The 5 Most Common Types of Fixed Industrial Robots

The International Federation of Robotics (IFR) defines five types of fixed industrial robots: Cartesian/Gantry, SCARA, Articulated, Parallel/Delta and Cylindrical (mobile robots are not included in the “fixed” robot category). These types are generally classified by their mechanical structure, which dictates the ways they can move.

Based on the current market situation and trends, we have modified this list by removing Cylindrical robots and adding Power & Force Limited Collaborative robots. Cylindrical robots have a small, declining share of the market and some industry analysts predict that they will be completely replaced by SCARA robots, which can cover similar applications at higher speed and performance. On the other hand, use of collaborative robots has grown rapidly since their first commercial sale by Universal Robots in 2008. This is why collaborative robots are on our list and cylindrical/spherical robots are not.

Therefore, our list of the top five industrial robot types includes:

    • Articulated
    • Cartesian/Gantry
    • Parallel/Delta
    • SCARA
    • Power & Force Limited Collaborative robots

These five common types of robots have emerged to address different applications, though there is now some overlap in the applications they serve. And range of industries where they are used is now very wide. The IFR’s 2021 report ranks electronics/electrical, automotive, metal & machinery, plastic and chemical products and food as the industries most commonly using fixed industrial robots. And the top applications identified in the report are material/parts handling and machine loading/unloading, welding, assembling, cleanrooms, dispensing/painting and processing/machining.

Articulated robots

Articulated robots most closely resemble a human arm and have multiple rotary joints–the most common versions have six axes. These can be large, powerful robots, capable of moving heavy loads precisely at moderate speeds. Smaller versions are available for precise movement of lighter loads. These robots have the largest market share (≈60%) and are growing between 5–10% per year.

Articulated robots are used across many industries and applications. Automotive has the biggest user base, but they are also used in other industries such as packaging, metalworking, plastics and electronics. Applications include material & parts handling (including machine loading & unloading, picking & placing and palletizing), assembling (ranging from small to large parts), welding, painting, and processing (machining, grinding, polishing).

SCARA robots

A SCARA robot is a “Selective Compliance Assembly Robot Arm,” also known as a “Selective Compliance Articulated Robot Arm.” They are compliant in the X-Y direction but rigid in the Z direction. These robots are fairly common, with around 15% market share and a 5-10% per year growth rate.

SCARA robots are most often applied in the Life Sciences, Semiconductor and Electronics industries. They are used in applications requiring high speed and high accuracy such as assembling, handling or picking & placing of lightweight parts, but also in 3D printing and dispensing.

Cartesian/Gantry robots

Cartesian robots, also known as gantry or linear robots, move along multiple linear axes. Since these axes are very rigid, they can precisely move heavy payloads, though this also means they require a lot of space. They have about 15% market share and a 5-10% per year growth rate.

Cartesian robots are often used in handling, loading/unloading, sorting & storing and picking & placing applications, but also in welding, assembling and machining. Industries using these robots include automotive, packaging, food & beverage, aerospace, heavy engineering and semiconductor.

Delta/Parallel robots

Delta robots (also known as parallel robots) are lightweight, high-speed robots, usually for fast handling of small and lightweight products or parts. They have a unique configuration with three or four lightweight arms arranged in parallelograms. These robots have 5% market share and a 3–5% growth rate.

They are often used in food or small part handling and/or packaging. Typical applications are assembling, picking & placing and packaging. Industries include food & beverage, cosmetics, packaging, electronics/ semiconductor, consumer goods, pharmaceutical and medical.

Power & Force Limiting Collaborative robots

We add the term “Power & Force Limiting” to our Collaborative robot category because the standards actually define four collaborative robot application modes, and we want to focus on this, the most well-known mode. Click here to read a blog on the different collaborative modes. Power & Force Limiting robots include models from Universal Robots, the FANUC CR green robots and the YuMi from ABB. Collaborative robots have become popular due to their ease of use, flexibility and “built-in” safety and ability to be used in close proximity to humans. They are most often an articulated robot with special features to limit power and force exerted by the axes to allow close, safe operation near humans or other machines. Larger, faster and stronger robots can also be used in collaborative applications with the addition of safety sensors and special programming.

