Building Blocks of the Smart Factory Now More Economical, Accessible

A smart factory is one of the essential components in Industry 4.0. Data visibility is a critical component to ultimately achieve real-time production visualization within a smart factory. With the advent of IIoT and big-data technologies, manufacturers are finally gaining the same real-time visibility into their enterprise performance that corporate functions like finance and sales have enjoyed for years.

The ultimate feature-rich smart factory can be defined as a flexible system that self-optimizes its performance over a network and self-adapts to learn and react to new conditions in real-time. This seems like a farfetched goal, but we already have the technology and knowhow from advances developed in different fields of computer science such as machine learning and artificial intelligence. These technologies are already successfully being used in other industries like self-driving cars or cryptocurrencies.

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Fig: Smart factory characteristics (Source: Deloitte University Press)

Until recently, the implementation or even the idea of a smart factory was elusive due to the prohibitive costs of computing and storage. Today, advancements in the fields of machine learning and AI and easy accessibility to cloud solutions for analytics, such as IBM Watson or similar companies, has made getting started in this field relatively easy.

One of the significant contributors in smart factory data visualization has been the growing number of IO-Link sensors in the market. These sensors not only produce the standard sensor data but also provide a wealth of diagnostic data and monitoring while being sold at a similar price point as non-IO-Link sensors. The data produced can be fed into these smart factory systems for condition monitoring and preventive maintenance. As they begin to produce self-monitoring data, they become the lifeblood of the smart factory.

Components

The tools that have been used in the IT industry for decades for visualizing and monitoring server load and performance can be easily integrated into the existing plant floor to get seamless data visibility and dashboards. There are two significant components of this system: Edge gateway and Applications.

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Fig: An IIoT system

Edge Gateway

The edge gateway is the middleware that connects the operation technology and Information technology. It can be a piece of software or hardware and software solutions that act as a universal protocol translator.

As shown in the figure, the edge gateway can be as simple as something that dumps the data in a database or connects to cloud providers for analytics or third-party solutions.

Applications

One of the most popular stacks is Influxdb to store the data, Telegraf as the collector, and Grafana as a frontend dashboard.

These tools are open source and give customers the opportunity to dive into the IIoT and get data visibility without prohibitive costs. These can be easily deployed into a small local PC in the network with minimal investment.

The applications discussed in the post:

Grafana

Telegraf

Influxdb

Node-red Tutorial

IO-Link Parameterization Maximizes Functionality, Reduces Expenses

Parameters are the key to maximizing performance and stretching sensor functionality on machines through IO-Link. They are typically addressed during set up and then often underutilized because they are misunderstood. Even users familiar with IO-Link parameters often don’t know the best method for adjustment in their systems and how to benefit from using them.

Using parameters reduces setup time
During standard installation, users must acquire all manuals for each IO-Link device and then hope that all manufactures provided detailed information for parameter setting. All IO-Link device manufacturers are required to produce an IODD file, which can be accessed through the IODD Finder. This IODD file provides a list of available parameters for an IO-Link device which will save the user time by eliminating the need for manuals. Some IO-Link masters can permanently store IODD files for rapid IO-Link parameterization. This feature brings the parameters into an online webpage and gives drop down menus with all available options along with buttons for reading and writing the parameters.

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Maximize functionality of the device
Setpoints can be changed on the fly during normal operation of the machine which will allow a device to expand to the actual range and resolution of each device. Multiple pieces of information can be extracted through IO-Link parameters that are not typically available in process data. One example being an IO-Link pressure sensor with a thermistor included so that temperature can be recorded in the parameters while sending normal pressure values. This allows the user to understand the health of their devices and gather optimal information for more visibility into their processes.

Allows for backup and recovery
IO-Link parameterization allows the user to read and write ALL parameters of IO-Link Data of the device. For example, a two-set point sensor will typically have a teach button/potentiometer that technically limits adjustment for only two parameters and cannot be backed up. This method leaves devices vulnerable to extended downtime from loss of setpoints as well as adding complex teach functions that are not precise. IO-Link parameterization on the other hand pulls teach buttons/potentiometers into the digital world with precision and repeatability. Some IO-Link master blocks have a parameter server function that backs up device parameters in case a sensor needs to be replaced, ultimately providing predictive maintenance, reduced downtime, and easy recipe changes quickly throughout the process.

