacturing. “His work in network-based supply chain analysis in the early 2000s has established de facto standards for supply chain resilience analysis.”
In the classroom, “Kumara was among the first researchers to start teaching undergraduate and graduate courses in databases, artificial intelligence and data analytics applied to manufacturing,” another nominator said. “He co-edited one of the earliest books on AI in manufacturing in 1990.” Kumara said he’s confident his group was among the first to address the interdisciplinary nature of smart manufacturing. “Therefore, in addition to process knowledge, having a good mathematical, statistical and advanced computing background is critical,” he said. “Ignoring one for the other will be producing more research that will be not useful.”
Joining the military opened up a world of possibilities for Baker. It led to undergraduate education at the U.S. Naval Academy (USNA), 22 years of working with nuclear power, earning a doctorate from the Naval Postgraduate School and a teaching career. In his first year of teaching, Baker was the mentor for a capstone project on AM. The USNA had no facility for students to work with AM at the time, but he changed that. “My most significant accomplishment in AM has been the creation of MakerSpace USNA,” he said. “Now we have over 30 different printers offering a wide range of capability.” Because of the significant interest in AM in the Navy as a whole, it is now implemented in the USNA core curriculum for all majors, including non-engineers. For those students who are serious about the technology, the academy offers elective courses in AM and CAD/CAM. “I know it can be a trite statement, but if you make it, they will come,” he said. “I have absolutely no problem getting my students interested in 3D printing, AM, or design.”
Van Til helped recession-proof the careers of some of his graduates. In 2007, he had the foresight to create a program in industrial and systems engineering (ISE). In the current, pandemic-catalyzed recession, an ISE degree is one of only eight “projected to be great ones to target in the current economic climate,” according to U.S. News & World Report. The department offers bachelor’s and master’s degrees in ISE and a doctoral degree in systems engineering. “In close cooperation with the department’s Industrial Advisory Board, ISE faculty created a curriculum to emphasize smart manufacturing and Industry 4.0,” Van Til said. “The ISE department has created a series of hands-on courses that focus on the operation and application of various digital twin tools.” Anecdotal evidence of the program’s success includes a Southeast Michigan-based automotive OEM that hired a graduate as its first “digital manufacturing engineer.” In addition, a German Tier One supplier hired an ISE graduate to work in its Detroit-area engineering center—as the first “digital industrial engineer” in the company’s global operations.
To watch a video of Van Til, go to: https://tinyurl.com/smVanTil
When a person in France interacted with cyber-physical components and people in Oklahoma and Wisconsin through the IoT-based, cyber-physical framework for distributed, collaborative manufacturing that Cecil’s team built, Cecil felt a measure of success. “Our framework supported cyber tasks, including genetic algorithm-based assembly planning, virtual reality simulation-based plan comparison and validation and manufacturing instruction generation and physical tasks,” he said. “Such tasks included assembly, monitoring and feedback of target micro-assembly steps using robots, work cells, cameras and other sensors.” This framework was created to support the assembly of micro devices. It adopted a software-defined networking approach to support the collaborative interactions between software, equipment and users. Cecil’s group is also exploring the role of 3D virtual/mixed reality-based digital twins, which can serve as a vital link between distributed cyber and physical worlds. His next objective for his cyber-physical manufacturing collaboration would cover more distance than that from Europe to the United States. “My specific interest is in designing next-generation, cyber-physical approaches that will enable astronauts to interact fully, using mixed reality and other interfaces, with robots, work cells and other manufacturing resources and achieve self-sufficiency on a future lunar base,” he said.
Huang began his research in smart manufacturing in the mid-1990s with internet-based, collaborative product design and manufacturing. He continued in the mid-2000s with a focus on RFID/IoT-enabled, real-time, smart manufacturing systems. His team developed the AUTOM smart manufacturing platform, which has been used in more than a dozen industrial companies for daily operations, resulting in significant benefits in terms of productivity, quality and costs. The AUTOM platform was further developed into a more comprehensive IoT platform, including factory and e-commerce logistics, and was used to make prefab modules for buildings. His lab has developed and applied IoT and digital twin technologies and solutions to establish real-time, cyber-physical information visibility and traceability. “But existing decision models for production planning, scheduling and execution have not yet made adequate use of these features,” he said. He is currently leading a team in new approaches and models for the design and operation of products, processes and systems in the context of the digital twin. His priority is to utilize spatial-temporal visibility and traceability to tackle uncertainty and achieve resilient smart manufacturing systems.
Shin would like to see closer collaboration between industry and academia, and he has made significant strides in that direction. “Many scientific tools developed in academia need to see the daylight in industry,” he said. “In order to make this happen, I want to make various digital models more computationally efficient and robust so that they can be easily adopted in industry.” He has done extensive research and development of in-process monitoring techniques and digital simulation models for laser-based manufacturing processes, including AM, machining, micromachining, shock peening and surface heat treatment. Shin played a pioneering role in the development and application of laser-assisted machining, which is now adopted around the world. He also established the Center for Laser-Based Manufacturing at Purdue, with participation from major manufacturers like Boeing, Rolls-Royce, General Electric and Lockheed Martin. Shin has also worked on various AI-based strategies to optimize and control manufacturing processes. For example, he and four company partners developed the generalized intelligent grinding advisory system (GIGAS) with funding from the National Institute of Standards and Technology. GIGAS reduces the cost of various grinding processes.
When Hovanski rewrote the undergraduate and graduate curriculum for smart manufacturing at Brigham Young, he used a “show, don’t tell” approach. “He developed coursework and laboratories that teach students how to use ‘sprints,’ or rapid implementation of focused principles, in order to realize value in the enterprise with the implementation of Industry 4.0,” said the person who nominated him for this list of luminaries. Students are empowered to build and understand digital twins within the digital thread as they use computer-aided design to empower data-rich, augmented reality experiences. “My most significant accomplishment in this area is developing a curriculum that will empower students to find value in any factory—using the principles and practices of smart manufacturing,” said Hovanski, who worked in industry 15 years before joining academia. He sees manufacturing processes finally moving away from the artisan-level skills of an expert to quantifiable, repeatable processes that can be integrated with the digital enterprise. “We finally are seeing the tools, sensors, controls and data integration to really close the loop on so many parts of the manufacturing process,” he said.
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