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.

Inductive Sensors and Their Unlimited Uses in Automation

Inductive sensors (also known as proximity sensors or proxes) are the most commonly used sensors in mechanical engineering and industrial automation. When they were invented in the 1960s, they marked a milestone in the development of control systems. In a nutshell, they generate an electromagnetic field that reacts to metal targets that approach the sensor head. They even work in harsh environments and can solve versatile applications.

There are hardly any industrial machines that work without inductive sensors. So, what can be solved with one, two, three, or more of them?

What can you do with one inductive sensor?

Inductive sensors are often used to detect an end position. This could be in a machine for end-of-travel detection, but also in a hydraulic cylinder or a linear direct drive as an end-of-stroke sensor. In machine control, they detect many positions and trigger other events. Another application is speed monitoring with a tooth wheel.

What can you do with two inductive sensors?

By just adding one more sensor you can get the direction of rotational motion and take the place of a more expensive encoder. In a case where you have a start and end position, this can also be solved with a second inductive sensor.

What can you do with three inductive sensors?

In case of the tooth wheel application, the third sensor can provide a reference signal and the solution turns into a multiturn rotary encoder.

What can I do with four inductive sensors and more?

For multi-point positioning, it may make sense to switch to a measurement solution, which can also be inductive. Beyond that, an array of inductive sensors can solve identification applications: In an array of 2 by 2 sensors, there are already 16 different unique combinations of holes in a hole plate. In an array of 3 by 3, it would be 512 combinations.

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.

IO-Link Event Data: How Sensors Tell You How They’re Doing

I have been working with IO-Link for more than 10 years, so I’ve heard lots of questions about how it works. One line of questions I hear from customers is about the operating condition of sensors. “I wish I knew when the IO-Link device loses output power,” or, “I wish my IO-Link photoelectric sensor would let me know when the lens is dirty.” The good news is that it does give you this information by sending Event Data. That’s a type of data that is usually not a focus of users, although it is available in JSON format from the REST API.

There are three types of IO-Link data:

      • Process Data – updated cyclically, it’s important to users because it contains the data for use in the running application, like I/O change of states or measurement values like temperature and position, etc.
      • Parameter Data – updated acyclically, it’s important to users because it’s the mechanism to read and write parameter values like setpoints, thresholds, and configuration settings to the sensor, and for reading non-time critical values like operating hours, etc.
      • Event Data – updated acyclically, it’s important to users because it provides immediate updates on device conditions.

Let’s dig deeper into Event Data. An Event is a status update from the IO-Link device when a condition is out of its normal range. The Event is labeled as a Warning or Error based on the severity of the condition change.

When an Event occurs on the IO-Link device, the device sets the Event Flag bit in the outgoing data packet to the IO-Link Master. The Master receives the Event Flag and then queries the IO-Link device for the Event information.

It is important to note that this is a one-time data message. The IO-Link device only sends the Event Flag at the moment the condition is out of range, and then again when the condition is back in range.

Event Data Types, Modes, and Codes

Event Data has three following three components:

      • Event Type – categorized in three ways
        • Notification – a simple event update; nothing is abnormal with the IO-Link device
        • Warning – a condition is out of range and risks damaging the IO-Link device
        • Error – a condition is out of range and is affecting the device negatively to the point that it may not function as expected
      • Event Mode – categorized in three ways
        • Event notice – usually associated with Event Type notifications, message will not be updated
        • Event appears – the condition is now out of range
        • Event disappears – the condition is now back in range
      • Event Code
        • A two-byte Hex code that represents the condition that is out of range

IO-Link condition monitoring sensor

To bring all these components together, let’s look at a photoelectric IO-Link sensor with internal condition monitoring functions and see what Events are available for it in this device manual screenshot. This device has Events for temperature (both warning and error), voltage, inclination (sensor angle is out of range), vibration, and signal quality (dirty lens).

By monitoring these events, you have a better feel for the conditions of your IO-Link device. Along with helping you identify immediate problems, this can help you in planning preventive and planned maintenance.

An IO-Link condition monitoring sensor uses Event Data similarly to report when conditions exceed the thresholds that you have set. For example, when the vibration level exceeds the threshold value, the IO-Link device sends the Warning event flag and the IO-Link Master queries for the event data. The event data consists of an Event Type, an Event Mode, and an Event Code that represents the specific alarm condition that is out of range. Remember this is a one-time action; the IO-Link sensor will not report this again until the value is in an acceptable range.

