Chain of Support: The Link to Performance During Emergencies

What businesses do in the face of adversity can expose what they are at their core. Adversity is like a catalyst to an otherwise stable state. It forces a reaction. In a chemical reaction, we can predict how a known catalyst will affect a known solution. However, companies are much more unpredictable.

As automation takes center stage in a world of decreased human to human contact and tighter labor budgets, it is critical to understand who your automation partners really are. Who are the humans behind the brands, and what processes do they have in place to respond to emergencies? In manufacturing, downtime, whether planned or not, must be minimized.

One thing we know for certain about adversity is it will happen. Know how your automation partners will respond to a problem. Have them explain their plan to you before the problem occurs. Them having a plan, and you being aware of it, minimizes the impact on production. You can’t wait until a situation occurs during third shift on a Friday to have the discussion.

Knowing the answers to key questions ahead of time can advert a crisis. Who do you call when you need a replacement part? Are they local? How quickly can they respond? If that first person isn’t available what is my next step? When can someone be available? Can they come on site or will they support remotely? How long will it take to get a replacement part? Do you offer assistance with deployment?

The answers to these questions make up the chain of support for a product. Frankly, these answers are the things that truly delineate automation companies. You can always count on innovative technologies to be released to address quality, conformance and efficiency, but you have to make sure there is a secure chain of support behind those technologies. Companies that can clearly explain what this looks like are the ones who will be around for the long haul. Afterall, it’s what we do in the face of adversity that defines who we are.

Beyond the Human Eye

Have you ever had to squint, strain, adjust your glasses, or just ask for someone with better vision to help read something for you? Now imagine having to adjust your eyesight 10 times a second. This is the power of machine vision. It can adjust, illuminate, filter, focus, read, and relay information that our eyes struggle with. Although the technology is 30 years old, machine vision is still in its early stages of adoption within the industrial space. In the past, machine vision was ‘nice to have’ but not really a ‘need to have’ technology because of costs, and the technology still not being refined. As traceability, human error proofing, and advanced applications grow more common, machine vision has found its rhythm within factory automation. It has evolved into a robust technology eager to solve advanced applications.

Take, for example, the accurate reading, validation, and logging of a date located on the concaved bottom of an aluminum can. Sometimes, nearly impossible to see with the human eye without some straining involved, it is completely necessary to ensure it is there to be able to sell the product. What would be your solution to ensuring the date stamp is there? Having the employee with the best eyes validate each can off the line? Using more ink and taking longer to print a larger code? Maybe adding a step by putting a black on white contrasting sticker on the bottom that could fall off? All of these would work but at what cost? A better solution is using a device easily capable of reading several cans a second even on a shiny, poor angled surface and saving a ton of unnecessary time and steps.

Machine vison is not magic; it is science. By combining high end image sensors, advanced algorithms, and trained vision specialists, an application like our aluminum can example can be solved in minutes and run forever, all while saving you time and money. In Figure 1 you can see the can’s code is lightly printed and overcome by any lighting due to hotspots from the angle of the can. In Figure 2 we have filtered out some of the glare, better defined the date through software, and validate the date is printed and correct.

Take a moment to imagine all the possibilities machine vision can open for your production process and the pain points it can alleviate. The technology is ready, are you?

Figure 1
Figure 1
Figure 2
Figure 2

Reduce the Number of Ethernet Nodes on Your Network Using IO-Link

Manufacturers have been using industrial Ethernet protocols as their controls network since the early 1990s. Industrial Ethernet protocols such as Ethernet/IP, ProfiNet, and Modbus TCP were preferred over fieldbus protocols because they offered the benefits of higher bandwidth, open connectivity and standardization, all while using the same Ethernet hardware as the office IT network. Being standard Ethernet also allows you to remotely monitor individual Ethernet devices over the network for diagnostics and alarms, delivering greater visibility of the manufacturing data.