Using IO Link parameterization is highly important because it reduces setup time, maximizes the functionality of the IO-Link device, and allows for backup and recovery of the parameters. Implementing parameters results in being more cost effective and decreases frustration during the installation process and required maintenance. These parameter functions are just one of the many benefits of using IO Link.

From Design and Build, to Operation and Maintenance, IO-Link Adds Flexibility

With almost twelve million installed nodes as of 2019, IO-Link is being rapidly adopted in a wide range of industries and applications. It is no wonder since it provides more flexibility in how we build and maintain our machines and delivers more data.

Design
As an IEC standard (IEC 61131-9), IO-Link provides consistency in how our devices are connected and integrated. With an already large and ever growing base of manufacturers providing IO-Link devices, we have an incredible amount of choice when it comes to what vendors we use and what devices we incorporate into our systems, all while having the confidence that all of these devices will work and communicate together. Fieldbus independent and based on a point-to-point connection using standard 3 and 4 wire sensor cables, IO-Link allows designers to replace PLC input cards in the control cabinet with machine-mounted IO-Link masters and input hubs. This technology means we are drastically less limited in how we design our machines.

Build/Commissioning
IO-Link is well known for simplifying and reducing build time of machines. Standardization of connections means that readily available double ended quick disconnect sensor cables can replace individually terminated wires, and analogue devices and devices using RS232 connections can be replaced with IO-Link devices which connect directly to a machine mounted IO-Link master or IO hub. Simplified wiring along with delivered diagnostics leads to greatly simplified network architecture and reduced build/commissioning time, as well as increased trouble shooting ability. This all leads to reduced hardware and labor cost.

When it comes to the software side of things, you might think that all of this additional functionality and flexibility increases the burden on programmers, however through the use of configuration files provided by the device manufacturers for both the IO-Link devices and the PLC, this additional functionality and data is at our fingertips with minimal time and effort. With the large adoption of IO-Link and growing manufacturer base comes great amounts of reference material, videos, example programs, and support, all of which can help to get our systems up and running quickly.

Operation
When it comes to operation IO-Link opens a world of possibilities. Bidirectional communication of not only process data but diagnostics and parameter data delivers real time visibility into the entire system during operation all the way down to the device level. Things like automated or guided changeover become possible, for example if a manufacturer produces two different parts on the same line, after the production of part A, devices can be reparameterized for production of part B with the push of a button.

Maintenance
Maintenance sees massive benefits from IO-Link thanks to reduced unplanned downtime through device diagnostics which allow for predictive maintenance practices. If a device does get damaged or fails at an inconvenient time, the issue can be found much quicker and be replaced. Once the IO-Link master recognizes that the device was replaced with the same hardware ID, it can automatically reparameterize the device.

IO-Link is already making our lives easier and providing manufacturers with more possibilities in their automated systems, and as we push into Industry 4.0 it continues to prove its value.

For more information on IO-Link and Industry 4.0 visit www.Balluff.com

 

Improve Error Proofing with IO-Link and IoT-Enabled Sensors

Though error-proofing sensors and poka yoke have been around for decades, continuing advancements related to the Industrial Internet of Things (IIoT) are making both more accessible and easier to maintain.

Balluff - The IO-Link Revolution!

Designed to eliminate product defects by preventing human errors or correcting them in real time, poka yoke has been a key to a lean manufacturing process since it was first applied to industrial applications in 1960. Today, error proofing relies far less on manual mechanisms and more on IoT-enabled error proofing sensors that connect devices and systems across the shop floor.

IoT is enabling immediate control of error-proofing devices such as sensors. This immediacy guards against error-proofing devices being bypassed, which has been a real problem for many years. Now, if a sensor needs adjustment it can be done remotely. A good example of this is with color sensors. When receiving sub-components from suppliers, colors can shift slightly. If the quality group identifies the color lot as acceptable but the sensor does not, often the color sensor is bypassed to keep production moving until someone can address it, creating a vulnerable situation. By using IoT-enabled sensors, the color sensor can be adjusted remotely at any time or from any location.