Power & Force Limiting Collaborative robots have about 5% market share and sales are growing rapidly at 20%+ per year. They are a big success with small and mid-size enterprises, but also with more traditional robot users in a very broad range of industries including automotive and electronics. Typical applications include machine loading/unloading, assembling, handling, dispensing, picking & placing, palletizing, and welding.

Summary­

The robot market is one of the most rapidly growing segments of the industrial automation industry. The need for more automation and robots is driven by factors such as supply chain issues, changing workforce, cost pressures, digitalization and mass customization (highly flexible manufacturing). A broad range of robot types, capabilities and price points have emerged to address these factors and satisfy the needs of applications and industries ranging from automotive to food & beverage to life sciences.

Note: Market share and growth rate estimates in this blog are based on public data published by the International Federation of Robotics, Loup Ventures, NIST and Interact Analysis.

Protecting photoelectric and capacitive sensors

Supply chain and labor shortages are putting extra pressure on automation solutions to keep manufacturing lines running. Even though sensors are designed to work in harsh environments, one good knock can put a sensor out of alignment or even out of condition. Keep reading for tips on ways to protect photoelectric and capacitive sensors.

Mounting solutions for photoelectric sensors

Photoelectric sensors are sensitive to environmental factors that can cloud their view, like dust, debris, and splashing liquids, or damage them with physical impact. One of the best things to do from the beginning is to protect them by mounting them in locations that keep them out of harm’s way. Adjustable mounting solutions make it easier to set up sensors a little further away from the action. Mounts that can be adjusted on three axes like ball joints or rod-and-mount combinations should lock firmly into position so that vibration or weight will not cause sensors to move out of alignment. And mounting materials like stainless steel or plastic can be chosen to meet factors like temperature, accessibility, susceptibility to impact, and contact with other materials.

When using retroreflective sensors, reflectors and reflective foils need similar attention. Consider whether the application involves heat or chemicals that might contact reflectors. Reflectors come in versions, especially for use with red, white, infrared, and laser lights, or especially for polarized or non-polarized light. And there are mounting solutions for reflectors as well.

Considering the material and design of capacitive sensors

Capacitive sensors must also be protected based on their working environment, the material they detect, and where they are installed. Particularly, is the sensor in contact with the material it is sensing or not?

If there is contact, pay special attention to the sensor’s material and design. Foods, beverages, chemicals, viscous substances, powders, or bulk materials can degrade a sensor constructed of the wrong material. And to switch perspectives, a sensor can affect the quality of the material it contacts, like changing the taste of a food product. If resistance to chemicals is needed, housings made of stainless steel, PTFE, and PEEK are available.

While the sensor’s material is important to its functionality, the physical design of the sensor is also important. A working environment can involve washdown processes or hygienic requirements. If that is the case, the sensor’s design should allow water and cleaning agents to easily run off, while hygienic requirements demand that the sensor not have gaps or crevices where material may accumulate and harbor bacteria. Consider capacitive sensors that hold FDA, Ecolab, and CIP certifications to work safely in these conditions.

Non-contact capacitive sensors can have their own special set of requirements. They can detect material through the walls of a tank, depending on the tank wall’s material type and thickness. Plastic walls and non-metallic packaging present a smaller challenge. Different housing styles – flat cylindrical, discs, and block styles – have different sensing capabilities.

Newer capacitive technology is designed as an adhesive tape to measure the material inside a tank or vessel continuously. Available with stainless steel, plastic, or PTFF housing, it works particularly well when there is little space available to detect through a plastic or glass wall of 8mm or less. When installing the tape, the user can cut it with scissors to adjust the length.