With Ethernet as the key technology for Industry 4.0 and digitalization, more and more devices will have Ethernet capabilities. Typical industrial Ethernet nodes on a plant floor could include PLC controllers, robots, I/O devices for sensors, actuators, flowmeters, transducers and manifolds. While, it’s great getting all the data and diagnostics of the entire manufacturing process, having every device connected via Ethernet has some downfalls. It can lead to larger Ethernet networks, which can mean more costs in hardware such as routers, switches and Ethernet cables, and some Ethernet software license costs are based on the number of Ethernet nodes being used in the network.

Also, as more Ethernet devices are added to a network, the Ethernet network itself can get more complex. Each individual Ethernet device requires an IP address. If an Ethernet node stopped working and needed to be replaced, an operator would need to know the previous IP address of the device and have quick access to the manual with instructions on how to assign the previous IP address to the new device. Someone must also manage the IP addresses on the network. There will need to be a list of the IP addresses on the network as well as the available ones, so when a new Ethernet device is added to the network, a duplicate address is not use

One way to reduce the number of Ethernet nodes while still getting device data and diagnostics is by using IO-Link for field device communications. IO-Link is an open point-to-point communication standard for sensors and actuators published by IEC (International Electrotechnical Commission) as IEC 61131-9. Since it’s fieldbus and manufacturer independent, there is a long list of manufacturer devices that come with IO-Link. Each IO-Link device can then be brought back to a single Ethernet node, through an IO-Link to Ethernet gateway. Since it’s open technology, there are also multiple manufacturers that make different IO-Link to industrial Ethernet gateways.

On the IO-Link to Ethernet gateway, each channel has an IO-Link master chipset. It is designed to automatically communicate and provide data as soon as an IO-Link device is connected to a port. So, there is no addressing or additional setup required. IO-Link is point to point, so it’s always a single IO-Link device connected to a single port on the gateway using a standard sensor cable. Depending on the number of IO-Link devices to be connected to a single Ethernet node, IO-Link gateways can come in 4, 8 or 16 device channels. This graphic (image 1) shows six IO-Link devices connected to a single 8-channel Ethernet gateway. This gateway then communicates back to the Ethernet PLC controller as a single IP address with a standard Ethernet cable. Without using IO-Link, this might require all six devices to be industrial Ethernet devices. Each device would have its own IP address to set up, along with six Ethernet cables going back to a 6-port managed switch before going to the PLC controller.

 

1
Image 1: Six IO-Link devices connected to a single 8-channel Ethernet gateway.

IO-Link Devices Connected:

  1. Device I/O Hub used to connect to 16 standard discrete sensors/photoeyes.
  2. Valve Manifold used to control up to 24 coils.
  3. Visual Indicator Light
  4. RFID Processor System
  5. Pressure Sensor
  6. IO-Link to Standard Analog (0-10V or 4-20ma) Converter

Error Proof Stamping Applications with Pressure Sensors

When improving product quality or production efficiency, manufacturing engineers typically turn to automation solutions to error proof and improve their application. In stamping applications, that often leads to adding sensors to help detect the presence of a material or a feature in a part being formed, for example, a hole in a part. In the stamping world, this can be referred to as “In-Die Sensing” or “Die Protection.” The term “Die Protection” is used because if the sensors do not see the material in the correct location when forming, then it could cause a die crash. The cost of a die crash can add up quickly. Not only is there lost production time, but also damage to the die that can be extremely costly to repair. Typically, several sensors are used throughout the die to look for material or features in the material at different locations, to make sure the material is present to protect the die. Manufacturing engineers tend to use photoelectric and/or inductive proximity sensors in these applications; however, pressure sensors are a cost-effective and straightforward alternative.

In today’s stamping applications, manufacturing engineers want to stamp parts faster while reducing downtime and scrap. One growing trend in press shops is the addition of nitrogen on the dies. By adding nitrogen-filled gas springs and/or nitrogen gas-filled lifters, the press can run faster and cycle parts through quicker.