The detection of errors has been greatly improved by integrating sensors directly into the processes. This is a major trend in flexible manufacturing where poka yoke devices have to be adjusted on-the-fly based on the specific product version being manufactured. This means that buttons or potentiometers on discrete sensors are not adequate. Sensors must provide true data to the control system or offer a means to program them remotely. They must also connect into the traceability system, so they know the exact product version is being made. Connections like this are rapidly migrating to IO-Link. This technology is driving flexible manufacturing at an accelerated rate.

IO-Link enables sensors to process and produce enriched data sets. This data can then be used to optimize efficiencies in an automated process, increase productivity and minimize errors.

Additionally, the easily expandable architecture built around IO-Link allows for easy integrations of poka yoke and industrial identification devices. By keeping a few IO-Link ports open, future expansion is easy and cost effective. For poka yoke, it is important that the system can be easily expanded and that updates are cost-effective.

Using Data to Drive Plant Productivity

What is keeping us from boosting productivity in our plants to the next level? During a recent presentation on Industry 4.0 and IIoT, I was asked this question.

The single biggest thing, in my opinion, that is keeping us from boosting productivity to the next level is a lack of DATA. Specifically, data about the systems and the processes.

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Since the beginning of time, we have been hungry for efficiency. While early man invented more efficient methods to hunt and survive, today we are looking for ways to produce more efficiently in our plants with minimum or zero waste. After exhausting all the avenues for lean operations on plant procedures and our day-to-day activities, we are now looking at how we can recover from unanticipated downtime quickly. I am sure in future we will be seeking information on how can we prevent the downtime altogether.

There are plentiful of reasons for downtime. Just a few examples:

  1. Unavailability of labor – something we might be experiencing these days, when the COVID-19 pandemic has reduced some labor forces
  2. Unavailability of raw materials
  3. Unavailability of replacement components
  4. Unavailability of assets
  5. Failures in machines/components

In this list, the first two reasons, are beyond the scope of this blog’s intentions and frankly somewhat out of controls from the production standpoint.

The next two reasons, however, are process related and the last one is purely based on the choices we made. These three reasons, to a certain extent, can be reduced or eliminated.

If the downtime is process related, we can learn from them and improve our processes with so called continuous improvement initiatives. We can only do these continuous improvements based on observable factors (a.k.a. data) and we cannot improve our processes based on speculations. Well, I shouldn’t say “cannot”, but it will be more like a fluke or luck. It is apt to say “ what can’t be measured, can’t be improved!”

A good example for elaborating my point is change-over in the plant to produce a different product. Unless there is a good process in place for ensuring all the change-over points are properly addressed and all the change parts are correctly installed and replaced, the changeover time could and will likely lead to tremendous amounts of lost productivity. Secondly, if these processes are done manually and not automated, that is also a loss of productivity or, as I like to say, an area for continuous improvement to boost productivity based on observable facts. Sometimes, we take these manual change-overs as a fact of life and incorporate that time required as a part of “planned” downtime.  Of course, if you do change-overs once a year – it may be cost effective to keep the process manual even in today’s situation. But, if your plant has multiple short batch productions per day or per week, then automating the changeovers could significant boost productivity. The cost benefit analysis should help prove if it is continuous improvement or not.

Assets are an important part of the equation for smooth operations. An example would be molds in the stamping plant or cutting-deburring tools in metal working plants. If plants have no visibility or traceability of these important assets for location, shape or form, it could lead to considerable downtime. The calibration data of these tools or number of parts produced with the tool are also important pieces of data that needs to be maintained for efficient operations. Again, this is data about the system and the integration of these traceability initiatives in the existing infrastructure.

Failures in machines or components could cause severe downtime and are often considered as unavoidable. We tackle these failures in a two-step approach. First, we hunt for the problem when it is not obvious, and two, we find the replacement part in the store room to change it out quickly. And, as process improvement, we schedule preventative maintenance to inspect, lubricate and replace parts in our regular planned downtime.

