Fast fingerprinting & detection of materials using portable / hand-held devices and high performance computing for use in manufacturing and supply chain applications.

Process industries have a pressing need to assess the state of materials at each stage in their supply chains – starting with raw materials at a vendor location and ending with the finished goods at customer sites. Such assessments entail the measurement of material properties like chemical composition and physical properties. Today’s assessments (done in expensive analytical laboratories) are largely based on cumbersome, intermittent manual sampling, which is expensive, time consuming, and error-prone. This project addresses DOE’s interest in turn-key solutions advancing the use of HPC in manufacturing supply chains. Our innovative idea is to use ML (machine learning) algorithms running on HPC servers to process spectra signals gathered from handheld infrared sensors for rapid and frequent material identification/estimation in supply chains. Our overall Phase I and Phase II objective is to bring to market a cutting-edge, HPC-based material identification application which produces accurate near real-time readings. The confluence of HPC, ML, and handheld sensors is at the center of our innovative approach and our project will be the first one to accomplish this. We will leverage the extensive expertise in ML, material sensing, and spectral data analysis from Rutgers University, which is our partner in this STTR project. In Phase I, we will demonstrate feasibility by developing ML algorithms which use data from handheld off-the-shelf IR sensors for material identification and prove the concept on a HPC test-bed. We will leverage our relationships with our industrial partners Constellation Brands (a large wine, spirits, and beer producer), Johnson & Johnson (a pharmaceutical co.), and Viavi (maker of IR sensors) to address real world material identification challenges (letters of support are attached). We will focus on estimating product quality at the blending location in a large winery site in our Phase I work. We will demonstrate end-to-end performance on our test-bed: data gathering, ML, and the use of the resultant models for quality estimation. Commercial Applications & Other Benefits: The envisioned commercial application will be used to support fast decision-making in supply chains to produce better quality & safer products. It is projected that companies will save millions of dollars by reducing supply chain costs and product recalls.