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.

Adding a higher level of visibility to older automation machines

It’s never too late to add more visibility to an automation machine.

In the past, when it came to IO-Link opportunities, if the PLC on the machine was a SLC 500, a PLC-5, or worse yet, a controller older than I, there wasn’t much to talk about. In most of these cases, the PLC could not handle another network communication card, or the PLC memory was maxed, or it used a older network like DeviceNet, Profibus or ASi that was maxed. Or it was just so worn out that it was already being held together with hope and prayer. But, today we can utilize IIoT and Industry 4.0 concepts to add more visibility to older machines.

IIOT and Industry 4.0 have created a volume of products that can be utilized locally at a machine, rather than the typical image of Big Data. There are three main features we can utilize to add a level of visibility: Devices to generate data, low cost controllers to collect and analyze the data, and visualization of the data.

Data Generating Devices

In today’s world, we have many devices that can generate data outside of direct communication to the PLC.  For example, in an Ethernet/IP environment, we can put intelligent devices directly on the EtherNet/IP network, or we can add devices indirectly by using technologies like IO-Link, which can be more cost effective and provide the same level of data. These devices can add monitoring of temperature, flow, pressure, and positioning data that can reduce downtime and scrap. With these devices connected to an Ethernet-based protocol, data can be extracted from them without the old PLC’s involvement.  Utilizing JSON, OPC UA, MQTT, UDP and TCP/IP, the data can be made available to a secondary controller.

Linux-Based Controllers

An inexpensive Raspberry Pi could be used as the secondary controller, but Linux-based open controllers with industrial specifications for temperature, vibration, etc. are available on the market. These lower cost controllers can then be utilized to collect and analyze the data on the Ethernet protocol. With a Linux based “sandbox” system, many programming software packages could be loaded, i.e. Node-Red, Codesys, Python, etc., to create the needed logic.

Visualization of Data

Now that the data is being produced, collected and analyzed, the next step is to view the information to add the extra layer of visibility to the process of an older machine. Some of the programming software that can be loaded into the Linux-based systems, which have a form a visualization, like a dashboard (Node-Red) or an HMI feel (Codesys). This can be displayed on a low-cost monitor on the floor near the machine.

By utilizing the products used in the “big” concepts of IIOT and Industry 4.0, you can add a layer of diagnostic visualization to older machines, that allows for easier maintenance, reduced scrap, and predictive maintenance.

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.

 

What Machine Vision Tool is Right for Your Application?

Machine vision is an inherent terminology in factory automation but selecting the most efficient and cost-effective vision product for your project or application can be tricky.

We can see machine vision from many angles of view, for example market segment and application or image processing deliver different perspectives. In this article I will focus on the “sensing element” itself, which scan your application.

The sensing element is a product which observes the application, analyzes it and forwards an evaluation. PC is a part of machine vision that can be embedded with the imager or separated like the controller. We could take many different approaches, but let’s look at the project according to the complexity of the application. The basic machine vision hardware comparison is

  1. smart sensors
  2. smart cameras
  3. vision systems

Each of these products are used in a different way and they fit different applications, but what do they all have in common? They must have components like an imager, lens, lighting, SW, processor and output HW. All major manufacturing companies, regardless of their focus or market segment, use these products, but what purpose and under what circumstances are they used?

Smart Sensors

Smart sensors are dedicated to detecting basic machine vision applications. There are hundreds of different types on the market and they must quickly provide standard performance in machine vision. Don’t make me wrong, this is not necessarily a negative. These sensors are used for simple applications. You do not want to wait seconds to detect QR code; you need a response time in milliseconds. Smart sensors typically include basic functions like:

  • data matrix, barcode and 2D code reading
  • presence of the object,
  • shape, color, thickness, distance

They are typically used in single purpose process and you cannot combine all the features.

Smart Cameras

Smart cameras are used in more complex projects. They provide all the function of smart sensors, but with more complex functions like:

  • find and check object
  • blob detection
  • edge detection
  • metrology
  • robot navigation
  • sorting
  • pattern recognition
  • complex optical character recognition

Due to their complexity, you can use them to find products with higher resolution , however it is not a requirement. Smart cameras can combine more programs and can do parallel several functions together. Image processing is more sophisticated, and limits may occur in processing speed, because of embedded PC.

Vision Systems

Typically, machine vision systems are used in applications where a smart camera is not enough.

