Using Guided Format Change to Improve Changeover and Productivity

Long before Covid, we were seeing an increase in the number of packaging SKUs. In 2019, Packaging Digest reported an estimated 42% increase in SKUs in the food and beverage industry.

Since Covid, there has been a further explosion of new packaging sizes, especially in the food and beverage marketplace. Food manufacturers have gotten very creative. Instead of raising prices due to the higher costs of goods, for example, they can reduce the size of product packages while keeping the consumer prices the same.

Many of today’s production machines are not equipped to changeover as quickly and as accurately to meet the demand of the marketplace. Manufacturers now face the challenge of “semi-automating” their existing machines, as opposed to procuring new machines or adding expensive motors to existing machines. One solution is to digitalize change points on existing machines.

Companies are looking to reduce the amount of time and the mistakes that occur when doing product changeovers. Allowing for operator guidance and position measurement can reduce your time and enhance your accuracy of those changeover events. Measurements are then tied to the recipe and the operator becomes the prime mover.

Guided Format Change

There are lots of technologies out there for helping with guided format change, such as automated position measurement, machine position, distance measurement, linear measurement, and digitalized rotary encoders.


As you are likely quite aware, there are often scales, marks, etc., written onto machines that don’t provide the greatest degree of accuracy. Introducing digitalized position and distance sensing can help you reduce time and limit errors during changeovers.

Change Part Identification

The other side of changeover is change part identification. Quite often during this process parts on the machine must be exchanged. Using the wrong change part can result in mistakes, waste, and delays, and can even damage existing machines.

Technologies, such as RFID, can help ensure the correct change part is chosen and added to the machine. During a recipe change, the operator can then validate that all the correct parts are installed before the startup of the next product run.

Guided format change is a cost-effective way to reduce changeover time and increase productivity either by retrofitting your existing machines or even new machines.

Security in the World of the Industrial Internet of Things

The Industrial Internet of Things (IIoT) is becoming an indispensable part of the manufacturing industry, leading to real-time monitoring and an increase in overall equipment effectiveness (OEE) and productivity. Since the machines are being connected to the intranet and sometimes to the Internet for remote monitoring, this brings a set of challenges and security concerns for these now-connected devices.

 What causes security to be so different between OT and IT?

Operational Technology (OT) manufacturing equipment is meant to run 24/7. So, if a bug is found that requires a machine to be shut down for an update, that stop causes a loss in productivity. So, manufacturers can’t rely on updating operational equipment as frequently as their Information Technology (IT) counterparts.

Additionally, the approach of security for OT machines has largely been “security through obscurity.” If, for example, a machine is not connected to the network, then the only way to access the hardware is to access it physically.

Another reason is that OT equipment can have a working lifetime that spans decades, compared to the typical 2-5-year service life of IT equipment. And when you add new technology, the old OT equipment becomes almost impossible to update to the latest security patches without the effort and expense of upgrading the hardware. Since OT equipment is in operation for such a long time, it makes sense that OT security focuses on keeping equipment working continuously as designed, where IT is more focused on keeping data available and protected.

These different purposes makes it hard to implement the IT standard on OT infrastructure. But that being said, according to Gartner’s 80/20 rule-of-thumb, 80 percent of security issues faced in the OT environment are the same faced by IT, while 20 percent are domain specific on critical assets, people, or environment. With so many security issues in common, and so many practical differences, what is the best approach?

The solution

The difference in operation philosophy and goals between IT and OT systems makes it necessary to consider IIoT security when implementing the systems carefully. Typical blanket IT security systems can’t be applied to OT systems, like PLCs or other control architecture, because these systems do not have built-in security features like firewalls.

We need the benefits of IIoT, but how do we overcome the security concerns?

The best solution practiced by the manufacturing industry is to separate these systems: The control side is left to the existing network infrastructure, and IT-focused work like monitoring is carried out on a newly added infrastructure.

The benefit of this method is that the control side is again secured by the method it was designed for – “security by obscurity” – and the new monitoring infrastructure can take advantage of the faster developments and updates of the IT lifecycle. This way, the operations and information technology operations don’t interfere with each other.

