The Importance of Data Accessibility with IIoT

20160809_100331 (1) Typically a college student is asked two questions: “What are you studying?” and “What would you like to do with your degree?” In my case, I always answer with “Computer Science” and “I have no idea”. Lately, the field that has grabbed my interest the most is the Internet of Things (IoT). The concept of data transfer and communication between ordinary utilities is going to revolutionize the way we go about our day to day tasks. Home automation is a key example of this. We have found ways to expedite those pesky tasks that nobody enjoys doing by simply automating them.

I’ve come to realize that there is data everywhere; we just need to take the opportunity to use it. I’ve done this in a few small side projects around my apartment. Is the door locked? Are my lights on? Did the refrigerator door completely close? These are all examples of data that is useful to me at any point in time. The trick is making it available. Using a low power microcontroller and a few sensors, I’m able to host this data and view it at any point in time. IoT has the capability of effectively improving our energy efficiency, security, and productivity simply by making data readily available.

IoT screenLikewise, these same concepts apply to industrial automation. I’ve spent the last few months developing a web application to demonstrate Industrial Internet of Things (IIoT).  The web app simply hosts a live feed of data from a conveyor system. From any computer on the network, we can see crucial data such as conveyor accumulation, sensor status or even maintenance needs.  Once this data is made available, we can even automate the analysis. For example, on a conveyor, we can look at the number of packages that go by every day. A simple script that increments by one for every passing object can give a very accurate representation of day to day productivity. More intense algorithms could analyze trends in mass quantities of data return valuable results. All of this is done simply by making data continuously accessible.

According to Business Insider, by 2020, there will be 34 billion devices connected to the internet and that there will be $6 trillion spent on incorporating and integrating IoT.  As a student with a passion for technology, I see a lot of potential in this field.  So next time I’m asked what I plan on doing with my degree, I might say an IoT developer. It’s a fascinating subject that only has room to grow.

To learn more about IIoT visit www.balluff.us.

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Machine Tool Identification with RFID -Automation for Advanced Machining

When most people think of automation in manufacturing the first thing that comes to mind is usually a robot. Without a doubt, robots play an integral part in automating the production process, and let’s face it they are pretty cool. However, there is an often overlooked topic in the automation discussion and that is Automatic Data Collection (ADC), which includes barcode and RFID technology. While it doesn’t carry the “cool factor” quite as well as robotics, RFID has helped automate manufacturing, specifically machining, over the last 30 years.

How is it used?

An RFID tag is placed in the tool holder and stays put for the life of the tool. The tag essentially acts as a mini database that can be read and written to thousands of times.

What type of data is typically written to the tag?

Tool Life, Tool Chain Pocket location, Offset Data, Maintenance Info, etc. Up to 2K of info can be written and read and erased and written again. In addition, this information can be updated on the spot.

What are the benefits of using RFID in Machine Tools?

RFID Improves Quality, Increases Efficiency, and Reduces overall Costs by:

Maximizing Tool and Machine Utilization

  • Precise up-to-date tool life information
  • Accurate transfer of tool offset data
  • Continuous tracking of the tool

Minimizing Human Error

  • Eliminates human data entry
  • Automates transfer of data from presetter to machine
  • Data can be accessed directly on the plant floor as opposed to a database lookup

ToolIDRFID is a tried and true technology that will continue to have a great impact on the machining process. Organizations all over the globe are saving millions every year by utilizing this simple method of collecting and transferring data. Machine tool ID is a no-brainer when quality, efficiency, and productivity matters!

For more information or to learn more visit www.balluff.us/rfid.

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To OCV, or OCR, that is the question

VisionOWLTo OCV, or OCR: that is the question:
Whether ’tis nobler to use OCV (Optical Character Verification) to verify print,
Or OCR (Optical Character Recognition) to decode a sea of print troubles.
And by decoding will turmoil end?
No more to have the camera sleep; we program the TTL (Time to Live)
That font won’t print correctly, ’tis a communication issue?
The undiscover’d font no longer puzzles the will as I can check with OCV.

OCR in Machine Vision software has a library of numbers, letters, fonts, and special characters. Sometimes print is not readable when quality checked using the ISO 1831:1980 specification library. Fortunately, we can teach printed characters utilizing OCV. To verify the quality of print, it can be graded following the ISO 15415,15416 AIM DPM-1-2006/ISO29158 standard. This standard also checks print quality when 1D or 2D barcodes are read.

Hence, methinks even Shakespeare would be impressed by modern-day OCV and OCR technology.

To learn more about machine vision visit www.balluff.us/vision.

Special thanks to Diane Weymier-Dodd for her contribution to this post. 

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Industry 4.0 & IIoT, who cares?!?! You should.

(If you aren’t sure what Industry 4.0 or IIoT (Industrial Internet of Things) are, take a look at these previous blog entries.)