When the vibration level is back in range, the alarm condition is no longer present in the IO-Link device, the process repeats itself. In this case the Event Type and Event Code will be the same. The only change is that the Event Mode will report Event Disappears.

Within the IO-Link Specification there is a list of defined Event Codes that are common across all vendors. There is also a block of undefined Event Code values that allow vendors to create Event Codes that are unique to their specific device.

“I wish the IO-Link device would let me know….” In the end, the device might be telling you what you want to know, especially if the device has condition monitoring functions built into it. If you want to know more about condition monitoring in your IO-Link devices, check out the Event section in the vendor’s manuals so you can learn how to use this information.

Control Meets IIoT, Providing Insights into a New World

In manufacturing and automation control, the programmable logic controller (PLC) is an essential tool. And since the PLC is integrated into the machine already, it’s understandable that you might see the PLC as all that you need to do anything in automation on the manufacturing floor.

Condition monitoring in machine automation

For example, process or condition monitoring is emerging as an important automation feature that can help ensure that machines are running smoothly. This can be done by monitoring motor or mechanical vibration, temperature or pressure. You can also add functionality for a machine or line configuration or setup by adding sensors to verify fixture locations for machine configuration at changeovers.

One way to do this is to wire these sensors to the PLC and modify its code and use it as an all-in-one device. After all, it’s on the machine already. But there’s a definite downside to using a PLC this way. Its processing power is limited, and there are limits to the number of additional processes and functions it can run. Why risk possible complications that could impact the reliability of your control systems? There are alternatives.

External monitoring and support processes

Consider using more flexible platforms, such as an edge gateway, Linux, and IO-Link. These external sources open a whole new world of alternatives that provide better reliability and more options for today and the future. It also makes it easier to access and integrate condition monitoring and configuration data into enterprise IT/OT (information technology/operational technology) systems, which PLCs are not well suited to interface with, if they can be integrated at all.

Here are some practical examples of this type of augmented or add-on/retrofit functionality:

      • Motor or pump vibration condition monitoring
      • Support-process related pressure, vibration and temperature monitoring
      • Monitoring of product or process flow
      • Portable battery based/cloud condition monitoring
      • Mold and Die cloud-based cycle/usage monitoring
      • Product changeover, operator guidance system
      • Automatic inventory monitoring warehouse system

Using external systems for these additional functions means you can readily take advantage of the ever-widening availability of more powerful computing systems and the simple connectivity and networking of smart sensors and transducers. Augmenting and improving your control systems with external monitoring and support processes is one of the notable benefits of employing Industrial Internet of Things (IIoT) and Industry 4.0 tools.

The ease of with which you can integrate these systems into IT/OT systems, even including cloud-based access, can dramatically change what is now available for process information-gathering and monitoring and augment processes without touching or effecting the rudimentary control system of new or existing machines or lines. In many cases, external systems can even be added at lower price points than PLC modification, which means they can be more easily justified for their ROI and functionality.

5 Manufacturing Trends to Consider as You Plan for 2022

It’s that time of year again where we all start to forget the current year (maybe that’s OK) and start thinking of plans for the next — strategy and budget season! 2022 is only a few weeks away!

I thought I’d share 5 insights I’ve had about 2022 that you might benefit from as you start planning for next year.

    1. Electric Vehicles

      The electric vehicles manufacturing market is receiving major investments, machine builders are building up expertise, and consumers are trending towards more electric vehicles. According to PEW research, 7% of US adults say they currently own a hybrid or electric vehicle, but 39% say the next time they purchase a vehicle they are at least somewhat likely to seriously consider electric. Traditional automotive won’t go away any time soon, but I see this as a growth generator.

    1. Automation in Agriculture & Food

      Automation in the agriculture, food, beverage and packaging markets is also growing strong with more demand for packaged goods and more SKUs than ever before. Urbanization and shortages in agriculture labor markets are driving investments in automation technologies in manufacturing and on the farm. Robotic agriculture startups seem to be growing faster than weeds and are providing real value for those who are struggling to get product from the field to the factory.

    1. Supply Chain Disruption

      Several economists have said the chip shortage will be with us well into 2023, and now I hear rumors of plastics or other materials having disruptions. Disruption might be the new normal for the short to mid-term. I flew out of LAX a few weeks ago and there were dozens of container ships parked outside the port. We are also seeing a major breakdown of our “over-land” logistics infrastructure. Investment in automation and labor for this market will be vital to a strong recovery. Plan for these things and be willing to have open and honest discussions with your vendors and your customers. Untruths might get you by in the short term but could permanently damage your business relationships for years.

