Document Type

Project

Lead Author Type

CIS Masters Student

Advisors

Dr. Jonathan Engelsma; jonathan.engelsma@gvsu.edu

Embargo Period

8-23-2018

Abstract

PURPOSE: In many parts of today’s manufacturing process, a product is removed from one manufacturing line and moved to another. Resulting from this move data collection, such as the environmental condition is often collected for the whole process line for a given time range, and not the direct product going through that line at a given moment. Moreover, variable product mix can affect an entire line’s environmental characteristics. Understanding this variability, it would seem logical to measure environmental data at the product level, rather than the process level to ensure product-level compliance within prescribed environmental tolerances. This project encompasses the creation of an Industrial Internet of Things (IIoT) device that monitors, logs and transmits environmental data to a cloud-based database service. The device also keeps track of the product in which it is monitoring, with minimal user interaction. It is a portable device suitable for continued use in a 24x7 manufacturing environment.

PROCEDURES: Research was conducted in three categories; software development, electronics and additive manufacturing. The research was conducted to attain the simplest and most efficient design to measure from multiple sensors in a custom enclosure that was portable.

ANALYSES: Datalogging is used widely in industry today, however the data is typically concentrated on a specific process or process line, rarely at the product level. Additionally, the data stored in traditional loggers require operators to download the data after the process has completed, minimizing the opportunity to catch deviations in the moment. Lastly commercially available data loggers lack the ability to contextualize to the product in which they are measuring and usually only measure one or two senses, such as temperature and humidity.

CONCLUSION: Adding specific part-context to a stream of data that is transmitted live from the assembly line can help identify manufacturing tolerance deviations in real-time. Moreover, the collected data can be analyzed using industry standard tools to further understand patterns to prevent similar defects from occurring.

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