Whatever the setting, environmental factors and installation factors can affect the functionality of photoelectric and capacitive sensors, sometimes bringing them to an untimely end. Details like mounting systems and sensor materials may not be the first requirements you look for, but they are important features that can extend the life of your sensors.

 

Detecting Liquid Media and Bubbles Using Optical Sensors

In my line of work in Life Sciences, we often deal with liquid media and bubble detection evaluation through a vessel or a tube. This can be done by using the absorption principle or the refraction principle with through-beam-configured optical sensors. These are commonly embedded in medical devices or lab instruments.

This configuration provides strong benefits:

    • Precise sensing
    • Ability to evaluate liquid media
    • Detect multiple events
    • High reliability

How does it work?

The refraction principle is based on the media’s refraction index. It uses an emitted light source (Tx) that is angled to limit the light falling on the receiver (Rx, Figure 1). When the light passes through a liquid, refraction causes the light to focus on the receiver as a beam (known as a “beam-make” configuration). All liquids and common vessel materials (silicon, plastic, glass, etc.) have a known refraction index. These sensors will detect those refraction differences and output a signal.

The absorption principle is preferred when a media’s absorption index is high. First, a beam is established through a vessel or tube (Figure 2). Light sources in the 1500nm range work best for aqueous-based media such as water. As a high absorption index liquid enters the tube, it will block the light (known as a beam-break configuration). The sensor detects this loss of light.

Discrete on-off signals are easily used by a control system. However, by using the actual light value information (commonly analog), more data can be extracted. This is becoming more popular now and can be done with either sensing principle. By using this light-value information, you can differentiate between types of media, measure concentrations, identify multiple objects (e.g., filter in an IV and the media) and much more.

There is a lot to know about through-beam sensors, so please leave a comment below if you have questions on how you can benefit from this technology.

Weld Immune vs. Weld Field Immune: What’s the difference? 

In today’s automotive plants and their tier suppliers, the weld cell is known to be one of the most hostile environments for sensors. Weld slag accumulation, elevated ambient temperatures, impacts by moving parts, and strong electromagnetic fields can all degrade sensor performance and cause false triggering. It is widely accepted that sensors will have a limited life span in most plants.

Poor sensor selection does mean higher failure rates which cause welders in all industries increased downtime, unnecessary maintenance, lost profits, and delayed delivery. There are many sensor features designed specifically to withstand these harsh welding environments and the problems that come along with them to combat this.

In the search for a suitable sensor for your welding application, you are sure to come across the terms weld immune and weld field immune. What do these words mean? Are they the same thing? And will they last in my weld cell?

Weld Immune ≠ Weld Field Immune

At first glance, it is easy to understand why someone may confuse these two terms or assume they are one and the same.

Weld field immune is a specific term referring to sensors designed to withstand strong electromagnetic fields. In some welding areas, especially very close to the weld gun, welders can generate strong magnetic fields. When this magnetic field is present, it can cause a standard sensor to perform intermittently, like flickering and false outputs.

Weld field immune sensors have special filtering and robust circuitry that withstand the influence of strong magnetic fields and avoid false triggers. This is also called magnetic field immune since they also perform well in any area with high magnetic noise.

On the other hand, weld immune is a broad term used to describe a sensor designed with any features that increase its performance in a welding application. It could refer to one or multiple sensor features, including:

    • Weld spatter resistant coatings
    • High-temperature resistance
    • Different housing or sensor face materials
    • Magnetic field immunity

A weld field immune sensor might be listed with the numerous weld immune sensors with special coatings and features, but that does not necessarily mean any of those other sensors are immune to weld fields. This is why it is always important to check the individual sensor specifications to ensure it is suitable for your application.

In an application where a sensor is failing due to impact damage or weld slag spatter, a steel face sensor with a weld resistant coating could be a great solution. If this sensor isn’t close to the weld gun and isn’t exposed to any strong magnetic fields, there is really no need for it to be weld field immune. The important features are the steel face and coating that can protect it against impact and weld slag sticking to it. This sensor would be classified as weld immune.