Vision system consists of industrial cameras, controller, separated lighting and lens system, and it is therefore important to have knowledge of different types of lighting and lenses. Industrial cameras provide resolution from VGA up to 30Mpxl and they are easy connected to controller.

Vision systems are highly flexible systems. They provide all the functions from smart sensors and cameras. They bring complexity as well as flexibility. With a vision system, you are not limited by resolution or speed. Thanks to the controller, you have dedicated and incomparable processing power which provides multi-speed acceleration.

And the most important information at the end. How does it look with pricing?

You can be sure that smart sensor is the most inexpensive solution. Basic pricing is in the range of $500 – $1500. Smart cameras can cost $2000 – $5000, while a vision system cost would start closer to $6000. It may look like an easy calculation, but you need to take into consideration the complexity of your project to determine which is best for you.

Pros Cons Cost
Smart sensor
    • Easy integration
    • Simple configuration
    • Included lightning and lenses
    • Limited functions
    • Closed SW
    • Limited programs/memory
$
Smart camera
    • Combine more programs together
    • Available functions
    • Limited resolution
    • Slower speed due to embedded PC
$$
Vision system
    • Connect more cameras(up to 8)
    • Open SW
    • Different resolution options
    • Requires skilled machine vision specialist
    • Requires knowledge of lightning and lenses
    • Increased integration time
$$$

Capture

What data can IO-Link provide?

As an application engineer, one of the most frequent questions I get asked by the customers is “What is IO-Link and what data does it contain?”.

Well, IO-Link is the first worldwide accepted sensor communication protocol to be adopted as an international standard IEC61131-9. It is an open standard, and not proprietary to one manufacturer. It uses bi-directional, single line serial communications to transfer data between the machine controller and sensors/actuators. No other communication protocol is manufacturer and fieldbus independent, and yet provides this level of communication down to the sensor/actuator level. It provides the user with three different data types: process data, parameter data, and diagnostics or event data.

Process Data

Process data of an IO-Link smart device is considered the latest state of that device. Containing both input and output data, process data is cyclically exchanged between IO-Link master and IO-Link slave device (i.e. sensor or actuator). The time interval or data update rate solely depends on amount of data, 1 to 32 bytes, and speed at which an IO-Link slave device communicates. IO-Link standard (IEC61131-9) defines three different communications speeds; COM1 is set to 4.8kBaud (slowest), COM2 is set to 38.4kBaud and COM3 is set to 230.4kBaud (fastest). Depending on the device, process data may contain status of inputs or outputs of remote I/O hub, position feedback of linear transducers, pressure feedback from pressure transducers, information from am RFID (Radio Frequency Identification) reader, and so on. For more information about process data content, refresh rate, and data mapping, one should reference an IO-Link slave device datasheet or user manual.

Lastly, process data is then buffered in memory of the IO-Link master device and passed to the controller via a specific fieldbus at request packet interval. Request packet interval is set in the controller settings.

Process Data

Parameter Data

Parameter data contains information and parameters specific to the IO-Link slave device. This data is exchanged acyclically, which means that it is requested from the IO-Link master or controller and not time based. Parameters can be read from a specific device or written to. Parameter data is primarily used for device configuration, or verification. A key advantage of IO-Link is that it gives the controller the full access to IO-Link slave device parameters, down to a sensor/actuator level. This means that your controller, PLC or PC based, can change the configuration of an IO-Link’s slave device dynamically without taking the device off line, and without use of proprietary cabling or configuration software.

Typical use of parameter data is for automatic machine configuration, recipe change, process tuning, maintenance, and easy component replacement.

Parameter Data

Diagnostics or Event Data

Diagnostic data provides the controller with events that affect the operation and performance of the IO-Link smart device. Content can vary depending on the device used, and the manufacturer. IO-Link smart devices can provide crucial data such as load, temperature, stress level, overload and short circuit diagnostics, error codes, configuration or parameter issues, access issues, etc., as part of diagnostic or event data. The event code size is 2 bytes, and in hexadecimal data format. This information can then be interpreted by the controller/user using a lookup table or IODD (I/O Device Description) file. User manual will have diagnostic data table that can be used as a reference.

Diagnostic and Event Data

Conclusion

In conclusion, IO-Link enables a plug-and-play relationship between the controller and the devices on the machine. Using IO-Link data, the controller can automatically recognize and configure an IO-Link slave device that is connected to its network. Process and diagnostic data provide continuous feedback on machine status and health down to a sensor level — the lowest level of the automation pyramid.

