Artificial Intelligence & Machine Learning: Influencing the Modern Factory

During the past several decades, artificial intelligence (AI) and machine learning have become integral in the automation process. To the uninitiated, artificial intelligence is defined as “a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence”. A subset of artificial intelligence, machine learning specifically focuses on giving systems the ability to learn and optimize processes without having to be consistently programmed. Modern factories have fully embraced these two practices and implemented them into the automation process with great success. Below, we’ll discuss the benefits artificial intelligence and machine learning have brought to the modern factory and how these technological feats continue to influence and streamline productivity.

The Basics

While the ideas of artificial intelligence/machine learning and automation are similar, it is important to note that they are not the same thing. Automation performs a task and then thinks no further about the process. For example, when you’re out of the office, you can set up an automated email response. The system relays an email and then the process ends. On the other hand, machine learning constantly intakes new data and allows for smart changes and alterations to systems or processes.

So how do modern factories utilize machine learning in the automation process? 

You may not be surprised to learn that there are a myriad of different ways and processes in which machine learning is used in modern factories. Here are just a couple of examples:

1. Predictive Maintenance: Maintenance represents a substantial part of any modern factory’s expenses. Machine learning now provides the ability to actively mitigate potential work stoppages or shutdowns for maintenance. By utilizing machine learning algorithms with real-time data from the production floor (via sensors, PLCs, etc.) and archived, contextual data (via ERP, quality, MES, etc.), factory operators can have a transparent picture of current processes and events. This clarity allows operators to forecast any potential mishaps down the line and predictively maintain equipment without completely halting operations.
2. Predictive Quality Analytics: Through machine learning, we can monitor the quality of the output the factory is producing. Additionally, the AI can predict product quality deterioration. By analyzing and monitoring constant influxes of data, machine learning can be used to prevent the wasting of raw materials and production time, thereby saving your company money in the long run.

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