I’m amazed at all the research published each week presenting the value Industry 4.0 and IIoT are bringing to manufacturing.  And the articles about Industry 4.0 and IIoT are not just in industry rags, there are mainstream publications like Fortune & Forbes who are aware of and presenting the power of Industry 4.0 to the masses.

But why should anyone even care?

Looking backwards a decade, no one should be surprised that an explosion of data has occurred.  In 2013 the IMS found the Compound Annual Growth Rate (CAGR) of Ethernet-based automation components was 16.4 percent in 2012.  It was outpacing fieldbus growth dramatically in every category and predicted strong CAGR through 2016.  And taking a look forward provides just as exciting an outlook in the global industrial Ethernet market as Technavio is expecting growth at a CAGR of more than 15% for 2016 through 2020!

industry4.0-2So as I look at the economic effects of IIoT, Morgan Stanley sees: investments in the automation industry are expected to grow at a faster pace than the GDP, capital budgets for IIoT type investments will grow 18% and greater than 70% of respondents believe IIoT is an important strategy for their company.  And with 73% of companies investing more than 20% of their technology budget on Big Data analytics and growing, this trend toward Industry 4.0 does not seem to be letting up.

But why are manufacturer’s making these investments?

This infographic really summarizes well how I feel our situation in the US today is laid out.

Infographic

We need upgrades and investment in US manufacturing infrastructure.  And to remain successful we need to improve production efficiency and evolve towards flexible manufacturing processes.  In a recent survey from SCM World the benefits of Smart Manfucaturing and can provide a 48% reduction in unplanned downtime from IIoT solutions. WOW!  Can you imagine the kinds of investments we could make if we weren’t throwing our money into the downtime fire?  In this same survey close to two thirds of respondents said they are ready now or will be in 5 years for implementation of IIoT solutions.

The kind of focus and growth I’m reading about every week is driving investments and benefits for all stakeholders in manufacturing and it would be smart to take a look at where your company stacks up.

If you are interested in seeing how Balluff enables & scales Industry 4.0 and IIoT, visit our website at www.balluff.us.

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Level Detection Basics – Where to begin?

Initially I started to write this blog to compare photoelectric sensors to ultrasonic sensors for level detection. This came to mind after traveling around and visiting customers that had some very interesting applications. However, as I started to shed some light on this with photoelectrics, sorry for the pun but it was intended, I thought it might be better to begin with some application questions and considerations so that we have a better understanding of the advantages and disadvantages of solutions that are available. That being said I guess we will have to wait to hear about ultrasonic sensors until later…get it, another pun. Sorry.

Level detection can present a wide variety of challenges some easier to overcome than others. Some of the questions to consider include the following with some explanation for each:

  • What is the material of the container or vessel?
    • Metallic containers will typically require the sensor to look down to see the media. This application may be able to be solved with photoelectrics, ultrasonics, and linear transducers or capacitive (mounted in a tube and lowered into the media.
    • SmartLevelNon-metallic containers may provide the ability for the sensors look down to see the media with the same technologies mentioned above or by sensing through the walls of the container. Capacitive sensors can sense through the walls of a container up to 4mm thick with standard technology or up to 10mm thick using a hybrid capacitive technology offered by Balluff when detecting water based conductive materials. If the container is clear or translucent we have photoelectric sensors that can look through the side walls to detect the media. You can get more information in our white paper, SMARTLEVEL Technology Accurate point level detection.
  • What type of sensing is required? The short answer to this is level right? However, there are basically two different types of level detection. For more information on this refer to the Balluff Basics on Level Sensing – Discrete vs. Continuous.
    • Single point level or point level sensing. This is typically accomplished with a single sensor that allows for a discrete or an on-off signal when the level actuates the sensor. The sensor is mounted at the specific level to be monitored, for instance low-low, low, half full (the optimistic view), high, or high-high. These sensors are typically lower cost and easier to implement or integrate into the level controls.
    • Example of in-tank continuous level sensor

      Example of in-tank continuous level sensor

      Continuous or dynamic level detection. These sensors provide an analog or continuous output based on the level of the media. This level detection is used primarily in applications that require precise level or precision dispensing. The output signals are usually a voltage 0-10V or current output 4-20mA.  These sensors are typically higher cost and require more work in integrating them into system controls.  That being said, they also offer several advantages such as the ability to program in unlimited point levels and in the case of the current output the ability to determine if the sensor is malfunctioning or the wire is broken.

Because of the amount of information on level detection this will be the first in a series on this topic. In my next blog I will discuss invasive vs non-invasive mounting and some other topics. For more information visit www.balluff.us.