Typically, the die is charged with nitrogen before the press starts running parts. Today, many stamping plants rely on an analog dial gauge (image 1) to determine if there is sufficient nitrogen pressure to operate safely. When a new die is set in the press, someone must look at the gauge and make sure it is correct before running the press. There is no type of signal or feedback from this gauge to the PLC or the press; therefore, no real error proofing method is in place to notify the operator if the pressure rating is correct or even present before starting the press. If the operator starts running the press without any nitrogen for the springs, then it will not cycle the material and can cause a crash.

11

Another, likely more significant problem engineers face is a hole forming in one of the hoses while they are running. A very small hole in a hose may not be noticeable to the operator and may not even show up on the analog dial gauge. Without this feedback from the gauge, the press will continue to run and increase the likelihood that the parts will be stamped and be out of specification, causing unnecessary scrap. Scrap costs can be quite large and grow larger until the leak is discovered. Additionally, if the material cannot move through the press properly because of a lack of nitrogen pressure to the springs or lifters, it could cause material to back up and cause a crash.

By using a pressure sensor, you can set high and low pressure settings that will give an output when either of those is reached. The outputs can be discrete, analog, or IO-Link, and they can be tied to your PLC to trigger an alarm for the operator, send an alert to the HMI, or even stop the press. You can also have the PLC make sure pressure is present before starting the press to verify it was adequately charged with nitrogen during set up.

Adding an electronic pressure sensor to monitor the nitrogen pressure is a simple and cost-effective way to error proof this application and avoid costly problems.

How Cameras Keep Tire Manufacturers From Spinning Their Wheels

Tires being transported between the curing presses and the staging area before their final inspection often become clustered together. This jam up can cause imperfections to the tires and damage to the conveyors. To alleviate this problem, some tire manufacturers have installed vision systems on their conveyors to provide visual feedback to their production and quality teams, and alert them when the tires start to get too close together.

A vision system can show you alerts back in your HMI by using inputs and outputs built into the camera or use an IO-Link port on the camera to attach a visual display, for example a SmartLight with audible and flashing alerts enabled. Once you see these alerts, the PLC can easily fix the issue from the program or a maintenance worker or engineer can quickly respond to the alert.

Widespread use of smart vision cameras with various pixel options has become a trend in tire manufacturing. In additional to giving an early alert to bunching problems, vision systems can also capture pictures and data to verify that tires were cleared all the way into final inspection. Although tire machine builders are being asked to incorporate vision systems into their machines during the integration process, it is more likely for systems to be added in plants at the application level.

Vision systems can improve production throughput, quality issues and record production data about the process for analytics and analysis down the road. Remember a tire plant usually consists of these processes in their own large section of the plant and involves many machines in each section:

  • Mixing
  • Tire Prep
  • Tire Build
  • Curing
  • Final Inspection

Each one of these process areas in a plant can benefit from the addition of vision systems. Here are a few examples:

  • Mixing areas can use cameras as they mill rubber and detect when rubber sheets are off the rollers and to look for engraved information embedded in the rubber material for logistics and material flow to the proper processes.
  • Tire Prep can use cameras to ensure all the different strand colors of steel cords are embedded or painted on the rubber plies before going to tire build process.
  • Tire Build can use vision to detect the side-wall beads are facing the right direction and reading the embedded position arrows on the beads before tire plies are wrapped around them.
  • Curing area can use vision to monitor tire clusters on conveyors and make sure they are not too close to each other by using the measuring tool in the camera software.
  • Final Inspection can use vision to read barcodes, QR codes, detect colors of embossed or engraved serial numbers, detect different color markings and shape of the markings on the tire.

The use of machine vision systems can decrease quality issues by pinpointing errors before they make it through the entire production process without detection.

Improve Error Proofing with IO-Link and IoT-Enabled Sensors

Though error-proofing sensors and poka yoke have been around for decades, continuing advancements related to the Industrial Internet of Things (IIoT) are making both more accessible and easier to maintain.

