Three Leading Examples of Advanced Manufacturing Technology

Driving a Digital Transformation with Advanced Manufacturing Technology

Factories of days past were very static environments. You had the building, the equipment, the workers, and the quota. Punch the timecard, meet the day’s quota, and head home for dinner. While revolutionary in their time, this type of factory fares poorly in modern culture with constantly fluctuating demands, an expectation of speed, and fierce competition and collaboration alike. Things move faster now—information, goods, machinery. Everything.

Traditional manufacturers implement advanced manufacturing technology in order to stay agile. It provides them with versatility, the ability to flex and bend to market demands, and more effectively and efficiently utilize their resources. This strategy reduces risk—a rigid company could fall apart in the face of crisis or reduced demand, whereas an advanced manufacturer can adapt to whatever the world throws at them.

Which technologies are these advanced manufacturers using that gives them such an advantage? While there are many, below are three important pieces of the equation.

 

Machine Learning

Machine learning benefits advanced manufacturers at basically every level of business, from demand forecasting to operations to production to maintenance and everything in between. 

Machine learning analyzes data to find patterns that it then learns from and contextualizes.

It can accurately predict expected demand in order to set production goals, boost the efficiency of machine utilization, analyze machine data to determine when parts are going to break before a human operator is able to notice, and more. Machine learning, a subset of artificial intelligence, has become a staple of any truly competitive, data-driven advanced manufacturer.

 

Edge Computing

Edge computing helps resolve the issue of having far too much data to reliably and time-efficiently transfer to a data center for analysis. By deploying devices at the “edge” of a system that can offer some degree of filtering and computing before sending the relevant information to the cloud for further analysis, manufacturers are able to achieve faster response times, especially in facilities that use many industrial IoT devices.

Edge computing also makes the technology used in smart factories scalable. Even with massive data throughput, edge devices offer unprecedented scalability, allowing for edge analytics use cases.

This technology is used for a variety of manufacturing use cases including condition-based monitoring, predictive maintenance, precision monitoring and control, virtual reality in production facilities, and Manufacturing-as-a-Service.

 

High-Frequency Data Collection

Traditional—if you can call it that—IoT sensors collect data, but at a speed that doesn’t always show the whole picture when it's time for analysis.

“Imagine you’re trying to learn a new tune on the piano, but the sheet music only has one note out of every ten. Wouldn’t that be pretty hard?

That’s what it’s like learning what your machine is doing with data that only plays a few notes from the entire piece.” – Lou Zhang, MachineMetrics 

High-frequency data adapters, however, allow for a data capture rate of 1000 points per second (1 kHz). With this level of granularity, data can show far more predictable trends, especially when paired with machine learning technology.

Unlike traditional IoT sensors, this sensorless data device can withstand hostile manufacturing environments such as those involving caustic chemicals or flying debris. Whereas a sensor may need to be recalibrated due to variables present in most any manufacturing environment, this type of high frequency data adapter utilizes information directly from the machine’s computer and is not subject to the need for calibration, replacement, or voiding the warranty of expensive manufacturing equipment. This alternative is scalable, reliable, accurate, and cost effective, whereas old-school IoT sensors are none of the above.

The MachineMetrics High Frequency Data Adapter is simple to DIY install and utilizes edge computing devices and, when relevant, machine learning to derive the most meaning from your most important data. This scalable solution can be deployed on dozens of pieces of equipment that all utilize only one edge device. This technology stack enables predictive maintenance, tooling optimization, diagnostics, and quality optimization in one affordable solution. The full MachineMetrics industrial IoT platform offers boosts to process optimization and production monitoring. Want to see how it could work for you? Book a demo.