Choosing Between M18 and Flatpack Proxes

Both M18s and flatpacks are inductive or proximity sensors that are widely used in mechanical engineering and industrial automation applications. Generally, they are similar in that they produce an electromagnetic field that reacts to a metal target when it approaches the sensor head. And the coil in both sensors is roughly the same size, so they have the same sensing range – between 5 to 8 millimeters. They also both work well in harsh environments, such as welding.

There are, however, some specific differences between the M18 and flatpack sensors that are worth consideration when setting up production.

M18

One benefit of the M18 sensor is that it’s adjustable. It has threads around it that allow you to adjust it up or down one millimeter every time you turn it 360 degrees. The M18 can take up a lot of space in a fixture, however. It has a standard length of around two inches long and, when you add a connector, it can be a problem when space is an issue.

Flatpack

A flatpack, on the other hand, has a more compact style and format while offering the same sensing range. The mounting of the flatpack provides a fixed distance so it offers less adjustability of the M18, but its small size delivers flexibility in installation and allows use in much tighter fixes and positions.

The flatpack also comes with a ceramic face and a welding cable, especially suited for harsh and demanding applications. You can also get it with a special glass composite protective face, a stainless-steel face, or a steel face with special coatings on it.

Each housing has its place, based on your detection application, of course. But having them both in your portfolio can expand your ability to solve your applications with sensor specificity.

Check out this previous blog for more information on inductive sensors and their unlimited uses in automation.

Predictive Maintenance vs. Predictive Analytics, What’s the Difference?

With more and more customers getting onboard with IIoT applications in their plants, a new era of efficiency is lurking around the corner. Automation for maintenance is on the rise thanks to a shortage of qualified maintenance techs coinciding with a desire for more efficient maintenance, reduced downtime, and the inroads IT is making on the plant floor. Predictive Maintenance and Predictive Analytics are part of almost every conversation in manufacturing these days, and often the words are used interchangeably.

This blog is intended to make the clear distinction between these phrases and put into perspective the benefits that maintenance automation brings to the table for plant management and decision-makers, to ensure they can bring to their plants focused innovation and boost efficiencies throughout them.

Before we jump into the meat of the topic, let’s quickly review the earlier stages of the maintenance continuum.

Reactive and Preventative approaches

The Reactive and Preventative approaches are most commonly used in the maintenance continuum. With a Reactive approach, we basically run the machine or line until a failure occurs. This is the most efficient approach with the least downtime while the machine or line runs. Unfortunately, when the machine or line comes to a screeching stop, it presents us with the most costly of downtimes in terms of time wasted and the cost of machine repairs.

The Preventative approach calls for scheduled maintenance on the machine or line to avoid impending machine failures and reduce unplanned downtimes. Unfortunately, the Preventative maintenance strategy does not catch approximately 80% of machine failures. Of course, the Preventative approach is not a complete waste of time and money; regular tune-ups help the operations run smoother compared to the Reactive strategy.

Predictive Maintenance vs. Predictive Analytics

As more companies implement IIoT solutions, data has become exponentially more important to the way we automate machines and processes within a production plant, including maintenance processes. The idea behind Predictive Maintenance (PdM), aka condition-based maintenance, is that by frequently monitoring critical components of the machine, such as motors, pumps, or bearings, we can predict the impending failures of those components over time. Hence, we can prevent the failures by scheduling planned downtime to service machines or components in question. We take action based on predictive conditions or observations. The duration between the monitored condition and the action taken is much shorter here than in the Predictive Analytics approach.

Predictive Analytics, the next higher level on the maintenance continuum, refers to collecting the condition-based data over time, marrying it with expert knowledge of the system, and finally applying machine learning or artificial intelligence to predict the event or failure in the future. This can help avoid the failure altogether. Of course, it depends on the data sets we track, for how long, and how good our expert knowledge systems are.

So, the difference between Predictive Maintenance and Predictive Analytics, among other things, is the time between condition and action. In short, Predictive Maintenance is a stepping-stone to Predictive Analytics. Once in place, the system monitors and learns from the patterns to provide input on improving the system’s longevity and uptime. Predictive Maintenance or Preventative Maintenance does not add value in that respect.

While Preventative Maintenance and Predictive Maintenance promises shorter unplanned downtimes, Predictive Analytics promises avoidance of unplanned downtime and the reduction of planned downtime.

The first step to improving your plant floor OEE is with monitoring the conditions of the critical assets in the factory and collecting data regarding the failures.

