The Industrial Internet of Things (IIoT) and adjacent technology has had a profound impact on industrial processes, creating opportunities for product and service transformation. The
adoption of robotic technology has further enhanced the IIoT ecosystem, driving growth toward operational excellence. Manufacturers that successfully integrate and synchronize highly efficient robots with their current network of devices have discovered greater productivity as well as quick return on investment.
Deployed in nearly every industry, robots can be used for a wide range of applications, delivering a variety of operational gains such as faster cycle times, increased production volume and improved part quality. When it comes to the IIoT, robotic automation thrives by working in sync with smart technology to facilitate a range of tasks.
Serialization and tracking
Widely used in the automotive and aerospace sectors for the monitoring of safety critical welds, part serialization and tracking are greatly facilitated via robotic automation. Easily fed between connected devices, as well as a central tracking service, serialization data allows manufacturers to identify when and where parts are produced (down to the device level depending on application needs). It also helps to ensure hardware or software failures can be quickly discovered, prompting informed decisions such as part recalls when necessary.
Regardless of a plant’s adherence to a strict weld procedure specification (WPS) – a formal document that describes welding procedures for creating consistently identical welds that are engineered for a particular part – the advantage of robots is that they are programmed to apply the same parameters consistently.
For example, a robot program determines torch angles and travel speed, which are two essential variables of a WPS. Plus, robot controllers can generally store weld settings as global files or in the weld instructions local to a specific program, facilitating better traceability of parts. Likewise, combining multiple levels of connected devices with a centralized data store can promote sitewide traceability and highly specific feedback for a given process.
Parts inspection
In conjunction with data tracking, the advent of connected devices now enables robots to identify potential issues and make informed decisions on the spot. More versatile than costly and inflexible coordinate measuring machines, pairing robotic automation with inspection tools such as cameras and laser sensors is a common use case that can greatly improve part consistency and quality.
Furthermore, the process of inspection provides manufacturers an objective analysis of a process or procedure, versus using fixed criteria to determine if a part process can be verified. These inspection methods can also utilize modern machine learning methods to develop models that are able to adapt to any level of inputs and determine unique points of variation or failure.
Whether inspection is performed to ensure surface quality, weld integrity or part geometry on an in-house part, or if it is carried out to check the caliber of parts being acquired from another section of the supply chain, the process can be very beneficial. Manufacturers can know exactly when a part was completed and by which devices, promoting traceability and enabling management to identify which machines or lines could be producing scrap or bad parts. These processes help optimize quality and quantity aspects of an overall equipment effectiveness rating.
Machine monitoring
Robots and their connected devices (e.g., grippers, scanners and torches) can provide a wealth of data pertaining to equipment performance and operational trends. Transforming this information into actionable insights is key for enabling data-driven optimized planning for preventative and predictive maintenance.
The implementation of a factory automation monitoring system that supports multiple brand devices and collects data in real time is ideal for facilitating this concept. When part quality decreases or if severe defects are found, IIoT monitoring systems, along with parts tracking systems, can be used to look up and determine the point of failure.
Proven edge server solutions, like the Yaskawa Cockpit, use an OPC-UA interface to enable an integrated, intelligent and innovative approach to data analytics. This allows manufacturers to see what is happening at any point on the value creation chain. In turn, this helps company leaders make informed choices that provide the ability to better manage supply chain complexity and execute strategic company goals.
AI-enhanced production
While the IIoT and robotic automation encompass unique technologies, developments in artificial intelligence (AI) are rapidly distorting the lines that separate the two – enter the concept
of the Internet of Robotic Things (IoRT). A relatively new concept, intelligent technology such as AI can be used to monitor then manipulate events. Simultaneously, robot information and device data are combined in real time to calculate an alternative course of action for objects in the physical world.
In essence, the computational power of AI can be used to analyze a wide variety of inputs, and an AI-infused system can make well-informed decisions about how to change the behavior of a robot or workcell. This can refer to changing robot speeds, pathing, tooling or even external device characteristics.
The overall goal here is to gain every bit of performance out of a robotic system. Simply put, a computer is much better at analyzing large sets of data and making accurate yet repeatable decisions than a human worker. It is this capability that is attempting to be leveraged, streamlining industry processes and turning a manufacturing facility into a truly autonomous operation.
Furthermore, these experimental systems seek to better traditional machine monitoring. With a multitude of connected sensors and devices talking in unison, AI-enabled solutions are looking to provide the utmost accuracy when it comes to predicting equipment failure.
New possibilities
The IIoT has caused a technological shift, and the primary capabilities to fulfill many industrial tasks and the ability to keep competitive are changing as a result. The use of robotic automation within the manufacturing supply chain can boost a variety of factors including efficiency, productivity and revenue. To realize greater benefits, company leaders are choosing to implement a range of advanced technologies, including sensors and intuitive platforms, harnessing the data provided to create more cohesive and efficient manufacturing operations.
Whether it be edge-computing or in the cloud, the ability for manufacturers to view, diagnose and react to production changes from nearly any location is highly advantageous. Companies that strategically plan for and successfully integrate the right mix of intelligent tools, while still investing in their current workforce, “risk” opening a world of new possibilities for a more productive future.