In another application where a sensor near the weld gun side of the welding procedure where MIG welding is performed, this location is subject to arc blow that can create a strong magnetic field at the weld wire tip location. In this situation, having a weld field immune sensor would be important to avoid false triggers that the magnetic field may cause. Additionally, being close to a MIG weld gun, it would also be wise to consider a sensor with other weld immune properties, like a weld slag resistant coating and a thermal barrier, to protect against high heat and weld slag.

Weld field immunity is just one of many features you can select when picking the best sensor for your application. Whether the issue is weld slag accumulation, elevated ambient temperatures, part impact, or strong electromagnetic fields, there are many weld immune solutions to consider. Check the placement and conditions of the sensors you’re using to decide which weld-immune features are needed for each sensor.

Click here for more on choosing the right sensor for your welding application.

 

Does Your Stamping Department Need a Checkup? Try a Die-Protection Risk Assessment

If you have ever walked through a stamping department at a metal forming facility, you have heard the rhythmic sound of the press stamping out parts, thump, thump. The stamping department is the heart manufacturing facility, and the noise you hear is the heartbeat of the plant. If it stops, the whole plant comes to a halt. With increasing demands for higher production rates, less downtime, and reduction in bad parts, stamping departments are under ever-increasing pressure to optimize the press department through die protection and error-proofing programs.

The die-protection risk assessment team

The first step in implementing or optimizing a die protection program is to perform a die-protection risk assessment. This is much like risk assessments conducted for safety applications, except they are done for each die set. To do this, build a team of people from various positions in the press department like tool makers, operators, and set-up teams.

Once this team is formed, they can help identify any incidents that could occur during the stamping operations for each die set and determine the likelihood and the severity of possible harm. With this information, they can identify which events have a higher risk/severity and determine what additional measures they should implement to prevent these incidents. An audit is possible even if there are already some die protection sensors in place to determine if there are more that should be added and verify the ones in place are appropriate and effective.

The top 4 die processes to check

The majority of quality and die protection problems occur in one of these three areas: material feed, material progression, and part- and slug-out detections. It’s important to monitor these areas carefully with various sensor technologies.

Material feed

Material feed is perhaps the most critical area to monitor. You need to ensure the material is in the press, in the correct location, and feeding properly before cycling the press. The material could be feeding as a steel blank, or it could come off a roll of steel. Several errors can prevent the material from advancing to the next stage or out of the press: the feed can slip, the stock material feeding in can buckle, or scrap can fail to drop and block the strip from advancing, to name a few. Inductive proximity sensors, which detect iron-based metals at short distances, are commonly used to check material feeds.

Material progression

Material progression is the next area to monitor. When using a progressive die, you will want to monitor the stripper to make sure it is functioning and the material is moving through the die properly. With a transfer die, you want to make sure the sheet of material is nesting correctly before cycling the press. Inductive proximity sensors are the most common sensor used in these applications, as well.

Here is an example of using two inductive proximity sensors to determine if the part is feeding properly or if there is a short or long feed. In this application, both proximity sensors must detect the edge of the metal. If the alignment is off by just a few millimeters, one sensor won’t detect the metal. You can use this information to prevent the press from cycling to the next step.

Short feed, long feed, perfect alignment

Part-out detection

The third critical area that stamping departments typically monitor is part-out detection, which makes sure the finished part has come out of the stamping

area after the cycle is complete. Cycling the press and closing the tooling on a formed part that failed to eject can result in a number of undesirable events, like blowing out an entire die section or sending metal shards flying into the room. Optical sensors are typically used to check for part-out, though the type of photoelectric needed depends on the situation. If the part consistently comes out of the press at the same position every time, a through-beam photo-eye would be a good choice. If the part is falling at different angles and locations, you might choose a non-safety rated light grid.