Other related Automation Insights blogs:

Looking Into & Through Transparent Material With Photoelectric Sensors

Advance automated manufacturing relies on sensor equipment to ensure each step of the process is done correctly, reliably, and effectively. For many standard applications, inductive, capacitive, or basic photoelectric sensors can do a fine job of monitoring and maintaining the automated manufacturing process. However, when transparent materials are the target, you need a different type of sensor, and maybe even need to think differently about how you will use it.

What are transparent materials?

When I think of transparent materials, water, glass, plexiglass, polymers, soaps, cooling agents, and packaging all come to mind. Because transparent material absorbs very little of the emitted red LED light, standard photoelectric sensors struggle on this type of application. If light can make its way back to the receiver, how can you tell if the beam was broken or not? By measuring the amount of light returned, instead of just if it is there or not, we can detect a transparent material and learn how transparent it is.

Imagine being able to determine proper mixes or thicknesses of liquid based on a transparency scale associated to a value of returned light. Another application that I believe a transparent material photoelectric senor would be ideal for is the thickness of a clear bottle. Imagine the wall thickness being crucial to the integrity of the bottle. Again, we would measure the amount of light allowed back to the receiver instead of an expensive measurement laser or even worse, a time-draining manual caliper.

Transparent material sensor vs. standard photoelectric sensor

So how does a transparent material sensor differ from a standard photoelectric sensor? Usually, the type of light is key. UV light is absorbed much greater than other wavelengths, like red or blue LEDs you find in standard photoelectric sensors. To add another level, you polarize that UV light to better control the light back into the receiver. Polarized UV light with a polarized reflector is the best combination. This can be done on a large or micro scale based on the sensor head size and build.

Uses for transparent material sensor include packaging trays, level tubes, medical tests, adhesive extrusion, and bottle fill levels, just to name a few. Transparent materials are everywhere, and the technology has matured. Make sure you are looking into specialized sensor technologies and working through best set-up practices to ensure reliable detection of transparent materials.

Tire Industry Automation: When a Photo-Eye Is Failing, Try an Ultrasonic Sensor

Should you use a photo-eye or an ultrasonic sensor for your automation application? This is a great question for tire industry manufacturing.

I was recently at a tire manufacturing plant when a maintenance technician asked me to suggest a photoelectric sensor for a large upgrade project he had coming up. I asked him about the application, project, and what other sensors he was considering.

His reply was a little startling. He said he had always used photo-eyes, but he couldn’t find a dependable one, so he would continually try different brands. My experience in this industry, along with good sensor training and advice from my colleague Jack Moermond, Balluff Sensor Products Manager, immediately made me think that photo-eye sensors were not the right choice for this application.

As I asked more questions, the problem became clear. The tire material the technician was detecting was black and dull. This type of material absorbs light and does not reflect it reliably back to the sensor. Also, environmental factors, such as dust and residue, can diminish a photo-eye’s signal quality.

Ultrasonic sensors for non-reflective materials and harsh environments

The technician didn’t have much experience with ultrasonic sensors, so I went on to explain why these may be a better solution for his application.

While photoelectric sensors send light beams to detect the presence of or measure the distance to an object, ultrasonics bounce sound waves off a target. This means that ultrasonics can be used in applications where an object’s reflectivity isn’t predictable, like with liquids, clear glass or plastic, or other materials. Dust build up on the face of an ultrasonic sensor does not give a false output. Ultrasonic sensors actually have a dead zone a few millimeters from the face where they won’t detect an object until the wave clears the dead zone, so take this into consideration when planning where to install an ultrasonic sensor.

Tire detection for process reliability with BUS ultrasonic sensors

Tire industry applications

The following are some popular tire industry applications where it might be better to choose an ultrasonic sensor over a photo-eye sensor.