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Predictive Maintenance for Zen State of Manufacturing

Industry4.0In a previous entry, Mission Industry 4.0 @ Balluff, I explained that the two primary objectives for Balluff’s work in the area of Industry 4.0 are to help customers achieve high production efficiencies in their  automation and achieve  ‘batch size one’ production.

There are several levers that can be adjusted to achieve high levels of manufacturing efficiencies in the realm of IIoT (Industrial Internet of Things). These levers may include selecting quality of production equipment, lean production processes, connectivity and interoperability of devices, and so on. Production efficiency in the short term can be measured by how fast row materials can be processed into the final product – or how fast we deliver goods from the time the order comes in. The later portion depends more on the entire value-chain of the organization. Let’s focus today’s discussion on manufacturing – inside the plant itself.  The long-term definition of production efficiency in the context of manufacturing incorporates the effectiveness of the production system or the automation at hand. What that means is the long-term production efficiency involves the health of the system and its components in harmony with the other levers mentioned above.

The Zen state of manufacturing – nothing important will come up on Google for this as I made this phrase up. It is the perfect state of the entire manufacturing plant that continues production without hiccups all days, all shifts, every day. Does it mean zero-maintenance? Absolutely not, regular maintenance is necessary. It is one of those ‘non-value added but necessary’ steps in the lean philosophy.  Everyone knows the benefits of maintenance, so what’s new?

Well, all manufacturing facilities have a good, in some cases very strictly followed maintenance schedule, but these plants still face unplanned downtimes ranging from minutes to hours. Of course I don’t need to dwell on the cost associated with unplanned downtime. In most cases, there are minor reasons for the downtime such as a bad sensor connection, or cloudy lens on the vision sensor, etc. What if these components could alert you well in advance so that you could fix it before they go down? This is where Predictive Maintenance (PdM) comes in. In a nutshell, PdM uses actual equipment-performance data to determine the condition of the equipment so that the maintenance can be scheduled, based on the state of the equipment. This approach promises cost savings over “time-based” preventive maintenance.

PowerSuppliesIt is not about choosing predictive maintenance over preventive maintenance. I doubt you could achieve the Zen state with just one or the other. Preventive and predictive maintenance are both important – like diet and exercise. While preventive maintenance focuses on eliminating common scenarios that could have dramatic impact on the production for long time, predictive maintenance focuses on prolonging the life of the system by reducing costs associated with unnecessary maintenance.  For example, it is common practice in manufacturing plants to routinely change power supplies every 10 years, even though the rated life of a power supply under prescribed conditions is 15 years. That means as a preventive measure the plants are throwing away 30% life left on the power supply. In other words, they are throwing away 30% of the money they spent on purchasing these power supplies. If the power supplies can talk, they could probably save you that money indicating that “Hey, I still have 30% life left, I can go until next time you stop the machine for changing oil/grease in that robot!”

In summary, to achieve the zen state of manufacturing, it is important to understand the virtues of predictive maintenance and condition monitoring of your equipment. To learn more visit www.balluff.us.

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Enhancing Stepper Motor Systems with Linear Encoders

Tabletop automation is a trend that is gaining momentum, especially in the fields of medical laboratory automation and 3D printing. Both of these applications demand a level of linear positioning accuracy and speed that might suggest a servomotor as a solution, but market-driven cost constraints put most servos out of financial consideration. New advances in stepper motor design, including higher torque, higher power ratings, and the availability of closed-loop operation via integrated motor encoder feedback are enabling steppers to expand their application envelope to include many tasks that formerly demanded a servo system.

Meeting the Demand for Even More Accurate, More Reliable Positioning

As tabletop automation development progresses, performance demands are increasing to the point that steppers systems may struggle to meet requirements. Fortunately, the addition of an external linear encoder for direct position feedback can enhance a stepper system to enable the expected level of reliable accuracy. An external linear encoder puts drive-mechanism non-linearity inside the control loop, meaning any deviations caused by drive component inaccuracy are automatically corrected and compensated by the overall closed-loop positioning system. In addition, the external linear encoder provides another level of assurance that the driven element has actually moved to the position indicated by the number of stepper pulses and/or the movement reported by the motor encoder. This prevents position errors due to stepper motor stalling, lost counts on the motor encoder, someone manually moving the mechanism against motor torque, or drive mechanism malfunction, i.e. broken drive belt or sheared/skipped gearing.

Incremental, Absolute, or Hybrid Encoder Signals

bmlThe position signals from the external encoder are typically incremental, meaning a digital quadrature square wave train of pulses that are counted by the controller. To find a position, the system must be “homed” to a reference position and then moved the required number of counts to reach the command position. The next move requires starting with the position at the last move and computing the differential move to the next command position. Absolute position signals, typically SSI (synchronous serial interface) provide a unique data value for each position. This position is available upon power-up…no homing movement is required and there is no need for a pulse counter. A recent innovation is the hybrid encoder, where the encoder reads absolute position from the scale, but outputs a quadrature incremental pulse train in response to position moves. The hybrid encoder (sometimes referred to as “absolute quadrature”) can be programmed to deliver a continuous burst of pulses corresponding to absolute position at power up, upon request from the controller, or both.

