Tag, You’re It: Choosing the Right Type of Tags for Your RFID System

Many companies have already discovered the benefits of implementing RFID into their systems. Traceability within the manufacturing process provides a competitive advantage of both efficiency and profitability. RFID tags are a major component of this technology. But it’s important to select the correct type for your specific application. These tags are classified into categories based on how they obtain power and how they use that power. The three categories are as follows:

  • Passive tags
  • Semi-passive tags
  • Active tags

Understanding the difference between these can help narrow down your decision when looking into implementing RFID systems to your process.

Passive tags do not have their own power source. The tag receives power only when the RFID reader is in range. These tags are limited since the power supplied is minimal. The biggest advantages of passive tags are that they are small and inexpensive. They can be useful in specific applications where space is limited. Also, if the environment in which the tag is being placed is harsh, the passive tag may be a good option because it can be cheaply replaced if damaged. Since these tags do not generate power, their read distance of just a few inches to about two feet is much shorter than others. Passive tags are also limited to the amount of data storage they possess. Depending on the application this can be an advantage or disadvantage.

Semi-Passive tags, as the name implies, are similar to passive tags in that they do not have an active transmitter. They still require an RFID interrogator to be in range for the device to work, although the semi-passive tags have their own battery to power the IC. If you are looking for longer read ranges than the passive tag, this could be an option. Since the read range of the passive sensor is solely based on how far away the interrogator can power the device and not the signals coming in, adding a battery unit to the semi-passive tags increases this distance. These distances can range up to 100 feet. Another advantage is the amount of data they can store. These added features do come with added costs. The onboard power supply also makes these tags larger and heavier. The electronics inside the tag are susceptible to harsh environments like high or low temperatures, resulting in shorter lifespans.

Active tags have both a battery and transmitter built within their housings. The typical read range is again increased to around 300 to 750 feet depending on the battery power and the antenna. This allows the tags to store more data with their increased memory capacity. Active tags display the most configurability in comparison to passive and semi-passive tags. They can be set up to conserve battery power when the interrogator is out of range and respond only when the reader is within range. They can also be set up as a beacon, which is when the tag does not wait until it receives a signal from the interrogator. Instead, the active tag can be configured to send the information in set time intervals. Since active tags contain an active transmitter, they can contribute to radio noise. They are also more expensive and usually larger in size and weight due to the increased electronics within its housing.

It’s important when selecting a tag for your RFID system to consider the application needs and the advantages and disadvantages of these different options.

Add Transparency and Traceability with RFID

How can traceability and easy data collection help make the assembly line more transparent and efficient? I’m sure if you ask any manufacturing engineer if being able to track vendor and lot information is going to benefit them in some way, they are going to say yes! All companies have some type of ERP system set up to track parts coming in and products going out, but what goes on between those lines? If a customer reports a missing or faulty component how do you easily know where it came from?  How do you know when the product was made or who made it?

This is where RFID comes in. RFID read/write heads and data collectors can help you track and control production on the assembly. These data collectors or “tags” come in various shapes and sizes. They can be small chips attached to the workpiece carrier or they can even come as a bolt that you screw right into your part. Read/write heads also come in different sizes and have variable read/write distances or frequencies (i.e. low frequency, high frequency, and ultra-high frequency). The read/write heads connect to a processor unit that ties directly back to the PLC. Once the PLC receives this information, it can provide it to the ERP system. This takes all the information on the floor level and makes it available to the management system.

For example, say you have an unexperienced line worker on your assembly line. You are producing large diesel engines and he has the job to put together the pistons at the front of the line. Many times, he snaps the O-rings putting them on. Other times, the rings aren’t put completely in place, but he still sends the engine to the next station. When customers start calling faulty O-rings, you need an easy way to locate the source of the problem.

If you have 10 different lines changing out various engines every day, it could be difficult to narrow down the source of the problem. But if you have RFID read/write heads at each station on the line and a tag on each engine, you can look into your ERP system and track down on which line the engines in question were assembled and who was responsible for putting together the pistons.

You can then determine if the problem was human error or if the cause was due to poor quality parts, and take steps to rectify the situation. If it is determined that the rings are poor quality, you can easily determine every engine that these rings have been used on and recall only those engines. This is just a small example of how RFID can help with transparency on the assembly line. If you are looking for better ways to track inventory, vendors, or just make data more accessible to you and your company, then RFID is your answer!

