Maximize Your Robotic Welding Productivity with Remote Monitoring Tools

CNC Strategic Communications Specialist

When the first robot went into production, there was most likely a person tasked with its maintenance and performance. Back then, the typical scenario was that the equipment continued to do its task until there was a fault or failure. When that occurred, someone in the area would notify supervision who would in turn notify the assigned maintenance person about the issue. The maintenance person would then investigate the situation, and if able to easily resolve the problem, the equipment was up and running again. If the issue could not be resolved easily, the maintenance worker assessed the situation to determine what parts and tools were needed, and then scheduled the downtime and repair – most likely on top of an already busy workload.

Enter the Internet Age

Fast forward several decades to the internet age, which provided a whole new paradigm shift for factory automation. Industrial robots were designed with the latest internet technology and arriving from the factory with Ethernet ports built into their controllers. Some of the major benefits of Ethernet were its relatively low cost, decent immunity to noise and good data transfer rates that when used properly were very reliable. These features would facilitate having all the robots within a factory included in a Local Area Network or (LAN).

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Using the original down robot example and factor in the connectivity benefits provided by the internet, it became much easier to diagnose and repair a robot. The maintenance person could receive status notifications from a supervisor or the down robot itself via an email. The email might have included details about the program that was running, and the errors posted on the controller. If the root cause of the issue was not easily identified, the maintenance person could remotely log into the robot controller from the PC in their office or anywhere else within the factory, as long as it was on the same network. Navigating through simple webpages made it easy to examine the program and determine where it stopped. Maintenance could also look at the history or trend of various types of errors to identify a possible pattern. In addition, they could look at the numerous states of the I/O to see if a particular clamp was open or to confirm that a part proximity sensor was on. After making an intelligent assessment of the situation, they could then proceed to the physical robot on the factory floor with the correct tools and parts required to address the down robot. Alternatively, if the problem could be resolved without tools, the local machine operator received instructions over a hand radio or phone on how to resolve the issue while the maintenance personnel watched the operator on the teach pendant real time from their PC to guide them through the troubleshooting steps.

Another major advantage of having robots on a network is the ability to easily perform regular system backups. Prior to Ethernet, someone would have to physically go to each robot controller and manually backup the robot on a memory storage device like a floppy disk. Floppy disks were small in capacity and communication rates were slow. It often took over an hour backing up a complete robot system onto multiple “floppy disks”, and the management of multiple disks for multiple robots became a job in itself. Later, with better data transfer speeds, it was possible to use a PC on the network to back up the robots faster, and on a larger, more robust storage device. This allowed backups to be scheduled more frequently during off-shifts or on weekends to capture regular programming updates.

As Ethernet technology progressed, so did its speed, security and reliability. Now, the “local networks” can be part of larger networks and permit access from outside their facility. These improvements ushered in a new era where peripheral equipment such as welding power supplies, fiber lasers, CNC’s and other machine tools could communicate with the robot over Ethernet. Not only can the welding power supplies communicate over Ethernet, now the information from that device is also available to the robot, which in turn, makes the information available to the user through the robot connection. This allows error messages or faults from welding equipment to be posted on the robot and give the user more information to help identify the root cause of the problem. This also minimizes the number of required Ethernet connections to put all of the equipment on a network. 

Artificial Intelligence

Today, we are in the midst of the Fourth Industrial Revolution, also known as Industry 4.0. This revolution capitalizes on the idea that all industrial equipment is communicating over Ethernet and providing real-time data to a collection source for documentation and data analysis. For simplicity, let’s focus on the areas of real-time data processing, machine learning and Artificial Intelligence. Real-time data processing allows you to monitor your welding applications such as process anomalies. The robot, data collector or even the welding power supply in some cases can be configured with predefined limits for each of the various welding processes. For example, if your weld parameters command 24 Volts and a current of 220 Amps you can monitor these values while welding, and set predefined limits. These limits can be set in specific units like Volts or Amps, or a percentage value. When one of the values exceeds the limits for a predetermined time, it can send a notification, stop the process completely, or identify the part for post inspection or possible rework, but ultimately prevents the part in question from getting to your customer.

Not only can the welding power supplies communicate over Ethernet, now the information from that device is also available to the robot, which in turn, makes the information available to the user through the robot connection.

Machine learning can also use real-time data collection and make determinations based on data trends. Using the prior example with data points of 24 Volts and 220 Amps, imagine that after a short period of time, the trend shows about a 2 amp drop at the end of every week. Machine learning could identify the trend, and to help avoid issues, make recommendations like change out the torch tip, replace the wire liner or inspect wire guides for slipping. This data may also show that the wire drive system is starting to pull more amps for a given process, further supporting the notion there is a wire delivery issue pending. Similarly, temperature changes in electric motors can be monitored to indicate when lubrication properties of the oil or grease is starting to break down within the gear box.

The next frontier of the Industry 4.0 movement is the concept of Artificial Intelligence (AI). This is where the real-time data is collected, possibly pre-analyzed by some of the machine learning algorithms, and then further processed using AI. Now a welding system can provide valuable data about the welding equipment and processes as well as the arc welding robot and various supporting devices. The intent is to gather information and use it for predictive maintenance versus preventative maintenance. With all of the available real-time information, we can access the health of the system and not just the individual components. If we were to monitor the temperature of an electric motor and the current used to move that motor, we could create a signature of that motor’s health while doing that task. When the signature is not consistent, AI can make predictions about events like when the bearings might fail (if nothing is done to stop it). This type of prediction allows customers to proactively schedule downtime for maintenance and repairs before the equipment suffers from failures that would stop production. The ability to collect and store system information on remote servers or in the Cloud makes it possible for equipment manufacturers to access analytics and monitor performance offsite. Customers can in turn access that information from anywhere in the world and visualize the health of their individual factories based on machine performance. This information can then be used to assess machine utilization and identify opportunities to in-source more work, further justifying that machinery. Having all of this information consolidated and available on a server or in the cloud allows remote data access using a PC, Tablet or smartphone.  Learn more about FANUC's Zero Down Time (ZDT).

Get the Most from Your Robotic Welding System

When purchasing a robotic welding system, one of the main decision factors is the value that the new system will provide your facility. When that new automation system is setup on your shop floor it needs to operate at peak efficiency with minimal down time and maximum output. Placing your robot on a network makes it available for remote monitoring and robot preventive maintenance. More importantly, it opens up a whole new world in terms of information and benefits that incorporate machine learning and artificial intelligence including eliminating unexpected downtime, optimizing maintenance costs and extending the life of your capital assets – all leading to maximized profits and a competitive advantage.