Visit many manufacturing shop floors today, and you’ll probably see a mix of smart machines and legacy equipment. Ideally, the older systems have been upgraded with manufacturing network communication boards or external sensors connected to a monitoring device to obtain real-time insights from all their machinery. However, some manufacturers never got past the hurdles of using older versions of these devices and traditional connectivity options, leaving them with knowledge gaps that can lead to unnecessary scrap, missed deadlines or worse.
Fortunately, a new generation of lower-cost smart sensors and industry-standard connectivity protocols are making it easier, faster and more affordable to augment legacy shop floor equipment. So, manufacturers readily track the performance and quality metrics that help address potential issues early, control costs, optimize resource utilization and automate manufacturing workflows.
Widely used sensors in manufacturing
Sensors support different types of data collection and enable manufacturers to track various metrics related to productivity, consumption, wear and other factors to monitor and improve their production operations. Here are examples of common sensors and their uses.
A proximity sensor can be used to count parts by detecting an associated machine movement. Another sensor may determine how many feet of raw material are going into the machine. Comparing those two measures can help the team understand their scrap and percentage of loss.
Amperage and pressure sensors can provide production and shear rates. Amperage sensors can be associated with machine speed and force of tooling. Meanwhile, pressure sensors can track metrics like the hydraulic pressure powering a machine. Capturing a standard base of amperages and pressures for each part can determine proper setup, tooling expectations and machine norms. Then variations of these base measurements can determine issues affecting part quality and equipment maintenance.
A vibration sensor measures the amount and frequency of vibration in a machine or piece of equipment. Those measurements can then be used to detect imbalances and other issues to predict maintenance issues. Additionally, having a vibration base measurement while making parts can provide a machine signature for good parts and a healthy machine.
A flow meter can be added to a device to track fluid-based processes, as well as whether a machine is applying the necessary amount of lubricant to the material. This helps to ensure consistent product quality while minimizing waste.
Historically, sensors simply produced counts or measures at pre-set intervals. An intermediary programmable logic controller (PLC) was required to capture data from these sensors; convert it to a usable computer format; and make it available to manufacturing execution system (MES), enterprise resource planning (ERP) and other software used to run manufacturing operations. So, extracting value from these sensors was often complex, time-consuming and expensive. But this is changing with newer alternatives.
Next-generation sensors simplify access to insights
Now we’re seeing a new generation of sensors with built-in artificial intelligence (AI) and support for widely adopted networking communications protocols. Coupled with improved wireless connectivity, these sensors are lowering the cost and complexity of extracting meaningful insights from legacy machine data.
Embedded AI
Embedded AI in sensors is generally basic machine learning that resides on a chip, but it allows manufacturers to gain relevant insights right from the sensor, itself. For example, with a vibration sensor, there’s little value from a six kilohertz readout of the machine vibration at a given time. However, newer, AI-enabled vibration sensors can capture when a vibration is more intense than usual, potentially signaling an issue that impacts machine wear or the parts being produced.
Built-in communications
Built-in support for communications protocols has removed the need for intermediary PLCs to extract data from sensors into a manufacturer’s software and made it easier to mix-and-match different sensors on the shop floor. The two most widely adopted protocols are Message Queuing Telemetry Transport (MQTT) and Open Platform Communications Unified Architecture (OPC UA).
MQTT is an Oasis standard messaging protocol designed for machine-to-machine (M2M) communications and the Internet of Things (IoT). MQTT’s light footprint works well for small devices like sensors, even those running within unreliable networks. Additionally, its publish/subscribe model removes the need to know the internet protocol (IP) addresses of machines, simplifying set-up. Newer sensors that support MQTT serve as the publisher.
OPC UA, published by the OPC Foundation, is an open, platform-independent M2M communication protocol designed for industrial automation and industrial IoT (IIoT). It securely exchanges process data, alarms and historical information from sensors on machines directly to a manufacturer’s software running either locally or in the cloud. Many newer sensors run OPC UA natively.
5G Advanced wireless networking
The advent of 5G Advanced wireless networking has significantly improved the quality and reliability of communications on the shop floor, reducing or even eliminating the need for hardwired Ethernet connections. Importantly, it brings highly reliable, low-power connectivity to wireless sensors and features precision positioning to support real-time tracking.
Extracting ROI from next-generation sensors
The lower cost of using next-generation sensors is translating into tangible returns on investment (ROI) for the business for manufacturers.
For example, one customer uses next-generation sensors to automatically and consistently collect data. This has downgraded the manufacturer’s risk-level and reduced mandatory audits, each costing roughly $30,000, from one per quarter to one per year. Real-time sensor data has also helped to reduce the number of specialized parts that the manufacturer needs to keep in inventory, freeing up financial resources.
Another customer combines modern sensor data from multiple machines into a single collection point. Now, instead of requiring one operator per machine, the manufacturer can assign one operator to 10 machines, saving both time and money.
Conclusion
Next-generation sensors with embedded AI and built-in support for communications protocols, such as MQTT and OPC UA, are making it easier and more affordable for manufacturers to derive the same types of meaningful insights from legacy equipment that they are extracting from smart machines. This, in turn, is providing new opportunities to use knowledge from the entire shop floor to improve efficiency, quality control and profitability.