Slug-ejection detection

The last event to monitor is slug ejection. A slug is a piece of scrap metal punched out of the material. For example, if you needed to punch some holes in metal, the slug would be the center part that is knocked out. You need to verify that the scrap has exited the press before the next cycle. Sometimes the scrap will stick together and fail to exit the die with each stroke. Failure to make sure the scrap material leaves the die could affect product quality or cause significant damage to the press, die, or both. Various sensor types can ensure proper scrap ejection and prevent crashes. The picture below shows a die with inductive ring sensors mounted in it to detect slugs as they fall out of the die.

Just like it is important to get regular checkups at the doctor, performing regular die-protection assessments can help you make continuous improvements that can increase production rates and reduce downtime. Material feed, material progression, part-out and slug-out detection are the first steps to optimize, but you can expand your assessments to include areas like auxiliary equipment. You can also consider smart factory solutions like intelligent sensors, condition monitoring, and diagnostics over networks to give you more data for preventative maintenance or more advanced error-proofing. The key to a successful program is to assemble the right team, start with the critical areas listed above, and learn about new technologies and concepts that are becoming available to help you plan ways to improve your stamping processes.

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.

Maximize the Benefits of Open-Source Code in Manufacturing Software

The rise of many players in manufacturing automation, along with factories’ growing adoption of Industrial Internet of Things (IIoT) and automation solutions, present a suitable environment for open-source software. This software is a value-adding solution for manufacturers, regardless of their operation technology and management requirements, due to the customization, resiliency, scalability, accessibility, cost-effectiveness, and quality it allows.

Customization

Software developers who use open-source code provide software with a core code that establishes specific features and allows users to access it and make changes as necessary. The process is much like being able to complete an author’s writing prompt or change the end of a story. Unlike a closed system that locks users in, open-source allows them to adapt and modify the code to meet a particular need or application.

This add-on coding system provides endless customization. It enables communities (i.e., users) to add or remove features beneficial in an integration phase, such as features for user testing or to find the best solution for a machine.

Customization is also valuable regarding data visualizations; users can develop dashboards and visuals that best describe their operations. Suppose a sensor provides real-time condition monitoring data over a particular machine. In that case, it’s possible to customize the code supporting the software that gathers and processes the data for specific parameters or to calculate specific values.

Resiliency

Additionally, open-source code is resilient to change because it can be modified quickly. The ability to quickly add or remove features and adapt to cyber environments or specific applications also makes it volatile. Like exposure to pathogens can help strengthen an immune response to said pathogens, so can an open-source code be made stronger by its exposure to different environments and applications to be ready to face cybersecurity threats. Implementing an open code isn’t any less risky (cybersecurity-wise) than closed codes due to the testing and enhancements made by so many coders or programmers. However, it is up to the implementer to use the same rules that apply to other closed source software. The implementer must be aware of the code’s source and avoid code from non-reputable sources who could have modified it with negative intentions. Overall, the code is resilient, adaptable, and agile to adapt given a new environment.

Scalability

The add-on and customization aspects of open-source also allow the code to be highly scalable. This scalable implementation happens in two dimensions: adoption timeline and application-based. Both are important to guarantee user acceptance and that it meets the operation and application requirements. Regarding the adoption timeline, scalability allows modification of the software and code to meet users’ expectations. Open-sourced code enables the implementation of features for user testing and feedback. The ultimate solution will include multiple iterations to meet the users’ needs and fulfill operation expectations.

On the other hand, this code is scalable based on the application(s), such as working on different machines, multiples of the same machine with different purposes, or adding/dropping features for specific uses. Say, for example, there are three of the same machine (A, B, and C), but they are in different environments. Machine A is in an environment that is 28°F , B is at room temperature, and C is exposed to constant wash-down. In this case, the condition monitoring software defines the acceptable parameters for each scenario, avoiding false alarms from erroneous triggers. In this example, the base code is adapted to include specific features based on the application.

Accessibility

In general, cost-effective and high-quality open-source code is available online. There are additional resources such as free coding tutorials that don’t require any licenses as well. Moreover, when programmers update an open code, they must make the new version available again, ensuring that the code is accessible and up to date.

