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Navigating the Automotive Plant for Automation Opportunities

When one first looks at an automotive manufacturing plant, the thought of identifying opportunities for automation may be overwhelming to some.

These plants are multi-functional and complex. A typical plant manages several processes, such as:

    • Press, stamping, and dye automation
    • Welding, joining, and body in white
    • Painting
    • Final assembly
    • Robot cell
    • Material handling, including AGV, conveyor, and ASRS
    • Engine and powertrain assembly
    • Casting and machining parts
    • EV and EV sub-processes

Navigating the complexity of the automation processes in your plant to promote more automation products will take some time. You will have to look at this task by:

Time. When tackling a large automotive plant, it’s important to understand how to dissect it into smaller parts and spread out your strategies over a full year or two.

Understanding. Probably the most important thing is to understand the processes and flow of the build assembly process in a plant and then to list the strategic products that can be of use in each area.

Prioritizing. Once you have a good understanding of the plant processes and a strategic timeline to present these technologies, the next step is to prioritize your time and the technology to the highest return on investment. You may now learn that your company could use a great deal of weld cables and weld sensors, for example, so this would be your starting point for presenting this new automation technology.

Knowing who to talk to in the plant. The key to getting the best return on your time and fast approval of your automation technology is knowing the key people in the plant who can influence the use of new automation technology. Typically, you should know/list and communicate monthly with engineering groups, process improvement groups, maintenance groups, purchasing and quality departments. Narrowing down your focus to specific groups or individuals can help you get technology approval faster, etc. Don’t feel like you need to know everyone in the plant, just the key individuals.

Knowing what subjects to discuss. Don’t just think MRO! Talk about the five technology opportunities to have new automation in your plant, including:

        1. MRO
        2. Large programs and specs
        3. Project upgrades
        4. Training
        5. VMI/vending

Most people concentrate on the MRO business and don’t engage in discussions to find out these other ways to introduce automation technology in your plant. Concentrating on all five of these opportunities will lead to placing a lot of automation in the plant for a very long time.

So, when you look at your plant be very excited about all the opportunities to present automation throughout it and watch your technology levels soar to levels of manufacturing excellence.

Good luck as you begin implementing your expansion of automation technology.

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Capacitive, the Other Proximity Sensor

What is the first thing that comes to mind if someone says “proximity sensor?” My guess is the inductive sensor, and justly so because it is the most used sensor in automation today. There are other technologies that use the term proximity in describing the sensing mode, including diffuse or proximity photoelectric sensors that use the reflectivity of the object to change states and proximity mode of ultrasonic sensors that use high-frequency sound waves to detect objects. All these sensors detect objects that are in close proximity to the sensor without making physical contact. One of the most overlooked or forgotten proximity sensors on the market today is the capacitive sensor.

Capacitive sensors are suitable for solving numerous applications. These sensors can be used to detect objects, such as glass, wood, paper, plastic, or ceramic, regardless of material color, texture, or finish. The list goes on and on. Since capacitive sensors can detect virtually anything, they can detect levels of liquids including water, oil, glue, and so forth, and they can detect levels of solids like plastic granules, soap powder, sand, and just about anything else. Levels can be detected either directly, when the sensor touches the medium, or indirectly when it senses the medium through a non-metallic container wall.

Capacitive sensors overview

Like any other sensor, there are certain considerations to account for when applying capacitive, multipurpose sensors, including:

1 – Target

    • Capacitive sensors can detect virtually any material.
    • The target material’s dielectric constant determines the reduction factor of the sensor. Metal / Water > Wood > Plastic > Paper.
    • The target size must be equal to or larger than the sensor face.

2 – Sensing distance

    • The rated sensing distance, or what you see in a catalog, is based on a mild steel target that is the same size as the sensor face.
    • The effective sensing distance considers mounting, supply voltage, and temperature. It is adjusted by the integral potentiometer or other means.
    • Additional influences that affect the sensing distance are the sensor housing shape, sensor face size, and the mounting style of the sensor (flush, non-flush).

3 – Environment

    • Temperatures from 160 to 180°F require special considerations. The high-temperature version sensors should be used in applications above this value.
    • Wet or very humid applications can cause false positives if the dielectric strength of the target is low.
    • In most instances, dust or material buildup can be tuned out if the target dielectric is higher than the dust contamination.

4 – Mounting

    • Installing capacitive sensors is very similar to installing inductive sensors. Flush sensors can be installed flush to the surrounding material. The distance between the sensors is two times the diameter of the sensing distance.
    • Non-flush sensors must have a free area around the sensor at least one diameter of the sensor or the sensing distance.

5 – Connector

    • Quick disconnect – M8 or M12.
    • Potted cable.

6 – Sensor

    • The sensor sensing area or face must be smaller or equal to the target material.
    • Maximum sensing distance is measured on metal – reduction factor will influence all sensing distances.
    • Use flush versions to reduce the effects of the surrounding material. Some plastic sensors will have a reduced sensing range when embedded in metal. Use a flush stainless-steel body to get the full sensing range.

These are just a few things to keep in mind when applying capacitive sensors. There is not “a” capacitive sensor application – but there are many which can be solved cost-effectively and reliably with these sensors.

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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.

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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.

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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.

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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:

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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

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How Condition Monitoring has Evolved and Its Role in IIoT

In recent years, as IIoT and Industry 4.0 have become part of our everyday vocabulary, we’ve also started hearing more about condition monitoring, predictive maintenance (PdM) and predictive analytics. Sometimes, we use these terms interchangeably as well. Strictly speaking, condition monitoring is a root that enables both predictive maintenance and predictive analytics. In today’s blog we will brush up a little on condition monitoring and explore its lineage.