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?

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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.

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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.

Tracking and Traceability in Mobility: A Step Towards IIoT

In today’s highly competitive automotive environment, it is becoming increasingly important for companies to drive out operating costs in order to ensure their plants maintain a healthy operating profit.

Improved operational efficiency in manufacturing is a goal of numerous measures. For example, in Tier 1 automotive parts manufacturing it is common place to have equipment that is designed to run numerous assemblies through one piece of capital equipment (Flexible Manufacturing). In order to accommodate multiple assemblies, different tooling is designed to be placed in this capital equipment. This reduces required plant floor real-estate and the costs normally required for unidimensional manufacturing equipment. However, with this flexibility new risks are introduced, such as running the machine with incorrect tooling which can cause increased scrap levels, incorrect assembly of parts and/or destruction/damage of expensive tooling, expedited freight, outsourcing costs, increased manpower, sorting and rework costs, and more.

Having operators manually enter recipes or tooling change information introduces the Human Error of Probability (HEP).  “The typical failure rates in businesses using common work practices range from 10 to 30 errors per hundred opportunities. The best performance possible in well managed workplaces using normal quality management methods are failure rates of 5 to 10 in every hundred opportunities.” (Sondalini)

Knowing the frequency of product change-over rates, you can quickly calculate the costs of these potential errors. One means of addressing this issue is to create Smart Tooling whereby RFID tags are affixed on the tooling and read/write antennas are mounted on the machinery and integrated into the control architecture of the capital equipment. The door to a scalable solution has now been opened in which each tool is assigned a unique ID or “license plate” identifying that specific tooling. Through proper integration of the capital equipment, the plant can now identify what tooling is in place at which OP station and may only run if the correct tooling is confirmed in place. In addition, one can then move toward predictive maintenance by placing process data onto the tag itself such as run time, parts produced, and tooling rework data. Collection and monitoring of this data moves the plant towards IIoT and predictive maintenance capabilities to inform key personnel when tooling is near end of life or re-work requirement thus contributing to improved OEE (Overall Equipment Effectiveness) rates.

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For more information on RFID, visit www.balluff.com.

*Source: Mike Sondalini, Managing Director, Lifetime Reliability Solutions, Article: Unearth the answers and solve the causes of human error in your company by understanding the hidden truths in human error rate tables

How flexible inspection capabilities help meet customization needs and deliver operational excellence

As the automotive industry introduces more options to meet the growing complexities and demands of its customers (such as increased variety of trim options) it has rendered challenges to the automotive manufacturing industry.

Demands of the market filter directly back to the manufacturing floor of tier suppliers as they must find the means to fulfill the market requirements on a flexible industrial network, either new or existing. The success of their customers is dependent on the tier supplier chain delivering within a tight timeline. Whereby, if pressure is applied upon that ecosystem, it will mean a more difficult task to meet the JIT (just in time) supply requirements resulting in increased operating costs and potential penalties.

Meeting customer requirements creates operational challenges including lost production time due to product varieties and tool change time increases. Finding ways to simplify tool change and validate the correct components are placed in the correct assembly or module to optimize production is now an industry priority. In addition, tracking and traceability is playing a strong role in ensuring the correct manufacturing process has been followed and implemented.

How can manufacturing implement highly flexible inspection capabilities while allowing direct communication to the process control network and/or MES network that will allow the capability to change inspection characteristics on the fly for different product inspection on common tooling?

Smart Vision Inspection Systems

Compact Smart Vision Inspection System technology has evolved a long way from the temperamental technologies of only a decade ago. Systems offered today have much more robust and simplistic intuitive software tools embedded directly in the Smart Vision inspection device. These effective programming cockpit tools allow ease of use to the end user at the plant providing the capability to execute fast reliable solutions with proven algorithm tools. Multi-network protocols such as EthernetIP, ProfiNet, TCP-IP-LAN (Gigabit Ethernet) and IO-LINK have now come to realization. Having multiple network capabilities delivers the opportunity of not just communicating the inspection result to the programmable logic controller (via process network) but also the ability to send image data independent of the process network via the Gigabit Ethernet network to the cloud or MES system. The ability to over-lay relevant information onto the image such as VIN, Lot Code, Date Code etc. is now achievable.  In addition, camera housings have become more industrially robust such as having aluminum housings with an ingress protection rating of IP67.

