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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.
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Implementing an open-source sensor data ingestion, fusion, and analysis capabilities for smart manufacturing
In many modern enterprises, factory managers monitor their machinery and processes to prevent faults and product defects, and maximize the productivity and efficiency. Asset condition, product quality and system productivity monitoring consume some 40-70% of the production costs. Oftentimes, resource constraints have prevented the adoption and implementation of these practices in small businesses. Recent evolution of manufacturing-as-a-service and increased digitalization opens opportunities for small and medium scale companies to adopt smart manufacturing practices, and thereby surmount these constraints. Specifically, sensor wrappers that delineate the specifications of sensor integration into manufacturing machinery, with appropriate edge-cloud computing and communication architecture can provide even small businesses with a real-time data pipeline to monitor their manufacturing machines. However, the data in itself is difficult to interpret locally. Additionally, proprietary standards and products of the various components of a sensor wrapper make it difficult to implement a sensor wrapper schema. In this paper, we report an open-source method to integrate sensors into legacy manufacturing equipment and hardware. We had implemented this pipeline with off-the-shelf sensors to a polisher (from Buehler), a shaft grinding machine (from Micromatic), and a hybrid manufacturing machine (from Optomec), and used hardware and software components such as a National Instruments Data Acquisition (NI-DAQ) module to collect and stream live data. We evaluate the performance of the data pipeline as it connects to the Smart Manufacturing Innovation Platform (SMIP)—web-based data ingestion platform part of the Clean Energy Smart Manufacturing Innovation Institute (CESMII), a U.S. Department of Energy-sponsored initiative—in terms of data volume versus latency tradeoffs. We demonstrate a viable implementation of Smart Manufacturing by creating a vendor-agnostic web dashboard that fuses multiple sensors to perform real-time performance analysis with lossless data integrity.
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- Award ID(s):
- 1757882
- PAR ID:
- 10321337
- Date Published:
- Journal Name:
- Manufacturing letters
- ISSN:
- 2213-8463
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- The DOI is not currently available.