Balluff - The IO-Link Revolution!

Designed to eliminate product defects by preventing human errors or correcting them in real time, poka yoke has been a key to a lean manufacturing process since it was first applied to industrial applications in 1960. Today, error proofing relies far less on manual mechanisms and more on IoT-enabled error proofing sensors that connect devices and systems across the shop floor.

IoT is enabling immediate control of error-proofing devices such as sensors. This immediacy guards against error-proofing devices being bypassed, which has been a real problem for many years. Now, if a sensor needs adjustment it can be done remotely. A good example of this is with color sensors. When receiving sub-components from suppliers, colors can shift slightly. If the quality group identifies the color lot as acceptable but the sensor does not, often the color sensor is bypassed to keep production moving until someone can address it, creating a vulnerable situation. By using IoT-enabled sensors, the color sensor can be adjusted remotely at any time or from any location.

The detection of errors has been greatly improved by integrating sensors directly into the processes. This is a major trend in flexible manufacturing where poka yoke devices have to be adjusted on-the-fly based on the specific product version being manufactured. This means that buttons or potentiometers on discrete sensors are not adequate. Sensors must provide true data to the control system or offer a means to program them remotely. They must also connect into the traceability system, so they know the exact product version is being made. Connections like this are rapidly migrating to IO-Link. This technology is driving flexible manufacturing at an accelerated rate.

IO-Link enables sensors to process and produce enriched data sets. This data can then be used to optimize efficiencies in an automated process, increase productivity and minimize errors.

Additionally, the easily expandable architecture built around IO-Link allows for easy integrations of poka yoke and industrial identification devices. By keeping a few IO-Link ports open, future expansion is easy and cost effective. For poka yoke, it is important that the system can be easily expanded and that updates are cost-effective.

Continuous Improvement Shouldn’t Stop for a Crisis

In any given year, New Year’s resolutions have long gone out the window by April. But this year, we at least have an excuse. 

 

You might have heard about this pandemic we are experiencing. 

 

Our gyms are closed, our refrigerators are full, and we have more streaming options than ever to keep us happily disengaged. So, unless you resolved to wash your hands until they were raw or become a recluse, there is a good chance you are failing. 

 

But COVID-19 hasn’t only impacted our homes and our waistlines; it has made an even more significant impact on our workplaces and how we complete our tasks. Some are now working from home, while manufacturing lines that have been deemed essential have been updated to incorporate additional safety precautions, including increased separation between workers. 

 

Just staying operational can be a struggle with a reduced workforce and increased regulations. So, it is easy to use excuses to explain why we’ve strayed from our commitment to continuous improvement. But even in a crisis, those are just excuses. Continuous improvement must be continuous – even in times of trial. Now is a great time to examine your processes, review your needs, and implement more lean strategies. 

 

Take a Gemba walk to determine what challenges you are facing and determine what you can fix. Eliminate unnecessary processes or process waste that doesn’t add value to the customer. And it is as important as ever, as teams adjust to their new normal, to communicate plainly and make each department’s plan clear and visible.

 

Every crisis can be an opportunity in disguise. (If that isn’t already on a poster with a kitten stuck in a tree, it should be.) Crises can provide a perspective that you didn’t previously have and the motivation you need to make changes to improve your processes. Good management includes optimizing the current situation: What can you do now that you couldn’t before? What doors does this open? How could you be better prepared if this happened again?

 

So, stop with the excuses and get lean.  

Using Data to Drive Plant Productivity

What is keeping us from boosting productivity in our plants to the next level? During a recent presentation on Industry 4.0 and IIoT, I was asked this question.

The single biggest thing, in my opinion, that is keeping us from boosting productivity to the next level is a lack of DATA. Specifically, data about the systems and the processes.