    1. Real not Hyped Sustainability

      As Generation Z (18-24year old) workers increasingly enter our economy, they are pushing us to truly work towards sustainability much more than Millennials did before them. What this means is other markets that I see as growth opportunities are ones where we can have major impact on this, like mining, waste/recycling, and agriculture.

    1. Technology as an HR tool

      All manufacturers will be impacted by the skills-gap and labor shortage if you aren’t already. Part of your strategy for 2022 must include automation and robotics as part of your labor strategy. We need to consider how can we use automation and robotics to do our dull, dirty, dangerous jobs or how can we use automation and robotics to extend the careers of our long-term experienced workers. What disruptive technology could you be investing in to make a real difference in your work processes — 3D printing, machine vision, AR/VR, exoskeletons, drones, virtual twin, AI, predictive maintenance, condition monitoring, smart sensors? Pick something you will do different in 2022. You have to.

What do you see for 2022 that will have a major impact on our businesses?

Choosing the Right Sensor for Your Welding Application

Automotive structural welding at tier suppliers can destroy thousands of sensors a year in just one factory. Costs from downtime, lost production, overtime, replacement time, and material costs  eat into profitability and add up to a big source of frustration for automated and robotic welders. When talking with customers, they often list inductive proximity sensor failure as a major concern. Thousands and thousands of proxes are being replaced and installations are being repaired every day. It isn’t particularly unusual for a company to lose a sensor on every shirt in a single application. That is three sensors a day  — 21 sensors a week — 1,100 sensors a year failing in a single application! And there could be thousands of sensor installations in an  automotive structural assembly line. When looking at the big picture, it is easy to see how this impacts the bottom line.

When I work with customers to improve this, I start with three parts of a big equation:

  • Sensor Housing
    Are you using the right sensor for your application? Is it the right form factor? Should you be using something with a coating on the housing? Or should you be using one with a coating on the face? Because sensors can fail from weld spatter hitting the sensor, a sensor with a coating designed for welding conditions can greatly extend the sensor life. Or maybe you need loading impact protection, so a steel face sensor may be the best choice. There are more housing styles available now than ever. Look at your conditions and choose accordingly.
  • Bunkering
    Are you using the best mounting type? Is your sensor protected from loading impact? Using a protective block can buffer the sensor from the bumps that can happen during the application.
  • Connectivity
    How is the sensor connected to the control and how does that cable survive? The cable is often the problem but there are high durability cable solutions, including TPE jacketed cables, or sacrificial cables to make replacement easier and faster.

When choosing a sensor, you can’t only focus on whether it can fulfill the task at hand, but whether it can fulfill it in the environment of the application.

For more information, visit Balluff.com

Getting Condition Data From The Shop Floor to Your Software

IIoT (Industrial Internet of Things)  is becoming more mainstream, leading to more vendors implementing innovative monitoring capabilities in the new generation of sensors. These sensors are now multifunctional and provide a host of additional features such as self-monitoring.

With these intelligent sensors, it is possible to set up a system that enables continuous monitoring of the machines and production line. However, the essential requirement to use the provided data for analysis and condition monitoring for preventative and predictive maintenance is to get it from the shop floor to the MES, ERP, or other analysis software suites.

There are a variety of ways this can be done. In this post we will look at a few popular ways and methods to do so.

The most popular and straightforward implementation is using a REST API(also known as RESTful API). This has been the de facto standard in e consumer space to transport data. It allows multiple data formats to be transferred, including multimedia and JSON (Javascript Object Notation)

This has certain disadvantages like actively polling for the data, making it unsuitable for a spotty network, and having high packet loss.

MQTT(Message Queuing Telemetry Transport) eliminates the above problem. It’s very low bandwidth and works excellent on unreliable networks as it works on a publish/subscribe model. This allows the receiver to passively listen for the data from the broker. The broker only notifies when there is a change and can be configured to have a Quality of Service(QoS) to resend data if one of them loses connection. This has been used in the IoT world for a long time has become a standard for data transport, so most of software suits have this feature inbuilt.

The third option is to use OPCUA, which is the standard for M2M communication. OPCUA provides additional functionality over MQTT as it was developed with machine communication in mind. Notably, inbuilt encryption allows for secure and authenticated communication.

In summary, below is a comparison of these protocols.