Keep in mind that the content of process data is specific to the device and will vary from device to device, and manufacturer to manufacturer. This makes IO-Link data one of the main differentiators between smart devices and their manufacturers. Luckily, IO-Link is an open standard, and fieldbus and manufacturer independent, so you can mix and match devices best suited for your application without worrying about device compatibility, special cabling, or third-party configuration software packages.

automation pyramid

 

Photoelectric Methods of Operation

Photoelectric sensors vary in their operating principles and can be used in a variety of ways, depending on the application. They can be used to detect whether an object is present, determine its position, measure level, and more. With so many types, it can be hard to narrow down the right sensor for your application while accounting for any environmental conditions. Below will give a brief overview of the different operating principles used in photoelectric sensors and where they can be best used.

Diffuse

Diffuse sensors are the most basic type of photoelectric sensor as they only require the sensor and the object being detected. The sensor has a built-in emitter and receiver, so as light is sent out from the emitter and reaches an object, the light will then bounce off the object and enter the receiver. This sends a discrete signal that an object is within the sensing range. Due to the reflectivity being target-dependent, diffuse sensors have the shortest range of the three main discrete operating principles. Background suppression sensors work under the same principle but can be taught to ignore objects in the background using triangulation to ensure any light beyond a certain angle does not trigger an output. While diffuse sensors can be affected by the color of the target object,  the use of a background suppression sensor can limit the effect color has on reliability. Foreground suppression sensors work in the same manner as background suppression but will ignore anything in the foreground of the taught distance.

diffuse

Retro-reflective

Retro-reflective sensors also have the emitter and receiver in a single housing but require a reflector or reflective tape be mounted opposite the sensor for it to be triggered by the received light. As an object passes in front of the reflector, the sensor no longer receives the light back, thus triggering an output. Due to the nature of the reflector, these sensors can operate over much larger distances than a diffuse sensor. These sensors come with non-polarized or polarizing filters. The polarizing filter allows for the sensor to detect shiny objects and not see it as a reflector and prevents any stray ambient light from triggering the sensor.

retroreflective

Through-beam

Through-beam sensors have a separate body for the emitter and receiver and are placed opposite each other. The output is triggered once the beam has been broken. Due to the separate emitter and receiver, the sensor can operate at the longest range of the aforementioned types. At these long ranges and depending on the light type used, the emitter and receiver can be troublesome to set up compared to the diffuse and retro-reflective.

throughbeam

Distance

The previous three types of photoelectric sensors give discrete outputs stating whether an object is present or not. With photoelectric distance sensors, you can get a continuous readout on the position of the object being measured. There are two main ways the distance of the object is measured, time of flight, which calculates how long it takes the light to return to the receiver, and triangulation, which uses the angle of the incoming reflected light to determine distance. Triangulation is the more accurate option, but time of flight can be more cost-effective while still providing good accuracy.

Light type and environment

With each operating principle, there are three light types used in photoelectric sensors: red light, laser red light, and infrared. Depending on the environmental conditions and application, certain light types will fare better than others. Red light is the standard light type and can be used in most applications. Laser red light is used for more precise detection as it has a smaller light spot. Infrared is used in lower-visibility environments as it can pass through more dirt and dust than the other two types. Although infrared can work better in these dirtier environments, photoelectric sensors should mainly be used where build-up is less likely. Mounting should also be considered as these sensors are usually not as heavy duty as some proximity switches and break/fail more easily.

As you can see, photoelectric sensors have many different methods of operation and flexibility with light type to help in a wide range of applications. When considering using these sensors, it is important to account for the environmental conditions surrounding the sensor, as well as mounting restrictions/positioning, when choosing which is right for your application.

For more information on photoelectric sensors, visit this page for more information.

Not All RFID is Created Equal: Is Yours Built for an Industrial Environment?

The retail environments where products are sold look nothing like the industrial environments where they are produced (think of the difference between a new car dealership and an automotive manufacturing plant). Yet the same RFID products developed for retail stores and their supply chain operations are still marketed to manufacturers for production operations. These products may work fine in warehouses, but that does not necessarily qualify them as industrial grade.

IO-Link_RFID

So what are the differences between retail and industrial RFID?

