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Manufacturing Data Compiler for Visualization Based on Engineering-Driven Machine Learning

Award Information
Agency: Department of Commerce
Branch: National Institute of Standards and Technology
Contract: 70NANB18H183
Agency Tracking Number: 025-02-08 (FY18)
Amount: $100,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N/A
Solicitation Number: 2018-NIST-SBIR-01
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-07-16
Award End Date (Contract End Date): 2019-01-31
Small Business Information
Ann Arbor, MI 48108
United States
DUNS: 072247088
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Tzyy‐Shuh Chang
 (734) 973-7500
Business Contact
 Tzyy‐Shuh Chang
Phone: (734) 973-7500
Research Institution

There has been substantial development in data analytics. However, the complex mathematical formulation of “big data analytics” is difficult to populate in general manufacturing plants. There is a need for an SPC-like tool to enable the acceptance of the advanced data analytics and its visualization. The MD Compiler, a tool to bridge the gap between data available and the information demanded by the users, is proposed. It will employ a hierarchical approach along with machine learning to complement the knowledge of the process so that the manufacturing data streams generate the informative process indexes that can be formulated quickly and presented to the operators for the illustration of the intended process attribute(s) and/or causal relations. The innovation of this SBIR proposal consists of the integration of the industrial process/product states into the data pipeline and applying unsupervised machine learning to automate the “mapping” processes in the analytics along with the concept of process snapshots. The MD Compiler, once developed, is expected to be a software package with functional modules that can take in the source data and automatically compile the data into informative process indexes for presentation with computing and data acquisition hardware support.

* Information listed above is at the time of submission. *

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