Authors:

Nathon Tubbs

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About Paper:

Industry 4.0 is a rapidly advancing and expanding field as manufacturers across the globe begin to adopt intricate technologies to improve industrial practices. AI-in-manufacturing is a leading-edge technology to gain quality, efficiency, and productivity but requires more process data than ever. By comparing recently established systems in accordance with Industry 4.0 and the traditional manufacturing observations, we realized that the study is needed to explore the challenges associated with the implementation of IoT Sensors on industrial machinery. First, an analysis was conducted on a local manufacturing site to determine a relation between operational process data, such as downtime events and maintenance repair order logs, and the recorded controlled process data collected by sensors on the machinery. The process data across the sixteen machines at the manufacturer were analyzed primarily on the root cause of the heater element failures in their oven processes. This practice was then replicated on the campus of Purdue University in Birck Nanotechnology Center. We installed ACCUENERGY current transducers (CTs) and an IoTa Watt Electricity Monitoring system onto a fluid jetting printer to collect an operational fingerprint for the system. Such information is not only useful to identify operational/material defects in the process but also to save energy consumption. These inexpensive and modern IoT sensors technologies have proven to add the monitoring capability on to the currently existing machineries. Through further testing and documentation, this IoT monitoring can evolve into a deep understanding of the processes as well as the basis of AI machine leaning in manufacturing.

Source:

Purdue University / 2023

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Co-authors:

Nathon Tubbs