Cost-effectiveness and quality

Regarding cost-effectiveness, using community open-source code significantly reduces the cost of developing, integrating, and testing software built in-house. It also reduces the implementation time and makes for better production operations. Essentially, it is high-quality, reliable code created by trusted sources for multiple coders and users.

“The application drives the technology” mantra is at the heart of open-source software development—a model where source code is available for community members to use, modify, and share. IIoT enablers and providers in the manufacturing industry own a particular solution that is then available for manufacturers to adapt to their specific operational requirements. With the increasing adoption of data-collecting technologies, it is in manufacturers’ best interest to seek software providers who grant them the flexibility to adjust software solutions to meet their specific needs. Automation is a catalyst for data-driven operation and maintenance.

Know Your RFID Frequency Basics

In 2008 I purchased my first toll road RFID transponder, letting me drive through and pay my toll without stopping at a booth. This was my first real-life exposure to RFID, and it was magical. Back then, all I knew was that RFID stood for “radio frequency identification” and that it exchanged data between a transmitter and receiver using radio waves. That’s enough for a highway driver, but you’ll need more information to use RFID in an industrial automation setting. So here are some basics on what makes up an RFID system and the uses of different radio frequencies.

At a minimum, an RFID system comprises a tag, an antenna, and a processor. Tags, also known as data carriers, can be active or passive. Active tags have a built-in power source, and passive tags are powered by the electromagnetic field emitted by the antenna and are dormant otherwise. Active tags have a much longer range than passive tags. But passive tags are most commonly used in industrial RFID applications due to lower component costs and no maintenance requirements.

Low frequency (LF), high frequency (HF), ultra-high frequency (UHF)

The next big topic is the different frequency ranges used by RFID: low frequency (LF), high frequency (HF), and ultra-high frequency (UHF). What do they mean? LF systems operate at a frequency range of 125…135 kHz, HF systems operate at 13.56 MHz, and UHF systems operate at a frequency range of 840…960 MHz. This tells you that the systems are not compatible with each other and that you must choose the tag, antenna, and processor unit from a single system for it to work properly. This also means that the LF, HF, and UHF systems will not interfere with each other, so you can install different types of RFID systems in a plant without running a risk of interference or crosstalk issues between them or any other radio communications technology.

 

Choosing the correct system frequency?

How do you choose the correct system frequency? The main difference between LF/HF systems and UHF systems is the coupling between the tags and the antenna/processor. LF and HF RFID systems use inductive coupling, where an inductive coil on the antenna head is energized to generate an inductive field. When a tag is present in that inductive field, it will be energized and begin communications back and forth. Using the specifications of the tag and the antenna/processor, it is easy to determine the read/write range or the air gap between the tag and the antenna head.

The downside of using LF/HF RFID technology based on inductive coupling is that the read/write range is relatively short, and it’s dependent on the physical size of the coils in the antenna head and the tag. The bigger the antenna and tag combination, the greater the read/write distance or the air gap between the antenna and the tag. The best LF and HF RFID uses are in close-range part tracking and production control where you need to read/write data to a single tag at a time.

UHF RFID systems use electromagnetic wave coupling to transmit power and data over radio waves between the antenna and the tag. The Federal Communications Commission strictly regulates the power level and frequency range of the radio waves, and there are different frequency range specifications depending on the country or region where the UHF RFID system is being used. In the United States, the frequency is limited to a range between 902 and 928MHz. Europe, China, and Japan have different operating range specifications based on their regulations, so you must select the correct frequency range based on the system’s location.

Using radio waves enables UHF RFID systems to achieve a much greater read/write range than inductive coupling-based RFID systems. UHF RFID read/write distance range varies based on transmission power, environmental interference, and the size of the UHF RFID tag, but can be as large as 6 meters or 20 feet. Environmental interferences such as metal structures or liquids, including human bodies, can deflect or absorb radio waves and significantly impact the performance and reliability of a UHF RFID system. UHF RFID systems are great at detecting multiple tags at greater distances, making them well suited for traceability and intralogistics applications. They are not well suited for single tag detection applications, especially if surrounded by metal structures.

