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.

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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.

Capacitive Prox Sensors Offer Versatility for Object and Level Detection

When you think of a proximity sensor, what is the first thing that comes to mind? In most cases it is probably the inductive proximity sensor and justly so because they are the most widely used sensor in automation today. But there are other types of proximity sensors. These include diffuse 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 of these sensors detect objects that are in close proximity of the sensor without making physical contact.

One of the most overlooked proximity sensors on the market today is the capacitive sensor. Why? For some, they have bad reputation from when they were released years ago as they were more susceptible to noise than most sensors. I have heard people say that they don’t discuss or use capacitive sensors because they had this bad experience in the past, however with the advancements of technology this is no longer the case.

Today capacitive sensors are available in as wide of a variety of housings and configurations as inductive sensors. They are available as small as 4mm in diameter, in hockey puck styles, extended temperature ranges, rectangular, square, with Teflon housings, remote sensing heads, adhesive cut-to-length for level detection and a hybrid technology that is capable of ignoring foaming and filming of liquids. The capability and diversity of this technology is constantly evolving.

Capacitive sensors are versatile in solving numerous 1applications. These sensors can be used to detect objects such as glass, wood, paper, plastic, ceramic, and the list goes on and on. The capacitive sensors used to detect objects are easily identified by the flush mounting or shielded face of the sensor. Shielding causes the electrostatic field to be short conical shaped much like the shielded version of the inductive proximity sensor. Typically, the sensing range for these sensors is up to 20 mm.

Just as there are non-flush or unshielded inductive sensors, there are non-flush capacitive sensors, and the mounting and housing2 looks the same. The non-flush capacitive sensors have a large spherical field which allows them to be used in level detection. 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 with the sensor touching the medium or indirectly where the sensor senses the medium through a non-metallic container wall. The sensing range for these sensors can be up to 30 mm or in the case of the hybrid technology it is dependent on the media.

The sensing distance of a capacitive sensor is determined by several factors including the sensing face area – the larger the better. The next factor is the material property of the object or dielectric constant, the higher the dielectric constant the greater the sensing distance. Lastly the size of the target affects the sensing range. Just like an inductive sensor you want the target to be equal to or larger than the sensor. The maximum sensing distance of a capacitive sensor is based on a metal target thus there is a reduction factor for non-metal targets.

As with most sensors today, the outputs of a capacitive sensor include PNP, NPN, push-pull, analog and the increasing popular IO-Link. IO-Link provides remote configuration, additional diagnostics and a window into what the sensor is “seeing”. This is invaluable when working on an application that is critical such as life sciences.

Most capacitive sensors have a potentiometer to allow adjustment of the sensitivity of the sensor to reliably detect the target. Today there are versions that have teach pushbuttons or a teach wire for remote configuration or even a remote amplifier. Although capacitive sensors can detect metal, inductive sensors should be used for these applications. Capacitive sensors are ideal for detecting non-metallic objects at close ranges, usually less than 30 mm and for detecting hidden or inaccessible materials or features.

Just remember, there is one more proximity sensor. Don’t overlook the capabilities of the capacitive sensor.

Are machine diagnostics overburdening our PLCs?

In today’s world, we depend on the PLC to be our eyes and ears on the health of our automation machines. We depend on them to know when there has been an equipment failure or when preventative maintenance is needed. To gain this level of diagnostics, the PLC must do more work, i.e. more rungs of code are needed to monitor the diagnostics supplied to the sensors, actuators, motors, drives, etc.

In terms of handling diagnostics on a machine, I see two philosophies. First, put the bare bones minimum in the PLC. With less PLC code, the scan times are faster, and the PLC runs more efficiently. But this version comes with the high probability for longer downtime when something goes wrong due to the lack of granular diagnostics. The second option is to add lots of diagnostic features, which means a lot of code, which can lessen downtime, but may throttle throughput, since the scan time of the PLC increases.

So how can you gain a higher level of diagnostics on the machine and lessen the burden on the PLC?

While we usually can’t have our cake and eat it too, with Industry 4.0 and IIoT concepts, you can have the best of both of these scenarios. There are many viewpoints of what these terms or ideas mean, but let’s just look at what these two ideas have made available to the market to lessen the burden on our PLCs.