Production environments often require a level of ruggedness, performance, and connectivity that only purpose-built industrial equipment can reliably satisfy. For example, general-purpose RFID equipment may have the physical Ethernet port needed to connect to a PC or server, but will not support EtherNet/IP, Profinet or other industrial protocols that run on PLCs and other industrial automation control equipment. Many retail grade readers need to be supported with an additional protocol conversion, which can require external hardware and slow system performance, and adds to implementation time, difficulty, and expense.

When evaluating RFID equipment, it is essential to make the distinction between what is possible for use in the environment and what is optimal and, therefore, more reliable. There are three fundamental qualities to consider that can determine if RFID systems will perform reliably in demanding production environments:

  • Will the RFID system integrate seamlessly with industrial control systems?
  • Will it provide the reliability and speed that production and their information systems tied in require?
  • Can it maintain uptime and performance long term – will it last on the production line?

RFID is often marketed as a “solution,” however in manufacturing operations, it is almost always used as a supporting technology to provide data and visibility to the MES, ERP, e-Kanban, robotics, asset tracking, material handling, quality control and other systems that run in production facilities. Failure to accurately provide data to these systems at the reliability and speed levels they require eliminates the value of using RFID.

The physical environments in industrial and supply chain settings cause RFID technology to perform differently. Tag density can be a consideration for industrial RFID users like retail, but an industrial environment has much more challenging and powerful potential interference sources, for example, the presence of metal found in most industrial products and environments.

When determining whether RFID products are suitable for a specific environment, it is important to look beyond published marketing hype and misleading specifications. Consider the design and construction of the product and how it could be affected by various work processes. Whenever possible, you should test the products where they will be used rather than in a lab or demonstration area, because the actual work location has interference and environmental conditions that may be overlooked and impossible to duplicate elsewhere.

The key attributes that differentiate industrial RFID equipment from supply chain-oriented alternatives include:

  • Native support for industrial protocols;
  • High tag read reliability and the ability to continuously operate at speeds that won’t slow production systems;
  • Durable housing with secure connectors with IP65 or better rating and relevant certifications for shock, vibration and temperature resistance;
  • The ability to support multiple RFID technologies and supporting devices as needed, including sensors, PLCs, IO-Link, and other industrial automation equipment.

Compromising on any of these criteria will likely result in unnecessary implementation time, support, and replacement costs and increase the risk for system failure.

Do Your Capacitive Sensors Ignore Foam & Condensation for True Level Detection?

Capacitive sensors detect any changes in their electrostatic sensing field. This includes not only the target material itself, but also application-induced influences such as condensation, foam, or temporary or permanent material build-up. High viscosity fluids can cause extensive delays in accurate point-level detection or cause complete failure due to the inability of a capacitive sensor to compensate for the material adhering to the container walls. In cases of low conductive fluids such as water or deionized water and relatively thin container walls, the user might be able to compensate for these sources of failure. Potential material build-up or condensation can be compensated for by adjusting the sensitivity of the sensor, cleaning of the container, or employing additional mechanical measures.

However, this strategy works only if the fluid conductivity stays low and no other additional influencing factors like temperature, material buildup, or filming challenge the sensor. Cleaning fluids like sodium hydrochloride, hydrochloric acid, chemical reagents, and saline solutions are very conductive, which cause standard capacitive sensors to false trigger on even the thinnest films or adherence. The same applies for bodily fluids such as blood, or concentrated acids or alkaline.

Challenges of this type of application are not obvious. This is especially true when the sensors performed well in the initial design phase but fail in the field for no obvious reason. An example of this would be when the sensors on the equipment are setup with deionized water however, the final process requires some type of acid  Difficult and time-consuming setup procedures and unstable applications requiring frequent readjustment are the primary reasons why capacitive level sensors have been historically avoided in certain applications.

Today, there are hybrid technologies employed in capacitive sensors for non-invasive level detection applications that would require little or no user adjustment after the initial setup process. They can detect any type conductive water based liquid through any non-metallic type of tank wall while automatically compensating for material build-up, condensation, and foam.

This hybrid sensing technology helps the sensors to distinguish effectively between true liquid levels and possible interferences caused by condensation, material build-up, or foaming fluids. While ignoring these interferences, the sensors still detect the relative change in capacitance caused by the media but use additional factors to evaluate the validity of the measurement taken before changing state. These sensors are fundamentally insensitive to any non-conductive material like plastic or glass, which allows them to be utilized in non-invasive level applications.