Because of the impact an environment has on UHF signals, it is advisable to conduct a full feasibility study by the vendor of the UHF RFID system before the system solution is purchased to ensure that the system will meet the application requirements. This includes bringing in the equipment needed, such as tags, antennas, processors, and mounting brackets to the point of use to ensure reliable transmission of data between the tag and the antenna and testing the system performance in normal working conditions. Performing a feasibility study reduces the risk of the system not meeting the customer’s expectations or application requirements.

Selecting an industrial RFID system

There are other factors to consider when selecting an industrial RFID system, but this summary is a good place to start:

    • Most industrial RFID applications use passive RFID tags due to their lower component costs and no battery replacement needs.
    • For applications requiring short distance and single tag detection, LF or HF RFID systems are recommended.
    • For applications where long-distance and multi-tag detection is needed, UHF RFID systems are recommended.
    • If you are considering UHF, a feasibility study is highly recommended to ensure that the UHF RFID system will perform as intended and meet your requirements.

Click here to browse our library of Automation Insights blogs related to RFID.

Condition Monitoring & Predictive Maintenance: Cost-Benefit Tradeoffs

In a previous blog post, we discussed the basics of the Potential-Failure (P-F) curve, which refers to the interval between the detection of a potential failure and occurrence of a functional failure. In this post we’ll discuss the cost-benefit tradeoffs of various maintenance approaches.

In general, the goal is to maximize the P-F interval, which is the time between the first symptoms of impending failure and the functional failure taking place. In other words, you want to become aware of an impending failure as soon as possible to allow more time for action. This, however, must be balanced with the cost of the methods of prevention, inspection, and detection.

There is a trade-off between the cost of systems to detect and predict the failures and how soon you might detect the condition. Generally, the earlier the detection/prediction, the more expensive it is. However, the longer it takes to detect an impending failure (i.e. the more the asset’s condition degrades), the more expensive it is to repair it.Every asset will have a unique trade-off between the cost of failure prevention (detection/prediction) and the cost of failure. This means some assets probably call for earlier detection methods that come with higher prevention costs like condition monitoring and analytics systems due to the high cost to repair (see the Prevention-1 and Repair-1 curves in the Cost-Failure/Time chart). And some assets may be better suited for more cost-efficient but delayed detection or even a “run-to-failure” model due to lower cost to repair (the Prevention-2 and Repair-2 curves in the Cost-Failure/Time chart).

 

There are four basic Maintenance approaches:

:

Reactive

The Reactive approach has low or even no cost to implement but can result in a high repair/failure cost because no action is taken until the asset has reached a fault state. This approach might be appropriate when the cost of monitoring systems is very high compared to the cost of repairing or replacing the asset. As a general guideline, the Reactive approach is not a good strategy for any critical and/or high value assets due to their high cost of a failure.

Reactive approaches:

      • Offer no visibility
      • Fix only if it breaks – low overall equipment effectiveness (OEE)
      • High downtime
      • Uncertainty of failures

Preventative

The Preventative approach (maintenance at time-based intervals) may be appropriate when failures are age related and maintenance can be performed at regular intervals before anticipated failures occur. Two drawbacks to this approach are: 1) the cost and time of preventative maintenance can be high; and 2) studies show that only 18% of failures are age related (source: ARC Advisory Group). 82% of failures are “random” due to improper design/installation, operator error, quality issues, machine overuse, etc. This means that taking the Preventative approach may be spending time and money on unnecessary work, and it may not prevent expensive failures in critical or high value assets.