Data Generating Devices Using IO-Link

The technology of IO-Link has created an explosion of data generating devices. The level of diversity of devices, from I/O, analog, temperature, pressure, flow, etc., provides more visibility to a machine than anything we have seen so far. Utilizing these devices on a machine can greatly increase visibility of the processes. Many IO-Link masters communicate over an Ethernet-based protocol, so the availability of the IO-Link device data via JSON, OPC UA, MQTT, UDP, TCP/IP, etc., provides the diagnostics on the Ethernet “wire” where more than just the PLC can access it.

Linux-Based Controllers

After using IO-Link to get the diagnostics on the Ethernet “wire,” we need to use some level of controller to collect it and analyze it. It isn’t unusual to hear that a Raspberry Pi is being used in industrial automation, but Linux-based “sandbox” controllers (with higher temperature, vibration, etc., standards than a Pi) are available today. These controllers can be loaded with Codesys, Python, Node-Red, etc., to provide a programming platform to utilize the diagnostics.

Visualization of Data

With IO-Link devices providing higher level diagnostic data and the Linux-based controllers collecting and analyzing the diagnostic data, how do you visualize it?  We usually see expensive HMIs on the plant floors to display the diagnostic health of a machine, but by utilizing the Linux-based controllers, we now can show the diagnostic data through a simple display. Most often the price is just the display, because some programming platforms have some level of visualization. For example, Node-Red has a dashboard view, which can be easily displayed on a simple monitor. If data is collected in a server, other visualization software, such as Grafana, can be used.

To conclude, let’s not overburden the PLC with diagnostic; lets utilize IIoT and Industry 4.0 philosophy to gain visibility of our industrial automation machines. IO-Link devices can provide the data, Linux-based controllers can collect and analyze the data, and simple displays can be used to visualize the data. By using this concept, we can greatly increase scan times in the PLC, while gaining a higher level of visibility to our machine’s process to gain more uptime.

Why Sensor & Cable Standardization is a Must for End-Users

Product standardization makes sense for companies that have many locations and utilize multiple suppliers of production equipment. Without setting standards for the components used on new capital equipment, companies incur higher purchasing, manufacturing, maintenance, and training costs.

Sensors and cables, in particular, need to be considered due to the following:

  • The large number of manufacturers of both sensors and cables
  • Product variations from each manufacturer

For example, inductive proximity sensors all perform the same basic function, but some are more appropriate to certain applications based on their specific features. Cables provide a similar scenario. Let’s look at some of the product features you need to consider.

Inductive Proximity Sensors Cables
 

·         Style – tubular or block style

·         Size and length

·         Electrical characteristics

·         Shielded or unshielded

·         Sensing Range

·         Housing material

·         Sensing Surface

 

·         Connector size

·         Length

·         Number of pins & conductors

·         Wire gage

·         Jacket material

·         Single or double ended

 

Without standards each equipment supplier may use their own preferred supplier, many times without considering the impact to the end customer. This can result in redundancy of sensor and cable spare parts inventory and potentially using items that are not best suited for the manufacturing environment. Over time this impacts operating efficiency and results in high inventory carrying costs.

Once the selection and purchasing of sensors and cables is standardized, the cost of inventory will coincide.  Overhead costs, such as purchasing, stocking, picking and invoicing, will go down as well. There is less overhead in procuring standard parts and materials that are more readily available, and inventory will be reduced. And, more standardization with the right material selection means lower manufacturing down-time.

In addition, companies can then look at their current inventory of cable and sensor spare parts and reduce that footprint by eliminating redundancy while upgrading the performance of their equipment. Done the right way, standardization simplifies supply chain management, can extend the mean time to failure, and reduce the mean time to repair.

Size Matters When Selecting Sensors for Semiconductor Equipment

As an industry account manager focusing on the semiconductor industry, I’ve come to realize that when it comes to sensors used in semiconductor production equipment, size definitely matters. A semiconductor manufacturing facility, better known as a fab or foundry, can cost thousands of dollars per square foot to construct, not to mention the cost to maintain the facility. Therefore, manufacturers of equipment used to produce semiconductors are under pressure to reduce the footprint of their machines. As the equipment becomes more compact, it becomes more difficult to incorporate optical sensors that are needed for precise object detection.

