The necessity for educational programs in advanced manufacturing became prominent during the economic crisis in 2007 when the demand of industrial plants was for already trained highly-skilled laborers. To respond to this demand, many advanced manufacturing educational pro-grams, such as mechatronics, were developed in community and technical colleges.
Since it was officially defined in the United States Congress in 2015, Smart Manufacturing (SM) has increasingly been under the spotlight. However, current efforts in deploying SM technologies in the US do not provide a workforce trained to utilize and perform SM technologies and techniques. Graduates of mechatronics and other advanced manufacturing programs remain mostly unaware of the technologies of Smart Manufacturing, such as Internet of Things (IoT) and Cyber Physical Systems (CPS), Industry 4.0 standards, and the capacity and range of applications of additive manufacturing and high-precision subtractive manufacturing technologies from tooling to end-user products.
The programs currently available do not provide workforce training on SM technologies that target community and technical colleges, which supply a significant percentage of the industrial workforce. In the project Smart Manufacturing for America’s Revolutionizing Technological Transformation (SMART2), this gap in workforce training is met by providing the needed training to career technical education (CTE) and STEM educators in mechatronics and engineering technology. This project is a collaborative effort among three institutions and provides professional training for faculty of advanced manufacturing education programs and an online knowledge-base platform for educators and manufacturers, as well as on-ground training work-shops and educational modules.
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Status of Smart Manufacturing in the United States
Programs in deploying Smart Manufacturing technologies have been under development in Germany, European Union, and Korea since 2011. This paper investigates the current status of Smart Manufacturing in the United States, and the trends in its technologies such as Industrial Internet of Things and artificial intelligence in standardized industrial robotics. In many other industrial countries, in particular, in East Asia, clear government policies and strategies exist that provide guidance and orient funding towards SM technologies, such as the “Made in China 2025” industrial policy and the Korean “Manufacturing Innovation 3.0” strategy. Although many efforts exist in the U.S. on the academic and industrial levels, that aim at increasing SM utilization, the efforts remain mostly isolated, and driven by the private sector without a clear guiding policy.
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- Award ID(s):
- 1801120
- NSF-PAR ID:
- 10088811
- Date Published:
- Journal Name:
- 2019 SoutheastCon
- Page Range / eLocation ID:
- 1 to 3
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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In this project, the following products were produced as a result of this project: Smart Manufacturing training workshops Online Educational modules on Smart Manufacturing Industrial speaker short talks that present the State-of-the-art Industrial Applications Peer-reviewed articles were produced. High school and Middle School visits In the hands-on training, we demonstrated the use of code-programmed drones in technical education and Smart Manufacturing (SM). Unmanned aerial and ground vehicle technologies are increasingly finding applications in industrial settings. Training on SM is achieved by using coded drones, with educational modules and a database of technologies and their applications.more » « less
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Recent advancements in sensors, device manufacturing, and big data technologies have enabled the design and manufacturing of smart wearables for a wide array of applications in healthcare. These devices can be used to remotely monitor and diagnose various diseases and aid in the rehabilitation of patients. Smart wearables are an unobtrusive and affordable alternative to costly and time-consuming health care efforts such as hospitalization and late diagnosis. Developments in micro- and nanotechnologies have led to the miniaturization of sensors, hybrid 3D printing of flexible plastics, embedded electronics, and intelligent fabrics, as well as wireless communication mediums that permit the processing, storage, and communication of data between patients and healthcare facilities. Due to these complex component architectures that comprise smart wearables, manufacturers have faced a number of problems, including minimum sensor configuration, data security, battery life, appropriate user interfaces, user acceptance, proper diagnosis, and many more. There has been a significant increase in interest from both the academic and industrial communities in research and innovation related to smart wearables. However, since smart wearables integrate several different aspects such as design, manufacturing, and analytics, the existing literature is quite widespread, making it less accessible for researchers and practitioners. The purpose of this study is to narrow this gap by providing a state-of-the-art review of the extant design, manufacturing, and analytics literature on smart wearables-all in one place- thereby facilitating future work in this rapidly growing field of research and application. Lastly, it also provides an in-depth discussion on two very important challenges facing the smart wearable devices, which include barriers to user adoption and the manufacturing technologies of the wearable devices.more » « less
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Abstract Recent advancements in sensors, device manufacturing and big data technologies have enabled the design and manufacturing of smart wearables for a wide array of applications in health care. These devices can be used to remotely monitor and diagnose various diseases and aid in the rehabilitation of patients. Smart wearables are an unobtrusive and affordable alternative to costly and time‐consuming healthcare efforts such as hospitalization and late diagnosis. Developments in micro‐ and nanotechnologies have led to the miniaturization of sensors, hybrid 3D printing of flexible plastics, embedded electronics and intelligent fabrics, as well as wireless communication mediums that permit the processing, storage and communication of data between patients and healthcare facilities. Due to these complex component architectures that comprise smart wearables, manufacturers have faced a number of problems, including minimum sensor configuration, data security, battery life, appropriate user interfaces, user acceptance, proper diagnosis and many more. There has been a significant increase in interest from both the academic and industrial communities in research and innovation related to smart wearables. However, as smart wearables integrate several different aspects such as design, manufacturing and analytics, the existing literature is quite widespread, making it less accessible for researchers and practitioners. The purpose of this study was to narrow this gap by providing a state‐of‐the‐art review of the extant design, manufacturing and analytics literature on smart wearables—all in one place—thereby facilitating future work in this rapidly growing field of research and application. Lastly, it also provides an in‐depth discussion on two very important challenges facing the smart wearable devices, which include barriers to user adoption and the manufacturing technologies of the wearable devices.
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null (Ed.)High-fidelity characterization and effective monitoring of spatial and spatiotemporal processes are crucial for high-performance quality control of many manufacturing processes and systems in the era of smart manufacturing. Although the recent development in measurement technologies has made it possible to acquire high-resolution three-dimensional (3D) surface measurement data, it is generally expensive and time-consuming to use such technologies in real-world production settings. Data-driven approaches that stem from statistics and machine learning can potentially enable intelligent, cost-effective surface measurement and thus allow manufacturers to use high-resolution surface data for better decision-making without introducing substantial production cost induced by data acquisition. Among these methods, spatial and spatiotemporal interpolation techniques can draw inferences about unmeasured locations on a surface using the measurement of other locations, thus decreasing the measurement cost and time. However, interpolation methods are very sensitive to the availability of measurement data, and their performances largely depend on the measurement scheme or the sampling design, i.e., how to allocate measurement efforts. As such, sampling design is considered to be another important field that enables intelligent surface measurement. This paper reviews and summarizes the state-of-the-art research in interpolation and sampling design for surface measurement in varied manufacturing applications. Research gaps and future research directions are also identified and can serve as a fundamental guideline to industrial practitioners and researchers for future studies in these areas.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SoutheastCon42311.2019.9020330