    • The tire building process requires a lot of winding and unwinding of material to build the different layers of a tire. As this material is fed through the machines it starts to sag and loop. An ultrasonic sensor in this location will monitor how much sag and loop is in the process.
    • When tires are being loaded into curing presses, the press needs to confirm that the correct size tire is in place. An ultrasonic sensor can measure the height or width of the tire from the sides or top for confirmation.
    • Ultrasonic sensors are great at detecting if a tire or material is in place before a process starts.
    • Hydraulic systems are common in tire manufacturing. Ultrasonic sensors are good for hydraulic fluid level monitoring. Tying them to a SmartLight offers a visual reference and alarm output if needed.
    • Everyone knows the term “wig-wag” in tire mixing and extrusion. The sheets of wig-wag require monitoring as they are fed through the process. When this material gets close to being used up, a new wig-wag must be used.
Wig-wag stacks

So, when there is an application for a photo-eye, especially in a tire manufacturing plant, keep in mind that, rather than a photoelectric sensor, an ultrasonic may be a better option.

The maintenance technician I spoke with is now looking at different options of ultrasonics to use. He said I gave him something new to think about for his machines and opened the door for adding this technology to his plant.

Happy hunting!

Picking Solutions: How Complex Must Your System Be?

Bin-picking, random picking, pick and place, pick and drop, palletization, depalletization—these are all part of the same project. You want a fully automated process that grabs the desired sample from one position and moves it somewhere else. Before you choose the right solution for your project, you should think about how the objects are arranged. There are three picking solutions: structured, semi-structured, and random.

As you can imagine, the basic differences between these solutions are in their complexity and their approach. The distribution and arrangement of the samples to be picked will set the requirements for a solution. Let’s have a look at the options:

Structured picking

From a technical point of view, this is the easiest type of picking application. Samples are well organized and very often in a single layer. Arranging the pieces in a highly organized way requires high-level preparation of the samples and more storage space to hold the pieces individually. Because the samples are in a single layer or are layered at a defined height, a traditional 2-dimensional camera is more than sufficient. There are even cases where the vision system isn’t necessary at all and can be replaced by a smart sensor or another type of sensor. Typical robot systems use SCARA or Delta models, which ensure maximum speed and a short cycle time.

Semi-structured picking

Greater flexibility in robotization is necessary since semi-structured bin picking requires some predictability in sample placement. A six-axis robot is used in most cases, and the demands on its grippers are more complex. However, it depends on the gripping requirements of the samples themselves. It is rarely sufficient to use a classic 2D area scan camera, and a 3D camera is required instead. Many picking applications also require a vision inspection step, which burdens the system and slows down the entire cycle time.

Random picking

Samples are randomly loaded in a carrier or pallet. On the one hand, this requires minimal preparation of samples for picking, but on the other hand, it significantly increases the demands on the process that will make a 3D vision system a requirement. You need to consider that there are very often collisions between selected samples. This is a factor not only when looking for the right gripper but also for the approach of the whole picking process.

Compared to structured picking, the cycle time is extended due to scanning evaluation, robot trajectory, and mounting accuracy. Some applications require the deployment of two picking stations to meet the required cycle time. It is often necessary to limit the gripping points used by the robot, which increases the demands on 3D image quality, grippers, and robot track guidance planning and can also require an intermediate step to place the same in the exact position needed for gripping.

In the end, the complexity of the picking solution is set primarily by the way the samples are arranged. The less structured their arrangement, the more complicated the system must be to meet the project’s demands. By considering how samples are organized before they are picked, as well as the picking process, you can design an overall process that meets your requirements the best.

RFID Basics – Gain Key Knowledge to Select the Best Fit System

As digitalization evolves, industrial companies are automating more and more manual processes. Consequently, they transfer paper-based tasks in the field of identification  to digital solutions. One important enabling technology is radio frequency identification (RFID), which uses radio frequency to exchange data between two different entities for the purpose of identification. Since this technology is mature, many companies now trust it to improve their efficiency. Strong arguments for RFID technology include its contactless reading, which makes it wear-free. Plus, it’s maintenance-free and insensitive to dirt.

RFID basics for selecting the best fit system

There are myriad applications for RFID in the manufacturing process, which can be clustered into the following areas:

    • Asset management e.g. tool identification on machine tools or mold management on injection molding machines in plastic processing companies
    • Traceability for work piece tracking in production
    • Access control for safety and security purposes by instructed and authorized experts to ensure that only the right people can access the machine and change parameters, etc.

But not all RFID is the same. It is important to select the system type and components that are best suited for your application.

Frequencies and their best applications

RFID runs on three different frequency bands, each of which has its advantages and disadvantages.