These capacitive sensors provide cost-effective, reliable point-level monitoring for a wide array of medical, biotechnology, life sciences, semiconductor processes, and other manufacturing processes and procedures. This technology brings considerable advantages to the area of liquid level detection, not only offering alternative machine designs, but also reduced assembly time for the machine builders.  Machine designers now have the flexibility to non-invasively detect almost any type of liquid through plastic, glass tubes, or other non-metallic container walls, reducing mechanical adaption effort and fabrication costs.

Discrete indication tasks like fluid presence detection in reagent supply lines, reagent bottle level feedback, chemical levels, and waste container overfill prevention are now a distinct competence for capacitive sensors. Reagents and waste liquids are composed of different formulas depending on the application.  The sensing technology has to be versatile enough to compensate automatically for changing environmental or media conditions within high tolerance limits. Applications that require precision and an extraordinary amount of reliability, such as blood presence detection in cardiovascular instruments or hemodialysis instruments, medical, pharmaceutical machine builders, equipment builders for semiconductor processes can rely now on these hybrid capacitive sensors

Looking for Lean Opportunities? Take a (Gemba) Walk

While we don’t tend to call them resolutions at work, the start of the year is a good time to set goals and implement strategies to get there. And just like at home, 2020 has many of us thinking of ways to be more lean.

For some, trying to determine a lean project to embark upon can be a cumbersome task and it can be difficult to know where or how to start. However, simply by applying the Go and See principle by incorporating a gemba walk in your daily routine can help identify lean opportunities in no time!

The process is simple. Go to the work environment where the work is being done (the gemba), observe the process first hand, and ask process owners open ended questions regarding the work they are performing to gain better insight as to how things are flowing, what obstacles may exist, etc. Below are a list of questions that can aid you on your future gemba walk as you interview process owners:

  1. What are you working on right now?
  2. Is there an established process for completing the task?
  3. What challenges are you facing?
  4. How do you identify a challenge?
  5. What can you fix on your own?
  6. What do you need help with fixing?
  7. Who do you talk to when something goes wrong?
  8. Do you use a visual management board?
  9. If yes, is it useful and how does it help?
  10. If no, why don’t you use one?

After compiling answers to these questions, you can quickly decipher between value-add vs. non-value-add activities and determine a game plan to better (or eliminate) the process, keeping in mind both internal and external customers.

Keeping the gemba walk part of your daily routine makes you visible to the team, creates open dialogue, and provides feedback, suggestions, and ideas — all of which can be used to continually better the process. Plus, the process owners see their input transferred into actions and results, helping instill a never ending lean culture.

So, make the gemba walk part of your New Year’s resolution and never stop improving!

Gemba

Increase Efficiencies and Add Value with Data

Industry 4.0 and the Industrial Internet of Things (IIoT) are very popular terms these days.  But they are more than just buzzwords; incorporating these concepts into your facility adds instant value.

Industry 4.0 and IIoT provide you with much needed data. Having information easily available regarding how well your machines are performing allows for process improvements and increased efficiencies. The need for increased efficiency is driving the industry to improve manufacturing processes, reduce downtime, increase productivity and eliminate waste.  Increased efficiency is necessary to stay competitive in today’s manufacturing market.  With technology continuing to advance and be more economical, it is more feasible than ever to implement increased efficiencies in the industry.

Industry 4.0 and IIoT are the technology concepts of smart manufacturing or the smart factory.  IIoT is at the core of this as it provides access to data directly from devices on the factory floor. By implementing a controls architecture with IO-Link and predictive maintenance practices through condition monitoring parameters from the devices on the machine, Industry 4.0 and IIoT is occurring.

Condition monitoring is the process of monitoring the condition of a machine through parameters.  In other words, monitoring a parameter that gives the condition of the machine or a device on the machine such as vibration, temperature, pressure, rate, humidity etc. in order to identify a significant change in condition, which indicates the possible development of a fault.  Condition monitoring is the primary aspect of predictive maintenance.

IO-Link is a point-to-point communication for devices which allows for diagnostics information without interfering with the process data. There are hundreds of IO-Link smart devices, which provide condition monitoring parameters for the health of the device and the health of the machine.  By utilizing capabilities of IO-Link for diagnostics the ability to gather large amounts of data directly from devices on the factory floor gives you more control over the machines efficiency.  Smart factory concepts are available today with IO-Link as the backbone of the smart machine and smart factory.

Dive into big data with confidence knowing you can gather the information you need with the smart factory concepts available today.