Implementing an open-source sensor data ingestion, fusion, and analysis capabilities for smart manufacturing

Kerry Wang, Parth Dave, Abhishek Hanchate, Dinakar Sagapuram, Gautam Natarajan, Satish T.S. Bukkapatnam

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)893-901
Number of pages9
JournalManufacturing Letters
Volume33
DOIs
StatePublished - Sep 2022
Externally publishedYes

Keywords

  • open-source
  • real-time analysis
  • sensor fusion
  • smart manufacturing

ASJC Scopus subject areas

  • Mechanics of Materials
  • Industrial and Manufacturing Engineering

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