To create value at scale, organizations are selectively implementing a myriad of advanced technologies, creating highly productive workspaces that are boosting throughput while unlocking drastic reductions in operational downtime and costs. A key driver for this success, is the implementation of high-performance robots and their intelligent peripherals, including an influx of internet-connected sensors and devices (inspired by Industry 4.0) for production monitoring and asset management. As a result, complete digital transformations are taking place for businesses of all sizes across the industrial landscape, optimizing production environments and yielding integrated smart factories that better address rapidly evolving demands.
When successfully integrated, robotic automation paired with intuitive auxiliary equipment has the potential to generate multiple productivity gains. That said, the digital data accumulated from these items is highly advantageous and can assist with tracking Overall Equipment Effectiveness (OEE), maintaining process efficiency and monitoring product quality. However, how do decision makers determine what data to collect? From robot controllers, weld interfaces and power sources to 3D perception systems and more, there are various “knowledge goldmines” to consider.
Ideally, companies looking to harness data for dynamic decision making should consider the use of a feature-rich Open Platform Communication Unified Architecture (OPC-UA) interfaces for production monitoring. The successor to the OPC (Object Linking and Embedding for Process Control) standard, the unified architecture of an Ethernet-based OPC-UA provides an intuitive method for device interoperability that is independent of proprietary application programming interfaces (APIs).
Extremely effective OPC-UA platforms, like Yaskawa Cockpit™, facilitate an integrated, intelligent and innovative (i3-Mechatronics) approach to data harvesting that provides a one-to-one topology where clients (aka: devices, sensors, robots, etc.) request data and servers timely respond with that data. Each client can interact with multiple servers and vice versa. Sometimes, a single device may even operate as both a client and a server. If needed, additional functionality is available via the OPC-UA Publish/Subscribe (PubSub) AddOn. Regardless, this method enables manufacturing supervisors and warehouse managers to see what is happening at any point on the value creation chain to gain actionalable insights for customizing operations that can better fulfill company initiatives.
Whether from individual devices or from a robust monitoring tool that allows the flexibility to oversee the health, status and performance of networked production environments, decision makers interested in harnessing the benefits of data utilization should take several action steps before implementation, including:
Defining Operational Goals
Whether the key reasons for data collection center around optimizing OEE, reducing downtime, eliminating bottlenecks, increasing throughput, enhancing quality or something else, having clear-cut organizational goals that help understand the “why” behind the data are important. Quality standards, long-term and short-term objectives, as well as targets for cycles per minute/day/hour, are all things to clarify before data gathering begins.
Roadmapping for Success
Once goals are established, defining how to reach each benchmark should occur. These action items will be based on questions such as, “What data do I need to collect to improve the issue?” and “What process should I use to harvest and analyze the data?”
Pursuing Action Items
Actionable data will enable decision makers to set achievable steps that designated workers can diligently implement to improve the issues at hand. Taking notes and charting progress for each part of the process is also advised. This allows businesses to keep track of what works and what does not, especially if a similar issue resurfaces.
As mentioned, data collection can serve multiple purposes, depending on an organization’s end goals. Where robotic automation is concerned, both production processes and specific applications are benefitting from data analytics, such as:
A robot provides the greatest reliability when it is well-maintained. That said, most Automotive Tier 1 suppliers work diligently to keep robot performance near a 99% efficiency rate. While there are multiple physical tests (i.e., Grease Analysis, Manual Test, Backlash Test, etc.) that can be performed to measure this, generated data can be used to check robot health as well. For example, a Torque Analysis can be performed to measure the torque of the motors. Using generated data, a spike in the results can show an inconsistency in robot motion. Performed at regular intervals, tests such as these are invaluable to robot performance and the prevention of unnecessary downtime.
Utilizing collected data for systematic analysis of equipment can be a huge gamechanger – as the ability to precisely determine when an asset may malfunction or need repair is ideal for streamlining maintenance schedules. Whether using data provided from individual devices or from an extremely reliable state-of-the-art software platform, like Yaskawa Cockpit that easily monitors, accumulates and visually delivers data in real time, it can be extremely beneficial to take the data harvesting leap.
From robotic welding for safety critical welds in the automobile industry to parcel pick and place for e-commerce fulfillment, nearly every industry is experiencing the benefit of robust robotic automation paired with intelligent software, especially for high-volume tasks. The necessity for quality and precision is a motivating factor, and a larger number of companies are choosing to implement smart technologies for improved processes.
To achieve the utmost accuracy for robotic welding, 2D and 3D profile sensors are integrated to offer real-time position tracking, seam tracking, groove detection and weld seam inspection. Not only is the ability to instantly “self-tune” based on data feedback valuable for welding, but also, it is incredibly helpful for other applications. From upstream processes to final loading, companies such as Ambi Robotics and Mujin, offer several solutions with proprietary perception tools that incorporate artificial intelligence (AI). These systems use AI-driven data from a simulation process to effectively teach robots realistic program paths to overcome tough production challenges. Addressing the simulation-to-reality (Sim2Real) gap has already been invaluable to tasks such as bin picking, insertion, piece picking, palletizing and depalletizing, and it is expected to impact more applications in the future.
In many cases, an Overall Equipment Effectiveness analysis is the catalyst for robot implementation. A dimensionless parameter, OEE is the product of three important elements: availability, performance and quality. The metrics provided from calculating these factors is an excellent gauge for measuring current equipment impact. Actionable insights for questions such as, “Am I waiting on a product from another station?” or “Am I starving downstream operations from an equipment or operator standpoint?” can be gained – helping to efficiently pinpoint the exact cause for sluggish throughput or unexpected downtime.
Likewise, production environments that already use robotic automation may also take an interest in OEE. Whether at the individual robot or workcell level, or in regard to an entire automated line, the use of data can be extremely valuable to discovering the top causes for production inefficiency and poor product quality.
As the industrial landscape progresses, data collection from a single device will continue to give way to inclusive monitoring systems that support an array of heterogeneous equipment (including third-party applications). Moreover, companies that implement high-performance robots and sophisticated tools to create smart factories and warehouses will be well-suited to realize greater operational efficiency for a better ROI.
If you are interested in learning more about robotic automation or data-driven optimized planning, reach out to one our Yaskawa experts today.
Tom Stocker is a Director of North American Sales