1

Since the beginning of time, we have been hungry for efficiency. While early man invented more efficient methods to hunt and survive, today we are looking for ways to produce more efficiently in our plants with minimum or zero waste. After exhausting all the avenues for lean operations on plant procedures and our day-to-day activities, we are now looking at how we can recover from unanticipated downtime quickly. I am sure in future we will be seeking information on how can we prevent the downtime altogether.

There are plentiful of reasons for downtime. Just a few examples:

  1. Unavailability of labor – something we might be experiencing these days, when the COVID-19 pandemic has reduced some labor forces
  2. Unavailability of raw materials
  3. Unavailability of replacement components
  4. Unavailability of assets
  5. Failures in machines/components

In this list, the first two reasons, are beyond the scope of this blog’s intentions and frankly somewhat out of controls from the production standpoint.

The next two reasons, however, are process related and the last one is purely based on the choices we made. These three reasons, to a certain extent, can be reduced or eliminated.

If the downtime is process related, we can learn from them and improve our processes with so called continuous improvement initiatives. We can only do these continuous improvements based on observable factors (a.k.a. data) and we cannot improve our processes based on speculations. Well, I shouldn’t say “cannot”, but it will be more like a fluke or luck. It is apt to say “ what can’t be measured, can’t be improved!”

A good example for elaborating my point is change-over in the plant to produce a different product. Unless there is a good process in place for ensuring all the change-over points are properly addressed and all the change parts are correctly installed and replaced, the changeover time could and will likely lead to tremendous amounts of lost productivity. Secondly, if these processes are done manually and not automated, that is also a loss of productivity or, as I like to say, an area for continuous improvement to boost productivity based on observable facts. Sometimes, we take these manual change-overs as a fact of life and incorporate that time required as a part of “planned” downtime.  Of course, if you do change-overs once a year – it may be cost effective to keep the process manual even in today’s situation. But, if your plant has multiple short batch productions per day or per week, then automating the changeovers could significant boost productivity. The cost benefit analysis should help prove if it is continuous improvement or not.

Assets are an important part of the equation for smooth operations. An example would be molds in the stamping plant or cutting-deburring tools in metal working plants. If plants have no visibility or traceability of these important assets for location, shape or form, it could lead to considerable downtime. The calibration data of these tools or number of parts produced with the tool are also important pieces of data that needs to be maintained for efficient operations. Again, this is data about the system and the integration of these traceability initiatives in the existing infrastructure.

Failures in machines or components could cause severe downtime and are often considered as unavoidable. We tackle these failures in a two-step approach. First, we hunt for the problem when it is not obvious, and two, we find the replacement part in the store room to change it out quickly. And, as process improvement, we schedule preventative maintenance to inspect, lubricate and replace parts in our regular planned downtime.

The preventative maintenance is typically scheduled based on theoretical rate of failure. This is a good measure, especially for mechanical components, but, predictive or condition-based maintenance usually yields higher returns on productivity and helps keep plants running smooth. Again, predictive maintenance relies on data about the condition of the system or components. So, where is this data and how do we get to it?

Standardization of interfaces is another important component for boosting productivity. In my next blog, I will share how IO-Link as a technology can help address all of these challenges and boost productivity to the next level.

Are machine diagnostics overburdening our PLCs?

In today’s world, we depend on the PLC to be our eyes and ears on the health of our automation machines. We depend on them to know when there has been an equipment failure or when preventative maintenance is needed. To gain this level of diagnostics, the PLC must do more work, i.e. more rungs of code are needed to monitor the diagnostics supplied to the sensors, actuators, motors, drives, etc.

In terms of handling diagnostics on a machine, I see two philosophies. First, put the bare bones minimum in the PLC. With less PLC code, the scan times are faster, and the PLC runs more efficiently. But this version comes with the high probability for longer downtime when something goes wrong due to the lack of granular diagnostics. The second option is to add lots of diagnostic features, which means a lot of code, which can lessen downtime, but may throttle throughput, since the scan time of the PLC increases.

So how can you gain a higher level of diagnostics on the machine and lessen the burden on the PLC?