Low Frequency (LF)
LF systems are in the range of 30…300 kHz and are best suited for close range and for difficult conditions, such as metallic surroundings. Therefore, they fit perfectly in tool identification applications, such as in machine tools, Additionally, they are used in livestock and other animal tracking. The semiconductor industry (front end) relies on this frequency (134kHz) as well.

High Frequency (HF)
HF in the range of 3…30 MHz is ideal for parts tracking at close range up to 400 mm. With HF you can process and store larger quantities of data, which is helpful for tracking and tracing workpieces in industrial applications. But companies also use it for production control. It comes along with high data transmission speeds. Accordingly, it accelerates identification processes.

Ultra High Frequency (UHF)
UHF systems in the range of  300 MHz…3 GHz are widely used in intralogistics applications and typically communicate at a range of up to 6 m distance. Importantly, they allow bulk reading of tags.

RFID key components

Every RFID system consists of three components.

    1. RFID tag (data carrier). The data carrier stores all kinds of information. It can be read and/or changed (write) by computers or automation systems. Read/write versions are available in various memory capacities and with various storage mechanisms. RFID tags are usually classified based on their modes of power supply, including:

– Passive data carriers: without power supply
– Active data carriers: with power supply

2. Antenna or Read/Write head. The antenna supplies the RFID tag with power and reads the data. If desired, it can also write new data on it.

3. Processing unit. The processing unit is used for signal processing and preparation. It typically includes an integrated interface for connecting to the controller or the PC system.

RFID systems are designed for some of the toughest environments and address just most identification applications in the plants. To learn more about industrial RFID applications and components visit www.balluff.us/rifd.

Controls Architectures Enable Condition Monitoring Throughout the Production Floor

In a previous blog post we covered some basics about condition monitoring and the capability of smart IO-Link end-devices to provide details about the health of the system. For example, a change in vibration level could mean a failure is near.

This post will detail three different architecture choices that enable condition monitoring to add efficiency to machines, processes, and systems: in-process, stand-alone, and hybrid models.

IO-Link is the technology that enables all three of these architectures. As a quick introduction, IO-Link is a data communications technology at the device level, instead of a traditional signal communication. Because it communicates using data instead of signals, it provides richer details from sensors and other end devices. (For more on IO-Link, search the blog.)

In-process condition monitoring architecture

In some systems, the PLC or machine controller is the central unit for processing data from all of the devices associated with the machine or system, synthesizing the data with the context, and then communicating information to higher-level systems, such as SCADA systems.

The data collected from devices is used primarily for controls purposes and secondarily to collect contextual information about the health of the system/machine and of the process. For example, on an assembly line, an IO-Link photo-eye sensor provides parts presence detection for process control, as well as vibration and inclination change detection information for condition monitoring.

With an in-process architecture, you can add dedicated condition monitoring sensors. For example, a vibration sensor or pressure sensor that does not have any bearings on the process can be connected and made part of the same architecture.

The advantage of an in-process architecture for condition monitoring is that both pieces of information (process information and condition monitoring information) can be collected at the same time and conveyed through a uniform messaging schema to higher-level SCADA systems to keep temporal data together. If properly stored, this information could be used later for machine improvements or machine learning purposes.

There are two key disadvantages with this type of architecture.

First, you can’t easily scale this system up. To add additional sensors for condition monitoring, you also need to alter and validate the machine controller program to incorporate changes in the controls architecture. This programming could become time consuming and costly due to the downtime related to the upgrades.

Second, machine controllers or PLCs are primarily designed for the purposes of machine control. Burdening these devices with data collection and dissemination could increase overall cost of the machine/system. If you are working with machine builders, you would need to validate their ability to offer systems that are capable of communicating with higher-level systems and Information Technology systems.

Stand-alone condition monitoring architecture

Stand-alone architectures, also known as add-on systems for condition monitoring, do not require a controller. In their simplest form, an IO-Link master, power supply, and appropriate condition monitoring sensors are all that you need. This approach is most prevalent at manufacturing plants that do not want to disturb the existing controls systems but want to add the ability to monitor key system parameters. To collect data, this architecture relies on Edge gateways, local storage, or remote (cloud) storage systems.

 

 

 

 

 

 

The biggest advantage of this system is that it is separate from the controls system and is scalable and modular, so it is not confined by the capabilities of the PLC or the machine controller.

