Desire for data: Design & Engineering

Experts, scholars and industrial leaders agree that we have entered the Fourth Industrial Revolution, which is characterized by interconnectedness of devices and the ability to collect seemingly unlimited amounts of data for analysis.

“Internet of things” is jargon that has emerged since the onset of the Fourth Industrial Revolution. IoT is driving new technologies, making connections and communication between devices commonplace in industrial settings. Companies now have access to a volume and quality of data that were not available in the past. As machine tools advance, controls will be able to provide not only a stream of data but the software and apps needed to analyze the data stream.

Our society craves data, which has become an integral part of the modern economy. It is therefore easy to see why manufacturers want data from many sources, including machine tools. Six Sigma, statistical process control and other lean manufacturing concepts rely on a good flow of data to drive improvements, and advances in connectivity thanks to new technologies make it simple for businesses to get data they never had before.

Helpful Data

High-volume manufacturers often survive on lower margins, so minimizing downtime is critical to profitability. A stream of data helps in a few ways. Being connected to a machine tool allows real-time monitoring, and current software lets a person assess machine condition from anywhere. Consider a shop like ours where one worker tends to several machines. Each of our EDMs is dedicated to a specific operation. If one stopped, it would disrupt the entire machining line. Remaining connected to every machine enables us to monitor machine status from a single location. We immediately know when a machine stops, and it can be addressed right away, reducing lost time.

Machines go down for many reasons, whether because of machining processes or mechanical or electrical failures. In all cases, it is necessary to understand when, why and how frequently a machine tool is out of service so corrective actions can be implemented. Systemic issues may not be recognized without a useful data stream. Collecting, sorting and charting data from machine tools lets us see patterns and trends. Finding those allows a maintenance department to predict failures and plan maintenance instead of reacting to failures, which is the main tenet of total productive maintenance.