Industrial image processing is now a fixture within todays’ manufacturing process and is only growing. The technology can now bring your company a step closer to enabling IIOT by bringing issues to your attention before they create down time (predictive maintenance). They aid in reaching operational excellence as they uncover processing errors, reduce or eliminate scrap and provide meaningful feedback to allow corrective actions to be implemented.

Traceability in Manufacturing – More than just RFID and Barcode

Traceability is a term that is commonly used in most plants today. Whether it is being used to describe tracking received and shipped goods, tracking valuable assets down to their exact location, or tracking an item through production as it is being built, traceability is usually associated with only two technologies — RFID and/or barcode. While these two technologies are critical in establishing a framework for traceability within the plant, there are other technologies that can help tell the rest of the story.

Utilizing vision along with a data collection technology adds another dimension to traceability by providing physical evidence in the form of an image. While vision cameras have been widely used in manufacturing for a long time, most cameras operate outside of the traceability system. The vision system and tracking system often operate independently. While they both end up sending data to the same place, that data must be transported and processed separately which causes a major increase in network traffic.

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Current vision technology allows images to be “stamped” with the information from the barcode or RFID tag. The image becomes redundant traceability by providing visual proof that everything happened correctly in the build process. In addition, instead of sending image files over the network they are sent through a separate channel to a server that contains all the process data from the tag and has the images associated with it. This frees up the production network and provides visual proof that the finished product is what we wanted it to be.

Used separately, the three technologies mentioned above provide actionable data which allows manufacturers to make important decisions.  Used together, they tell a complete story and provide visual evidence of every step along the way. This allows manufacturers to make more informed decisions based on the whole story not just part of it.

What to Ask Before You Build an RFID System to Meet Your Traceability Needs

An industrial RFID system is a powerful solution for reliably and comprehensively documenting individual working steps in manufacturing environments. But an industrial RFID system that meets your application needs isn’t available off-the-shelf. To build the system you need, it is important to consider what problems you hope RFID will solve and what return on investments you hope to see.

RFID can deliver many benefits, including process visibility and providing data needed to better manage product quality. It can be used to improve safety, satisfaction and profit margins. It can even be used to help comply with regulatory standards or to manage product recalls. And RFID can be used in a wide range of applications from broad areas like supply management to inventory tracking to more specific applications. These improvements can improve time, cost or performance—though not typically all three.

It is essential to understand and document the goal and how improvements will be measured to in order to plan a RFID system (readers, antennas, tags, cables) to best meet those goals.

Other important questions to consider:

Will the system be centralized or de-centralized? Will the system be license plate only or contain process data on the tag?

How will the data on the tags be used?  Will the information be used to interface with a PLC, database or ERP? Will it be used to provide MES or logical functionality? Or to provide data to an HMI or web browser/cloud interface?

Will the system be required to comply with any international regulations or standards? If so, which ones: EPC Global, Class 1 Gen 2 (UHF only), ISO 15693, or 14443 (HF only)?

What environment does the system need to perform in? Will it be used indoor or outdoor? Will it be exposed to liquids (cleaning fluids, coolants, machine oils, caustics) or high or low temperatures?

Does the RFID system need to work with barcodes or any other human readable information?

What are the performance expectations for the components? What is the read/write range distance from head to tag? What is the station cycle timing? Is the tag metal-mounted? Does the tag need to be reused or be disposable? What communication bus is required?

With a clear set of objectives and goals, the mechanical and physical requirements discovered by answering the questions above, and guidance from an expert, a RFID system can be configured that meets your needs and delivers a strong return on investment.

Smart choices deliver leaner processes in Packaging, Food and Beverage industry

In all industries, there is a need for more flexible and individualized production as well as increased transparency and documentable processes. Overall equipment efficiency, zero downtime and the demand for shorter production runs have created the need for smart machines and ultimately the smart factory. Now more than ever, this is important in the Packaging, Food and Beverage (PFB) industry to ensure that the products and processes are clean, safe and efficient.

Take a look at how the Smart Factory can be implemented in Packaging, Food, and Beverage industries.