Equipment failures have been around since the beginning of time. Over the years, through observation (collecting data) and brute-force methods, we learned that from time-to-time every piece of equipment needs some TLC. Out of this understanding, maintenance departments came to existence, and there we started having experts that could tell based on touch, smell and noise what is failing or what has gone wrong.

Figure 1: Automation Pyramid

Then we started automating the maintenance function either as a preventative measure (scheduled maintenance) or through some automated pieces of equipment that would collect data and provide alerts about a failure. We proudly call these SCADA systems – Supervisory Control and Data Acquisition. Of course, these systems did not necessarily prevent failures, but help curtail them.  If we look at the automation pyramid, the smart system at the bottom is a PLC and all the sensors are what we call “dumb sensors”. So, that means, whatever information the SCADA system gets would be filtered by the PLC. PLCs were/have been/ and are always focused on the process at hand; they are not data acquisition equipment. So, the data we receive in the SCADA system is only as good as the PLC can provide. That means the information is primarily about processes. So, the only alerts maintenance receives is when the equipment fails, and the process comes to a halt.

With the maintenance experts who could sense impending failures becoming mythological heroes, and  SCADA systems that cannot really tell us the story about the health of the machines, once again, we are looking at condition monitoring with a fresh set of eyes.

Sensors are at the grass root level in the automation pyramid, and until the arrival of IO-Link technology, these sensors were solely focused on their purpose of existence; object detection, or measurement of some kind. The only information one could gather from these sensors was ON/OFF or a signal of 4-20mA, 0-10V, and so on. Now, things are different, these sensors are now becoming pretty intelligent and they, like nosy neighbors, can collect more information about their own health and the environment. These intelligent sensors can utilize IO-Link as a communication to transfer all this information via a gateway module (generally known as IO-Link master) to whomever wants to listen.

Figure 2: IO-Link enabled Balluff photo-eye

The new generation of SCADA systems can now collect information not only from PLCs about the process health, but also from individual devices. For example, a photo-eye can measure the intensity of the reflected light and provide an alert if the intensity drops beyond a certain level, indicating a symptom of pending failure. Or a power supply inside the cabinet providing an alert to the supervisory control about adverse conditions due to increase temperature or humidity in the cabinet. These types of alerts about the symptoms help maintenance prevent unplanned downtime on the plant floor and make factories run more efficiently with reduced scrap, reduced down-time and reduced headaches.

Figure 3: The Next Generation Condition Monitoring

There are many different condition monitoring architectures that can be employed, and we will cover that in my next blog.

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Rise of the Robots: IO-Link Maximizes Utilization, Saves Time and Money

Manufacturers around the world are buying industrial robots at an incredible pace. In the April 2020 report from Tractia & Statista, “the global market for robots is expected to grow at a compound annual growth rate (CAGR) of around 26 percent to reach just under 210 billion US dollars by 2025.” But are we gaining everything we can to capitalize on this investment when the robots are applied? Robot utilization is a key metric for realizing return-on-investment (ROI). By adding smart devices on and around the robot, we can improve efficiencies, add flexibility, and expand visibility in our robot implementations. To maximize robot utilization and secure a real ROI there are key actions to follow beyond only enabling a robot; these are: embracing the open automation standard IO-Link, implementing smart grippers, and expanding end-effector application possibilities.

In this blog, I will discuss the benefits of implementing IO-Link. Future blog posts will concentrate on the other actions.

Why care about IO-Link?

First, a quick definition. IO-Link is an open standard (IEC 61131-9) that is more than ten years old and supported by close to 300 component suppliers in manufacturing, providing more than 70 automation technologies (figure 1). It works in a point-to-point architecture utilizing a central master with sub-devices that connect directly to the master, very similar to the way USB works in the PC environment. It was designed to be easy to integrate, simple to support, and fast to implement into manufacturing processes.

Figure 1 – The IO-Link consortium has close to 300 companies providing more than 70 automation technologies.

Using standard cordsets and 24Vdc power, IO-Link has been applied as a retrofit on current machines and designed into the newest robotic work cells. Available devices include pneumatic valve manifolds, grippers, smart sensors, I/O hubs, safety I/O, vacuum generators and more. Machine builders and equipment OEMs find that IO-Link saves them dramatically on engineering, building and the commissioning of new machines. Manufacturers find value in the flexibility and diagnostic capabilities of the devices, making it easier to troubleshoot problems and recover more quickly from downtime. With the ability to pre-program device parameters, troublesome complex-device setup can be automated, reducing new machine build times and reducing part replacement times during device failure on the production line.

Capture Data & Control Automation

Figure 2 – With IIoT-ready IO-Link sensors and masters, data can be captured without impacting the automation control.

The final point of value for robotic smart manufacturing is that IO-Link is set up to support applications for the Industrial Internet of Things (IIoT). IO-Link devices are IIoT ready, enabling Industry 4.0 projects and smart factory applications. This is important as predictive maintenance and big-data applications are only possible if we have the capabilities of collecting data from devices in, around and close to the production. As we look to gain more visibility into our processes, the ability to reach deep into your production systems will provide major new insights. By integrating IIoT-ready IO-Link devices into robotic automation applications, we can capture data for future analytics projects while not interrupting the control of the automation processes (figure 2).

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Chain of Support: The Link to Performance During Emergencies

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

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

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

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

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