An estimated 10% to 20% of tools are changed out too soon to avoid failure during cutting. Edge computing is giving machine tool builders and manufacturers the tool they need to avoid such unnecessary expense.
Traditional maintenance programs are based on machine hours or predetermined intervals. Machine builders typically responded after a failure had occurred, with precious production hours – or days – ticking agonizingly by as service teams investigated the problem and waited for replacement parts.
Reducing unplanned downtime is key to profitability. Remote machine monitoring (RMM) helps by collecting and transmitting data to the cloud in real time, enabling machine builders, operators, and shops to respond much more quickly to problems.
The Holy Grail of data is the ability to use it to prevent breakdowns. With predictive maintenance, tool builders can review the data, detect exceptions to historical patterns, and – ideally – service the machine before a occurs. In addition to increasing overall equipment effectiveness (OEE), this saves shops money by eliminating the need to keep replacement parts on hand.
Challenges and Opportunities
Those are upsides of the Industrial Internet of Things (IIoT), but there are other considerations when entering the arena:
Data integrity due to the remote access.
Cybersecurity for the network.
The RMM software’s ability to work with different machine brands; manufacturers not wanting to use different IIoT solutions for different machines.
First, a myriad of PLCs, controllers, and sensors are available to control machines and track variables like temperature, pressure, or strain. RMM systems must be able to connect to a variety of devices with proprietary protocols and data formats.
Second, most manufacturers don’t want software companies integrating with their internal networks via ethernet or Wi-Fi. Machines that weren’t designed to connect to the internet would be exposed over an open network, and many manufacturers don’t have the proper security in place to protect data from being stolen or equipment from being hacked.
Fortunately, manufacturers and machine tool builders have options: Software as a solution (SaaS) providers address these issues by encrypting data and transmitting it independently of a shop’s IT network.
for example, gathers data from a small computer (customers call it “the Little Green Device”) that connects to any modern CNC machine tool control system via an ethernet port. The platform’s software adaptors automatically unlock, map out, collect, and standardize data points (internal sensors, machine status, modes, alarms, overrides, load, speeds, feeds, etc.), bypassing the need for additional sensors and providing a scalable solution customers can easily install and configure. The data is instantly and securely streamed to the cloud via ethernet, Wi-Fi, or cellular communication.
The platform can be installed on older machines by adding the external sensors necessary to connect digital and analog input/output (I/O) that can be configured and managed remotely through a web interface.
The Cloud Gets a Speed-Enhancing Boost
The platform is able to provide virtually instantaneous feedback by using edge computing in addition to cloud computing.
In a traditional Internet of Things (IoT) scenario, a piece of hardware collects, encrypts, and sends data from the machine tool to a central network server – the cloud, which processes the data.
All that happens very quickly, but not fast enough to produce the information necessary for predictive maintenance activities.
First, it takes time for data to travel from the edge device to the cloud. This slight delay is only milliseconds, but it can be critical for certain decisions, such as stopping a machine tool from breaking.
Secondly, machines produce hundreds of datapoints every millisecond. All that data traveling back and forth between the edge and the cloud strains communication bandwidth.
Rather than constantly sending every piece of data back to the cloud, edge-enabled devices gather and process data in real time right there, at the “edge” of the machine, allowing operators to respond faster and more effectively.
Any equipment builder, OEM or distributor can install the device on new and existing machines to connect to the machine’s PLC and any additional sensors into the electrical cabinet. Once installed, the provider adds the device to a list of machine assets accessible via the Service app and associate that machine with the customer’s location. Once the customer powers up the machine, it appears on the provider’s list of assets.
The platform’s analytics engine monitors various data points and initiates an action, such as a text notification, when a limit is exceeded or other anomaly in machine health is detected. A rules engine is provided for deploying monitors based on condition data; machine-learning algorithms detect anomalous behavior.
Machine builders can use the data to improve customer service in two ways:
With data showing how their equipment’s being used, they can tweak machine design to improve the customer’s experience.
To create and deploy optimized preventative maintenance schedules tied to calendar time, usage time, or initiated from machine conditions. Tasks can be assigned through a workflow to track the customer’s maintenance plan to better plan inventory and service needs.
BY STEPHANIE JOHNSTON