Updating Controls Architecture

  • Eliminates analog wiring and reduces costs by 15% to 20%
  • Simplifies troubleshooting
  • Enables visibility down to the sensor/device
  • Simplifies retrofits
  • Reduces terminations
  • Eliminates manual configuration of devices and sensors

Automating Guided Format Change and Change Parts

  • Eliminates changeover errors
  • Reduces planned downtime to perform change over
  • Reduces product waste from start-up after a change over
  • Consistent positioning every time
  • Ensures proper change parts are swapped out

Predictive Maintenance through IO-Link

  • Enhances diagnostics
  • Reduces unplanned downtime
  • Provides condition monitoring
  • Provides more accurate data
  • Reduces equipment slows and stops
  • Reduces product waste

Traceability

  • Delivers accurate data and reduced errors
  • Tracks raw materials and finished goods
  • Date and lot code accuracy for potential product recall
  • Allows robust tags to be embedded in totes, pallets, containers, and fixtures
  • Increases security with access control

Why is all of this important?

Converting a manufacturing process to a smart process will improve many aspects and cure pains that may have been encountered in the past. In the PFB industry, downtime can be very costly due to raw material having a short expiration date before it must be discarded. Therefore, overall equipment efficiency (OEE) is an integral part of any process within PFB. Simply put, OEE is the percentage of manufacturing time that is truly productive. Implementing improved controls architecture, automating change over processes, using networking devices that feature predictive maintenance, and incorporating RFID technology for traceability greatly improve OEE and reduce time spent troubleshooting to find a solution to a reoccurring problem.

Through IO-Link technology and smart devices connected to IO-Link, time spent searching for the root of a problem is greatly reduced thanks to continuous diagnostics and predictive maintenance. IO-Link systems alert operators to sensor malfunctions and when preventative maintenance is required.

Unlike preventative maintenance, which only captures 18% of machine failures and is based on a schedule, predictive maintenance relies on data to provide operators and controls personnel critical information on times when they may need to do maintenance in the future. This results in planned downtime which can be strategically scheduled around production runs, as opposed to unplanned downtime that comes with no warning and could disrupt a production run.

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Reducing the time it takes to change over a machine to a different packaging size allows the process to finish the batch quicker than if a manual change over was used, which in turn means a shorter production blog 2.20 2run for that line. Automated change over allows the process to be exact every time and eliminates the risk of operator error due to more accurate positioning.

 

 

blog 2.20 3Traceability using RFID can be a very important part of the smart PFB factory. Utilizing RFID throughout the process —tracking of raw materials, finished goods, and totes leaving the facility — can greatly increase the efficiency and throughput of the process. RFID can even be applied to change part detection to identify if the correct equipment is being swapped in or out during change over.

Adding smart solutions to a PFB production line improves efficiency, increases output, minimizes downtime and saves money.

For more information on the Smart Factory check out this blog post: The Need for Data and System Interoperability in Smart Manufacturing For a deeper dive into format change check out this blog post: Flexibility Through Automated Format Changes on Packaging Machines

 

 

Why IO-Link is the Best Suited Technology for Smart Manufacturing

While fieldbus solutions utilize sensors and devices with networking ability, they come with limitations. IO-Link provides one standard device level communication that is smart in nature and network independent. That enables interoperability throughout the controls pyramid, making it the most suitable choice for smart manufacturing.

IO-Link offers a cost effective solution to the problems. Here is how:

  • IO-Link uses data communication rather than signal communication. That means the communication is digital with 24V signal with high resistance to the electrical noise signals.
  • IO-Link offers three different communication modes: Process communication, Diagnostic communication (also known as configuration or parameter communication), and Events.
    • Process communication offers the measurement data for which the device or sensor is primarily selected. This communication is cyclical and continuous in nature similar to discrete I/O or analog communication.
    • Diagnostic communication is a messaging (acyclic) communication that is used to set up configuration parameters, receive error codes and diagnostic messages.
    • Event communication is also acyclic in nature and is how the device informs the controller about some significant event that the sensor or that device experienced.
  • IO-Link is point-to-point communication, so the devices communicate to the IO-Link master module, which acts as a gateway to the fieldbus or network systems or even standard TCP/IP communication system. So, depending on the field-bus/network used, the IO-Link master may change but all the IO-Link devices enjoy the freedom from the choice of network. Power is part of the IO-Link communication, so it does not require separate power port/drop on the devices.
  • Every open IO-Link master port offers expansion possibilities for future integration. For example, you could host an IO-Link RFID device or a barcode reader for machine access control as a part of a traceability improvement program.

For more information, visit www.balluff.com/io-link.