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Fostering Learning and Adaptability of Future Manufacturing Workers with Intelligent Extended Reality (IXR)

This project imagines the future of work in precision manufacturing where the spatial and causal reasoning and decision-making abilities of workers on complex production and inspection tasks are augmented through teaming with intelligent extended reality (IXR) technologies. Evidence suggests that the newer wave of automation in manufacturing is not so much to replace workers but rather to complement human work to increase precision, safety, and product quality. Yet, U.S. manufacturers are not adequately addressing the changing nature of skill requirements, which is anticipated to leave 2.4 million U.S. manufacturing jobs unfilled by 2030. Hence, there is an urgent need for breakthrough technologies that enable workplace-based learning and rapid upskilling on complex, cognitively demanding, and hard-to-automate manufacturing tasks, especially in the post-COVID economy. Our convergent research team will create new technological pathways to enable intelligent worker-XR teaming and advance the fundamental understanding of impacts on both labor economy as well as worker learning and innovation. This project will create new perspectives, methods, and discoveries to unleash the full potentials of all of U.S. workforce and thus, strengthen America’s economic competitiveness and global leadership in precision manufacturing. 


National Science Foundation (2021-2025)


Augmented and Virtual Reality Tools for Workforce Training in Robot-enabled Manufacturing Techniques

The rapid adoption of Advanced Manufacturing Methods is critical to ensure that our armed forces rapidly embrace these capabilities to prepare and transform our aircraft, ships, submarines, and land-based fighting vehicles and ensure that the US maintains an advantage in a new period of global peer competition.  These new technologies, such as robotic-, additive-, and AI-enabled process tools are unfamiliar to manufacturing and maintenance workers and traditional educational methods will not quickly and effectively close this gap.  Therefore, we are proposing to develop Virtual- and Augmented Reality tools to rapidly, consistently, effectively, and quantitatively train maintenance and sustainment workers in the use and mastery of these techniques as they are applied to robotic-assisted cold spray metallization and robotic spray painting.  These AR- and VR-tools will be readily adapted to the future training for other robotic manufacturing methods. As all the content will be posted on the funded Massachusetts Institute of Technology (MIT) Open EdX platform for free consumption, it will be broadly accessible to the manufacturing community.  For robotic solutions, the ARM Institute will accredit the training; other accreditations are being determined.


Department of Defense

Naval Sea Systems Command (NAVSEA)


Training an Agile, Adaptive Workforce for the Future of Manufacturing with Intelligent Augmented Reality (IAR)

The future of the American manufacturing workforce faces a perfect storm of challenges: (1) a shortage of workers due to the retirement of the Baby Boom generation, (2) a shifting skillset due to the introduction of advanced technologies, and (3) a lack of understanding and appeal of manufacturing jobs among younger cohorts. Consequently, over 2.4 million U.S. manufacturing jobs are anticipated to be left unfilled by 2030 with a projected cost of $2.5 trillion on the U.S. manufacturing GDP. Augmented reality (AR) has been recently adopted for experiential training and upskilling of manufacturing workers. AR is proven to reduce new-hire training time by 50% through spatiotemporal alignment of instructions with worker experience. However, evidence suggests that overreliance of workers on AR scaffolds can cause brittleness of knowledge and deteriorate performance in adapting to novel situations. This project will investigate if and how AR can help manufacturing workers develop agility and adaptability on the shop floor while avoiding the risks associated with dependence on technology and stifled innovation. A new intelligent AR system will enable dynamic adjustment of AR instructions to worker task performance and enhance their ability to master complex tasks such as assembly and maintenance. This research will serve the national priority for rapid upskilling of manufacturing workforce, especially underrepresented and under-served minority groups.


From User Reviews to User-Centered Generative Design: Automated Methods for Augmented Designer Performance

This project imagines user-centered design processes where the latent needs of myriad users are automatically elicited from social media, forums, and online reviews, and translated into new concept recommendations for designers. Our motivation stems from the growing abundance of user-generated feedback and the lack of advanced computational frameworks and techniques for turning data into new design knowledge and insights. Recent studies show that 49% of design firms lack systems and tools for monitoring external platforms, and only 8% have adopted digital, AI-driven approaches for new product development despite acknowledging them as a high priority. This project will advance the fundamental understanding of if and how AI can augment the performance of designers in early-stage product development by investigating two fundamental questions: (1) Can we build and validate novel natural language processing (NLP) algorithms for large-scale elicitation of latent user needs with cross-domain transferability and minimal need for manually labeled data? (2) Can we build and validate novel deep generative design algorithms that capture the visual and functional aspects of past successful designs and automatically translate them into new design concepts? Our convergence research team is well-positioned to undertake these questions, with expertise across four disciplines of engineering, computer science, business, and design.


National Science Foundation (2021-2024)

Engineering Design and System Engineering (#2050052)


Advanced Design Augmentation for Socially-Aware Product Design

Advanced Design Augmentation (ADA) Technologies, LLC, offers an end-to-end AI-powered software-as-a-service platform that aims at augmenting designers’ creativity and innovation by eliciting the unmet needs of users from social media, online forums, and e-commerce platforms, and leveraging that information to generate and evaluate new design concepts. The vision of ADA Tech, LLC, is to foster designer-AI co-creation and innovation centered on empathy with users and bias mitigation, to bridge the gap between user need discovery, social impact, and design. ADA aims at augmenting product designers’ performance to innovate “socially-aware” products, by identifying pressing and latent societal needs from social media and e-commerce platforms and automatically generating design concept recommendations for designers informed by those needs.


Northeastern University

Impact Engine

Computer Programming

Developing Integrative Manufacturing and Production Engineering Curricula That Leverage Data Science [Online]

This project will contribute to the national need for well-educated engineers and technicians in production engineering. It will do so by developing modular courses in data science for production engineering. These courses will be developed by Northeastern University in collaboration with MassBay Community College, four Manufacturing USA institutes, and three industry partners. The overall goal of the project is to design, develop, and deploy sustainable, online courses and curricula that bridge the production engineering-oriented data science skills gap of incumbent professional engineers and entering engineers and technicians. The program will address four groups of learners: working professionals who need to upgrade their data science skills; career-transitioning learners without manufacturing backgrounds; undergraduates who wish to minor in data science for production engineering; and two-year community college students preparing for either entering the workforce or a four-year college program. The project plans to develop: 1. A modular production engineering-oriented data science curriculum with seven courses that, in turn, are comprised of modules; 2. An online course/module recommendation system to help students determine which course or module best meets their needs and current skillset; 3. Credentials including certificates and a minor in data science for production engineering. The project aims to address the production engineering-oriented data science skills gap, thus helping to meet the demand for workers in manufacturing, which is estimated to have at least two million unfilled positions between 2018 and 2028.

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