Consumer demands for mass customization and private branding are increasing manufacturing complexity, necessitating higher levels of flexibility, speed and accuracy in production and throughout the supply chain. To withstand these market pressures and achieve tangible outcomes to stay competitive, manufacturers of all sizes in diverse industries are investing in Industry 4.0 technologies such as analytics, additive manufacturing, artificial intelligence and robotics.1
While robotic automation and peripheral technologies are effectively helping manufacturers overcome production pitfalls in diverse ways, the growing population of interconnected robots, conveyors, sensors and more are creating new challenges. In addition to obtaining the knowledge to properly implement and use these devices for the purposes they were designed, manufacturers need an effective way to understand how they are functioning.
Machine designs are becoming more sophisticated, and they are capable of producing enormous amounts of data. While this is good news for companies looking to manage risks and enhance operations via data-driven optimized planning, accessing the pertinent data to make these value-added decisions has traditionally been a challenge. From choosing what robots and devices should be monitored and managed to determining what type of data warrants collecting and how to do it, there is much to consider.
Before the Industrial Internet of Things (IIoT) and Industry 4.0, machines operated independently without connectivity, and equipment monitoring on the factory floor was a reactionary, labor-intensive process that required time-consuming manual intervention. The presence of real-time data to make predictions for maintenance or repairs was non-existent, making the process highly inefficient. This also created a communication disconnect between production floor workers and company decision makers.
The technology boom of the 1990s, along with the adoption of the World Wide Web, enhanced networking capabilities, where machines could be monitored over local or wide area networks. These early systems provided simple alerts, sounding alarms if a machine was overheating or had failed. While this method was an improvement, it was still limited to the determination of whether a machine was up or down.
As technology evolved and networks grew, so did the capability for individual companies to produce actionable machine data that could be monitored internally and remotely. The use of hardware agents (drivers, adapters, monitoring terminals, etc.) generated a plethora of operational data, but accessing it or making sense of it was a bit more complicated as the information gathered was most likely only accessible by an individual vendor’s specific software tool.
The uptick in usage for robots, CNC machines, conveyors and more, and their subsequent monitoring devices, has created factories full of incompatible tools, requiring complex monitoring and costly management. As the population of interconnected devices has grown, so has the need for a universal solution to visually oversee all automation tools required for production.
To address this issue and enable organizational leaders to successfully overcome the challenge being presented on their shop floors, robot OEMs, like Yaskawa Motoman, have successfully developed factory automation monitoring systems that are extensible, offering a single point of consolidation solution that can monitor, accumulate and visually deliver operational data for networked production environments.
Inspired by Industry 4.0 principles, edge server systems, like Yaskawa Cockpit, reside between the LAN and external Internet resources; hence, on the “edge.” Furthermore, mechatronic engineering, information and communications technology, along with digital solutions, help facilitate an integrated, intelligent and innovative approach, allowing users to see what is happening at any point on the value creation chain to gain actionable insights for informed decisions.
Manufacturers that implement factory automation monitoring systems are provided with several key functions:
User-friendly asset management: The data generated by these platforms is processed, stored and presented locally through a simple browser-based interface, offering decision makers the ability to access all operational and production volume data in real-time from one location. Easy-to-understand dashboards provide basic information and utilize color-coding to indicate equipment status.
If needed, users can click on a given status to gain detailed performance information. Unlike conventional machine visualization tools, platforms like this are enriched by key performance indicators, aggregated operational data, backup functionalities and more that can help identify potential improvements to enhance productivity.
Real-time alarm notifications and event logging: In the case of severe problems or production stops, every second counts. These monitoring systems monitor and track relevant and concentrated information to instantly alert personnel when alarms occur, bringing immediate attention to potentially critical issues. In addition, access into device event logs can reveal details of device-level operations.
Preventive maintenance analysis: A key aspect of a single-point-of-consolidation solution is the maintenance sub-system, which visually displays the overall health of an operational system, as well as individual components, making it easy to determine when to perform preventive maintenance tasks. Furthermore, the software in these solutions can track critical components, like the torque values of a speed reducer. When applied, the useful insights gained can eliminate downtime and improve productivity.
Robust data management and system integrity: Complete solutions provide additional functions for maintaining the integrity and robustness of the robot management system itself. This is done through performance and resource monitoring, as well as data backups for the robot controllers.
Convenient monitoring of third party devices: While robot OEM factory automation monitoring systems will automatically support their own brand of robots, other brand devices throughout an extended enterprise can be incorporated via add-on functions. Well worth the investment, this enables manufacturers to integrate all components into a single system, providing a centralized view of all operations.
Standard communication architecture: Easily adaptable, these systems use a standard Open Platform Communication Unified Architecture (OPC-UA) interface to connect heterogeneous systems across an extended enterprise for sharing of data pertaining to equipment performance, operational trends and historical analysis.
As mentioned, factory automation monitoring systems deliver value for various scenarios. Device monitoring allows decision makers like production managers, plant operators and shift supervisors to see the status of all devices from anywhere within or external to the facility, providing greater flexibility by not requiring someone to be physically next to a device to determine if it is functioning properly.
Maintenance personnel can obtain a quick view of when preventive maintenance should be done on various components. This is based upon recommended thresholds. By knowing when a component is approaching the threshold for maintenance, routine maintenance can be scheduled when it is convenient – not when something breaks. This reduces unplanned downtime, increasing production and reducing costs.
Manufacturers can collect data that can be valuable to other systems within the enterprise. Whether it is an ERP system or a manufacturing execution system, the data can be used for additional reporting, planning or analytics.
As more and more shop floor operations become unattended, it is important that supervisory or maintenance personnel are immediately informed when issues arise. Effective systems should be able to send alarm notices immediately as they occur so that the proper people can respond to issues affecting overall equipment effectiveness (OEE).
When it comes to industries that can benefit from the incorporation of factory automated monitoring systems, none are excluded, especially the welding industry. Complex welding workcells integrated with an array of mechanical pieces can easily be tracked, keeping an eye on all welding parameters or consumables at any time. Connected hardware could include:
- Robots – examples of captured data could include speed per axis, time the robot is moving versus the time the robot is idle and an oscilloscope for graphical representation of voltages applied to axes over time.
- Controllers – examples of captured data could include cycle time of the job or part, number of cycles completed, arc starts or weld counts, arc-on time, number of tip changes, hood filter hours remaining, and the average voltage and current of the welding power supply.
- Power supplies and wire feeders – examples of captured data could include arc starts or weld counts, arc-on time, wire feed speed, how much wire was consumed, and average current and voltage.
- Cycle starts, E-stops and door interlocks – examples of captured data could include the number of cycle starts or stops initiated as well as the number of cell entries.
With more complex workcells, data from advanced sensors for environment variabilities (i.e., temperature, pressure and humidity) or mechanical variabilities (i.e., vibration and acceleration) could be captured, as well.
The traditional model of manufacturing is fundamentally changing, prompting a demand for mass product customization, accurate supply chain planning and synchronization, and faster multichannel retail responsiveness.2 To better facilitate this and add value to operations, manufacturers should consider implementing robotic technologies managed by single point of consolidation solutions.
While there is still a considerable amount of skepticism surrounding widespread use of Industry 4.0 technologies with subsequent device connectivity and monitoring solutions for various reasons, the effective and timely utilization of factory automated monitoring systems has the potential to increase the competitive edge for companies of all sizes.3
1. and 2. AI and Robotics Automation in Consumer-driven Supply Chains, The Consumer Goods Forum, 2018
3. The Evolution of Automation, PMMI, 2017