A more detailed explanation can be found for these standards :

REST API : https://www.redhat.com/en/topics/api/what-is-a-rest-api

MQTT : https://mqtt.org/

OPCUA : https://opcfoundation.org/about/opc-technologies/opc-ua/

Turning Big Data into Actionable Data

While RFID technology has been available for almost seventy years, the last decade has seen widespread acceptance, specifically in automated manufacturing. Deployed for common applications like automatic data transfer in machining operations, quality control in production, logistics traceability and inventory control, RFID has played a major role in the evolution of data collection and handling. With this evolution has come massive amounts of data that can ultimately hold the key to process improvement, quality assurance and regulatory compliance. However, the challenge many organizations face today is how to turn all that data into actionable data.

Prominent industry buzzwords like Industry 4.0 and the Industrial Internet of Things (IIOT) once seemed like distant concepts conjured up by a marketing team far away from the actual plant floor, but those buzzwords are the result of manufacturing organizations around the globe identifying the need for better visibility into their operations. Automation hardware and the infrastructure that supports it has advanced rapidly due to this request, but software that turns raw data into actionable data is still very much in demand. This software needs to provide interactive feedback in the form of reporting, dashboards, and real time indicators.

The response to the demand will bring vendors from other industries and start-ups, while a handful of familiar players in automation will step up to the challenge. Competition keeps us all on our toes, but the key to filling the software gap in the plant is partnering with a vendor who understands the needs on the plant floor. So, how do you separate the pretenders from the contenders? I compiled a check list to help.

Does the prospective vendor have:

  • A firm understanding that down time and scrap need to be reduced or eliminated?
  • A core competency in automation for the plant floor?
  • Smart hardware devices like RFID and condition monitoring sensors?
  • A system solution that can collect, analyze, and transport data from the device to the cloud?
  • A user-friendly interface that allows interaction with mobile devices like tablets and phones?
  • The capability to provide customized reports to meet the needs of your organization?
  • A great industry reputation for quality and dependability?
  • A chain of support for pre-sales, installation, and post-sales support?
  • Examples of successful system deployments?
  • The willingness to develop or modify current devices to address your specific needs?

If you can check the box for all of these, it is a safe bet you are in good hands. Otherwise, you’re rolling the dice.

Which Photoelectric Sensor Should I Be Using?

There are many variations within the category of photoelectric sensors, so how do you select the best sensor for your application? Below, I will discuss the benefits of different types of photoelectric sensors and sensing modes.

Through Beam

Through beam sensors consist of an emitter and a receiver. The emitter produces a beam of light, while the receiver identifies whether that light is present or not. So, when an object breaks the beam, an output is triggered by the receiver. Some of the advantages of using the simple through beam technology is that, unlike some of the other photoelectric sensors, it doesn’t matter the color, texture or transparency of your target.

Retroreflective

What if you would like to have a through beam sensor, but don’t have enough room for two sensor heads in your application? Retroreflective sensors have an emitter and receiver within one housing and use a high-quality reflector to reflect the light beam back to the sensor head. This allows for easy connection of just one sensor head, but it doesn’t have the range of your typical through beam sensor. When using these types of sensors, you must factor in how small or reflective your target material is. If you are trying to sense a highly reflective material, then the light reflected back to the receiver could cause the sensor to think an object is present. If you are having these problems, but still want to use a retroreflective sensor, then you should consider versions with a polarizing lens. These lenses make the sensors insensitive to interference with shiny, reflective material.

Fork

Fork sensors include the transmitter and receiver in one housing, and they are already aligned. This saves time and energy during set up. Fork sensors are fantastic for small component and detail detection.

Diffuse

If you don’t have room for a sensor head on each side of your application or even a reflector, or you have had trouble with the alignment of a retroreflective sensor, a diffuse sensor may be a good choice. Diffuse sensors use technology to be able reflect light off the material and back to the sensor. This eliminates the need for a second device or reflector. This significantly reduces set up. You can simply place your target material in front of the sensor and teach it to that point. Once your object reaches that point, the light will be reflected back to the sensor, producing the output. While they are simpler to install, they also have a shorter range compared to through beam sensors and may be affected by your material’s color or the reflectivity or your background… Unless, you have a diffuse sensor with background suppression.

Background Suppression

Diffuse sensors have an emitter and receiver in one housing. In diffuse sensors with background suppression, the emitter and receiver are at a fixed angle so that they intersect at the position of your target material. This will help narrow the operating area (area in which your target material will be entering) and not let reflective material in the background have an influence in your detection.

Conclusion

Photoelectric sensors are simple to use when you need non-contact detection of a material’s presence, color, distance, size or shape, and with their various types, housing and sizes, you can find one that is ideal for your application.