While we usually can’t have our cake and eat it too, with Industry 4.0 and IIoT concepts, you can have the best of both of these scenarios. There are many viewpoints of what these terms or ideas mean, but let’s just look at what these two ideas have made available to the market to lessen the burden on our PLCs.

Data Generating Devices Using IO-Link

The technology of IO-Link has created an explosion of data generating devices. The level of diversity of devices, from I/O, analog, temperature, pressure, flow, etc., provides more visibility to a machine than anything we have seen so far. Utilizing these devices on a machine can greatly increase visibility of the processes. Many IO-Link masters communicate over an Ethernet-based protocol, so the availability of the IO-Link device data via JSON, OPC UA, MQTT, UDP, TCP/IP, etc., provides the diagnostics on the Ethernet “wire” where more than just the PLC can access it.

Linux-Based Controllers

After using IO-Link to get the diagnostics on the Ethernet “wire,” we need to use some level of controller to collect it and analyze it. It isn’t unusual to hear that a Raspberry Pi is being used in industrial automation, but Linux-based “sandbox” controllers (with higher temperature, vibration, etc., standards than a Pi) are available today. These controllers can be loaded with Codesys, Python, Node-Red, etc., to provide a programming platform to utilize the diagnostics.

Visualization of Data

With IO-Link devices providing higher level diagnostic data and the Linux-based controllers collecting and analyzing the diagnostic data, how do you visualize it?  We usually see expensive HMIs on the plant floors to display the diagnostic health of a machine, but by utilizing the Linux-based controllers, we now can show the diagnostic data through a simple display. Most often the price is just the display, because some programming platforms have some level of visualization. For example, Node-Red has a dashboard view, which can be easily displayed on a simple monitor. If data is collected in a server, other visualization software, such as Grafana, can be used.

To conclude, let’s not overburden the PLC with diagnostic; lets utilize IIoT and Industry 4.0 philosophy to gain visibility of our industrial automation machines. IO-Link devices can provide the data, Linux-based controllers can collect and analyze the data, and simple displays can be used to visualize the data. By using this concept, we can greatly increase scan times in the PLC, while gaining a higher level of visibility to our machine’s process to gain more uptime.

Palletized Automation with Inductive Coupling

RFID is an excellent way to track material on a pallet through a warehouse. A data tag is placed on the pallet and is read by a read/write head when it comes in range. Commonly used to identify when the pallet goes through the different stages of its scheduled process, RFID provides an easy way to know where material is throughout a process and learn how long it takes for product to go through each stage. But what if you need I/O on the pallet itself or an interchangeable end-of-arm tool?

Inductive Coupling

1

Inductive coupling delivers reliable transmission of data without contact. It is the same technology used to charge a cell phone wirelessly. There is a base and a remote, and when they are aligned within a certain distance, power and signal can be transferred between them as if it was a standard wire connection.

2

When a robot is changing end-of-arm tooling, inductive couplers can be used to power the end of arm tool without the worry of the maintenance that comes with a physical connection wearing out over time.

For another example of how inductive couplers can be used in a process like this, let’s say your process requires a robot to place parts on a metal product and weld them together. You want I/O on the pallet to tell the robot that the parts are in the right place before it welds them to the product. This requires the sensors to be powered on the pallet while also communicating back to the robot. Inductive couplers are a great solution because by communicating over an air gap, they do not need to be connected and disconnected when the pallet arrives or leaves the station. When the pallet comes into the station, the base and remote align, and all the I/O on the pallet is powered and can communicate to the robot so it can perform the task.

Additionally, Inductive couplers can act as a unique identifier, much like an RFID system. For example,  when a pallet filled with product A comes within range of the robot, the base and remote align telling the robot to perform action A. Conversely, when a pallet loaded with product B comes into range, the robot communicates with the pallet and knows to perform a different task. This allows multiple products to go down the same line without as much changeover, thereby reducing errors and downtime.