A self-contained optical sensor that includes the optics along with the required electronics is often much too large. There simply isn’t enough space for mounting in the area where the object is to be detected. An alternative method is to use a remote amplifier containing the electronics with a fiber optic cable leading to the point of detection where the light beam is focused on the target. However, there are some drawbacks to this method that can be difficult to overcome. There are instances where the fiber optic cable is too large and not flexible enough to be routed through the equipment. Also, a tighter beam pattern is often required in semiconductor equipment for precise positioning. To provide a tighter beam pattern with fiber optics, it is necessary to add additional lenses. These lenses increase the size, complexity and cost of the sensor.

1The most effective way to overcome the limitations of fiber optic sensors is to use very small sensor heads connected to a remote amplifier by electric cables, as opposed to fiber optic cables. The photoelectric sensor heads are exceptionally small, and because the cables are extremely flexible they can easily accommodate tight bends. Therefore, these micro-optic photoelectric sensors are particularly well suited for use in semiconductor equipment. The extremely small beam angles and sharply defined light spots are ideal for the precise positioning required for producing semiconductors. No supplementary lensing is required.

2An excellent example of how this micro-optic sensor technology is utilized in semiconductor equipment is for precision wafer detection needed for automated wafer handling. At the end of a robot arm used for wafer handling there is a very thin end-effector known as a blade. By utilizing a very tightly controlled and focused light spot, the sensor can detect wafers just a few μm thick with extreme precision.

3The combination of extremely small optical sensor heads with an external processor unit (amplifier) connected via highly flexible cables is a configuration that is ideal for use in semiconductor production equipment.

 

Sensor and Device Connectivity Solutions For Collaborative Robots

Sensors and peripheral devices are a critical part of any robot system, including collaborative applications. A wide variety of sensors and devices are used on and around robots along with actuation and signaling devices. Integrating these and connecting them to the robot control system and network can present challenges due to multiple/long cables, slip rings, many terminations, high costs to connect, inflexible configurations and difficult troubleshooting. But device level protocols, such as IO-Link, provide simpler, cost-effective and “open” ways to connect these sensors to the control system.

Just as the human body requires eyes, ears, skin, nose and tongue to sense the environment around it so that action can be taken, a collaborative robot needs sensors to complete its programmed tasks. We’ve discussed the four modes of collaborative operation in previous blogs, detailing how each mode has special safety/sensing needs, but they have common needs to detect work material, fixtures, gripper position, force, quality and other aspects of the manufacturing process. This is where sensors come in.

Typical collaborative robot sensors include inductive, photoelectric, capacitive, vision, magnetic, safety and other types of sensors. These sensors help the robot detect the position, orientation, type of objects, and it’s own position, and move accurately and safely within its surroundings. Other devices around a robot include valves, RFID readers/writers, indicator lights, actuators, power supplies and more.

The table, below, considers the four collaborative modes and the use of different types of sensors in these modes:

Table 1.JPG

But how can users easily and cost-effectively connect this many sensors and devices to the robot control system? One solution is IO-Link. In the past, robot users would run cables from each sensor to the control system, resulting in long cable runs, wiring difficulties (cutting, stripping, terminating, labeling) and challenges with troubleshooting. IO-Link solves these issues through simple point-to-point wiring using off-the-shelf cables.

Table 2.png

Collaborative (and traditional) robot users face many challenges when connecting sensors and peripheral devices to their control systems. IO-Link addresses many of these issues and can offer significant benefits:

  • Reduced wiring through a single field network connection to hubs
  • Simple connectivity using off-the-shelf cables with plug connectors
  • Compatible will all major industrial Ethernet-based protocols
  • Easy tool change with Inductive Couplers
  • Advanced data/diagnostics
  • Parametarization of field devices
  • Faster/simpler troubleshooting
  • Support for implementation of IIoT/Industry 4.0 solutions

IO-Link: an excellent solution for simple, easy, fast and cost-effective device connection to collaborative robots.