This architecture uses industrial-grade gateways to interface directly with information technology systems. As needs differ from machine to machine and from company to company as to what rate to collect the data, where to store the data, and when to issue alerts, the biggest challenge is to find the right partner who can integrate IT/OT systems. They also need to maintain your IT data-handling policies.

This stand-alone approach allows you to create various dashboards and alerting mechanisms that offer flexibility and increased productivity. For example, based on certain configurable conditions, the system can send email or text messages to defined groups, such as maintenance or line supervisors. You can set up priorities and manage severities, using concise, modular dashboards to give you visibility of the entire plant. Scaling up the system by adding gateways and sensors, if it is designed properly, could be easy to do.

Since this architecture is independent of the machine controls, and typically not all machines in the plant come from the same machine builders, this architecture allows you to collect uniform condition monitoring data from various systems throughout the plant. This is the main reason that stand-alone architecture is more sought after than in-process architecture.

It is important to mention here that not all of the IO-Link gateways (masters) available in the market are capable of communicating directly with the higher-level IT system.

Hybrid architectures for condition monitoring

As the name suggests, this approach offers a combination of in-process and stand-alone approaches. It uses IO-Link gateways in the PLC or machine controller-based controls architecture to communicate directly with higher-level systems to collect data for condition monitoring. Again, as in stand-alone systems, not all IO-Link gateways are capable of communicating directly with higher-level systems for data collection.

The biggest advantage of this system is that it does not burden PLCs or machine controllers with data collection. It creates a parallel path for health monitoring while devices are being used for process control. This could help you avoid duplication of devices.

When the devices are used in the controls loop for machine control, scalability is limited. By specifying IO-Link gateways and devices that can support higher-level communication abilities, you can add out-of-process condition monitoring and achieve uniformity in data collection throughout the plant even though the machines are from various machine builders.

Overall, no matter what approach is the best fit for your situation, condition monitoring can provide many efficiencies in the plant.

How to Develop and Qualify Sensors for Arctic Conditions

The climatic conditions in the arctic are characterized by cold winters and short summers. There is a large variability in climate and weather: Some regions face permafrost and are ice covered year-round with temperatures down to -40°C / -40°F (and lower), other land areas face the extremes of solar radiation up to +30°C / +86°F in summer.

As oil and gas exploration, as well as renewable energy (e.g. cold climate versions of wind turbines) move into arctic areas, the need grows for sensors designed to deal with the extreme conditions and to perform reliably over their whole life cycle.

One option is the implementation of a Highly Accelerated Life Test (abbreviation HALT) in the development process. The basic idea of HALT is the accelerated aging of electronic products (including sealing gaskets, potting compound, housing etc.) with the aim of detecting their possible weak spots as early as possible and to correct them at the development stage.

Example of a sensor in the HALT test facility (Balluff magnetostrictive linear position sensor BTL)

The item under test is subjected to higher and higher thermal and mechanical stress in order to cause failures. The limits where the product will fail functionally or be destroyed are determined in order to push these limits as far out as possible, and so achieve a higher reliability for the product.

Image3
Product operational specs = data sheet values

The HALT procedure – in brief:

a) Analysis of weaknesses already known, definition of failure criteria, establishing the stress factors

b) Stressing the test specimen beyond the specification to find the “upper and lower operating limits”, and the “upper and lower destruct limit” for temperature, rapid change of temperature, vibration, combined vibration and temperature stress

c) Determination of the causes of failure

d) Devising a solution to eliminate the weaknesses

e) Repeating of steps b) to c)

Image4
Example: Temperature step test – cold and hot
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Example: Combined vibration test and rapid temperature changes
Cowan
Example: Cowan Dynamics E2H Electro-Hydraulic valve actuator Photo: Cowan Dynamics (Canada)

In contrast to other environmental tests, HALT is not qualification testing according to specific technical standards (as  ISO/IEC etc.), but it applies stimuli to the items under test until they fail, so weak spots will be revealed. A HALT test is not an exam you can pass!

However, if sensors are implemented into more complex automation systems that will be operated in remote areas, this method may help to prevent major faults in the field and is therefore also used in the aircraft and automotive industry.

For more information about Balluff testing methods and the laboratory, please visit www.balluff.com or download our brochure “The Balluff Testing Laboratory”.