For more information about magnetic linear encoder systems, visit www.balluff.us.

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Simplifying wiring with sensors and Multiple Interface Blocks

When machine builders build assembly machines for their customers they want to keep the wiring as clean and clear as possible for an attractive machine but more importantly the ease of troubleshooting in the event of a failure. Simplifying connections with unnecessary cables and splitters not only makes it easier for the maintenance technicians to trouble shoot but it also saves the company money with unneeded product and components to inventory and maintain.

V-Twin sensor with one cable

V-Twin sensor with one cable

In the past it was common practice to wire sensors and cables all the way back into a terminal box located in sections of an assembly line. This could be very difficult to track down the exact sensor cable for repair and furthermore in some cases five meter cables or longer would be used to make the longer runs back to the terminal box. The terminal boxes would also get very crowded further complicating trouble shooting methods to get the assembly lines back up and running production. This is where Interface Blocks come in and can provide a much cleaner, effective way to manage sensor connections with significantly decreasing downtime.

For example: If our customer has a pneumatic cylinder that requires two sensors, one for the extended position and one for the retracted positon. The customer could run the sensor cables back to the Interface Block. This sometimes is used with a splitter to go into one port to provide the outputs for both sensors only using one port. Now we can take this a step further by using twin magnetic field sensors (V-Twin) with one connection cable. This example eliminates the splitter again eliminating an unneeded component. As you can see in the reference examples below this is a much cleaner and effective way to manage sensors and connections.

BMFvsVTwin

For more information visit www.balluff.us.

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How do I see PLC data from my smartphone?

From my smartphone, I can do anything from making coffee to adjusting my home thermostat. This wave of appliances and other physical devices connecting and communicating through a network is known as the Internet of Things and it’s playing a crucial role in industry. With the Industrial Internet of Things (IIoT) we can now monitor PLC data without ever intruding on the PLC. Let’s take a look at how I implemented PLC tags on a web application.

IIoT_computer The first step is to download OPC UA historian software. OPC UA stands for Open Platform Communications Unified Architecture. OPC is a client/server communication standard that was made as a gateway between the PLC and a Windows PC. The UA was added as an upgrade that allowed communication across other operating systems such as Linux and iOS along with other added functionality improvements. Once this software is running and the PLC and PC are communicating, we can work on hosting that data.

IIoT_StructureHosting the controller data can seem like a daunting task at first due to the many different options in software and programming languages to use. For example: Ruby, PHP, ASP, ASP.NET and much more are available for back-end development. For my web app, I used SQL to host the data from the OPC UA software. As for the back-end, I went with node.js because it has great packages for working with SQL; in addition to the fact that node.js uses JavaScript syntax which I’m familiar with. The front end of the app was written with HTML and CSS with JavaScript for interactivity. With all these elements in place, I was ready to host the server on the PC to host PLC data.

With smart IO-Link sensors on our conveyor I was able to look at diagnostic and functional data in the PLC and setup an interactive screen at the conveyor for viewing production and maintenance information.

And now I can even check my sensor outputs with the same smartphone that just made my coffee and adjusted my office’s temperature.

IIoT_warehouse

You can learn more about the Industrial Internet of Things at www.balluff.us.

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QR Codes for Business vs Industry

QRCode

Example of a QR code for business use

In a previous post I discussed the different types of bar codes. Aside from the 1D bar codes that we see in the grocery store, the most common type of bar code today is the QR code.

The QR code was 1st designed for the automotive industry to track vehicles in the assembly process. The QR code system became popular outside the automotive industry due to its greater storage capacity compared to standard UPC bar codes. A QR code can have up to 7,089 ASCII characters and can read numeric, alphanumeric, byte/binary, and kanji. Businesses often use this type of QR code on vehicles and products for advertising. When a picture is taken with a cell phone, typically in a QR code reader app, the user will be taken to a website for more information.

Sharpshooter vision sensor for reading micro & QR codes

Sharpshooter vision sensor for reading micro & QR codes

Micro QR codes, on the other hand, have a limitation of 35 digits of numeric characters. These are usually seen in industrial applications. For example, they are seen on cam shafts, crankshafts, pistons, and circuit boards. An example of data that is often written to a micro QR code would be a serial number to track and trace through an assembly plant. An industrial vision sensor is typically needed to decipher micro QR codes.

ILoveBalluffQRCodes

An example of a QR code (left) vs a micro QR code (right)

For more information visit www.balluff.us/vision.

 

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