Preventative approaches:

      • Scheduled tune ups
      • Higher equipment longevity
      • Reduced downtime compared to reactive mode

Condition-Based

The Condition-Based approach attempts to address failures regardless of whether they are age-based or random. Assets are monitored for one or more potential failure indicators, such as vibration, temperature, current/voltage, pressure, etc. The data is often sent to a PLC, local HMI, special processor, or the cloud through an edge gateway. Predefined limits are set and alerts (alarm, operator message, maintenance/repair) are only sent when a limit is reached. This approach avoids unnecessary maintenance and can give warning before a failure occurs. Condition-based monitoring can be very cost-effective, though very sophisticated solutions can be expensive. It is a good solution when the cost of failure is medium or high and known indicators provide a reliable warning of impending failure.

Condition-based approaches:

      • Based on condition (PdM)
      • Enables predictive maintenance
      • Improves OEE, equipment longevity
      • Drastically reduces unplanned downtime

Predictive Analytics

Predictive Analytics is the most sophisticated approach and attempts to learn from machine performance to predict failures. It utilizes data gathered through Condition Monitoring, and then applies analysis or AI/Machine Learning to uncover patterns to predict failures before they occur. The hardware and software to implement Predictive Analytics can be expensive, and this method is best for high-value/critical assets and expensive potential failures.

Predictive Analytics approaches:

      • Based on patterns – stored information
      • Based on machine learning
      • Improves OEE, equipment longevity
      • Avoids downtime

Each user will need to evaluate the unique attributes of their assets and decide on the best approach and trade-offs of the cost of prevention (detection of potential failure) against the cost of repair/failure. In general, a Reactive approach is only best when the cost of failure is very low. Preventative maintenance may be appropriate when failures are clearly age-related. And advanced approaches such as Condition Based monitoring and Predictive Analytics are best when the cost of repair or failure is high.

Also note that technology providers are continually improving condition monitoring and predictive solutions. By lowering condition monitoring system costs and making them easier to set up and use,  users can cost-effectively move from Reactive or Preventative approaches to Condition-Based or Predictive approaches.

Avoid Downtime in Metal Forming With Inductive & Photoelectric Sensors

Industrial sensor technology revolutionized how part placement and object detection are performed in metal forming applications. Inductive proximity sensors came into standard use in the industry in the 1960s as the first non-contact sensor that could detect ferrous and nonferrous metals. Photoelectric sensors detect objects at greater distances. Used together in a stamping environment, these sensors can decrease the possibility of missing material or incorrect placement that can result in a die crash and expensive downtime.

Inductive sensors

In an industrial die press, inductive sensors are placed on the bottom and top of the dies to detect the sheet metal for stamping. The small sensing range of inductive sensors allows operators to confirm that the sheet metal is correctly in place and aligned to ensure that the stamping process creates as little scrap as possible.

In addition, installing barrel-style proximity sensors so that their sensing face is flush with the die structure will confirm the creation of the proper shape. The sensors in place at the correct angles within the die will trigger when the die presses the sheet metal into place. The information these sensors gather within the press effectively make the process visible to operators. Inductive sensors can also detect the direction of scrap material as it is being removed and the movement of finished products.

Photoelectric sensors

Photoelectric sensors in metal forming have two main functions. The first function is part presence, such as confirming that only a single sheet of metal loads into the die, also known as double-blank detection. Doing this requires placing a distance-sensing photoelectric sensor at the entry-way to the die. By measuring the distance to the sheet metal, the sensor can detect the accidental entry of two or more sheets in the press. Running the press with multiple metal sheets can damage the die form and the sensors installed in the die, resulting in expensive downtime while repairing or replacing the damaged parts.

The second typical function of photoelectric sensors verifies the movement of the part out of the press. A photoelectric light grid in place just outside the exit of the press can confirm the movement of material out before the next sheet enters into the press. Additionally, an optical window in place where parts move out will count the parts as they drop into a dunnage bin. These automated verification steps help ensure that stamping processes can move at high speeds with high accuracy.

These examples offer a brief overview of the sensors you mostly commonly find in use in a die press. The exact sensors are specific to the presses and the processes in use by different manufacturers, and the technology the stamping industry uses is constantly changing as it advances. So, as with all industrial automation, selecting the most suitable sensor comes down to the requirements of the individual application.