The preventative maintenance is typically scheduled based on theoretical rate of failure. This is a good measure, especially for mechanical components, but, predictive or condition-based maintenance usually yields higher returns on productivity and helps keep plants running smooth. Again, predictive maintenance relies on data about the condition of the system or components. So, where is this data and how do we get to it?

Standardization of interfaces is another important component for boosting productivity. In my next blog, I will share how IO-Link as a technology can help address all of these challenges and boost productivity to the next level.

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.

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.

Top 5 Insights from 2019

With a new year comes new innovation and insights. Before we jump into new topics for 2020, let’s not forget some of the hottest topics from last year. Below are the five most popular blogs from our site in 2019.

1. How to Select the Best Lighting Techniques for Your Machine Vision Application

How to select the best vision_LI.jpgThe key to deploying a robust machine vision application in a factory automation setting is ensuring that you create the necessary environment for a stable image.  The three areas you must focus on to ensure image stability are: lighting, lensing and material handling.  For this blog, I will focus on the seven main lighting techniques that are used in machine vision applications.

READ MORE>>

2. M12 Connector Coding

blog 7.10_LI.jpgNew automation products hit the market every day and each device requires the correct cable to operate. Even in standard cables sizes, there are a variety of connector types that correspond with different applications.

READ MORE>>

3. When to use optical filtering in a machine vision application

blog 7.3_LI.jpgIndustrial image processing is essentially a requirement in modern manufacturing. Vision solutions can deliver visual quality control, identification and positioning. While vision systems have gotten easier to install and use, there isn’t a one-size-fits-all solution. Knowing how and when you should use optical filtering in a machine vision application is a vital part of making sure your system delivers everything you need.

READ MORE>>

4. The Difference Between Intrinsically Safe and Explosion Proof

5.14_LIThe difference between a product being ‘explosion proof’ and ‘intrinsically safe’ can be confusing but it is vital to select the proper one for your application. Both approvals are meant to prevent a potential electrical equipment malfunction from initiating an explosion or ignition through gases that may be present in the surrounding area. This is accomplished in both cases by keeping the potential energy level below what is necessary to start ignition process in an open atmosphere.

READ MORE>>

5. Smart choices deliver leaner processes in Packaging, Food and Beverage industry

Smart choices deliver leaner processes in PFB_LI.jpgIn all industries, there is a need for more flexible and individualized production as well as increased transparency and documentable processes. Overall equipment efficiency, zero downtime and the demand for shorter production runs have created the need for smart machines and ultimately the smart factory. Now more than ever, this is important in the Packaging, Food and Beverage (PFB) industry to ensure that the products and processes are clean, safe and efficient.

READ MORE>>

We appreciate your dedication to Automation Insights in 2019 and look forward to growth and innovation in 2020!

 

 

Make 2020 the Year of Smart Manufacturing

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As we near the end of 2019, it is time to start thinking of New Year’s resolutions. Mostly, these are personal — a promise to eat better, to work out, or save money. But the clean slate of a fresh year on the calendar is also a good time to reevaluate business practices and look at how we can improve on the work floor. And as we enter a new decade, one of the areas every manufacturer needs to be considering is smart manufacturing.

Smart manufacturing uses real-time data and technology to help you meet the changing demands and conditions in the factory and supply chain to meet customer needs. This accurate, yet seemingly vague, definition means that the implementation of smart manufacturing into the workplace can help you meet an array of issues that negatively impact efficiency and the bottom line.

Implementation of smart manufacturing can:

  • Reduce manufacturing costs
  • Permit higher machine availability
  • Boost overall equipment effectiveness
  • Improve asset utilization
  • Allow for traceability of products and parts
  • Enhance supply chain
  • Ease new technology integration
  • Improve product quality
  • Reduce scrap rates
  • Minimize die crashes
  • Decrease unplanned downtime

These are big claims, but all achievable with the modernization of our systems, which is long overdue for most. According to the latest polls, 4 out of 10 manufacturers have little to no visibility into the real-time status of their manufacturing processes and an even higher percentage are utilizing at least some equipment that is far past its intended lifespan.

Half of manufacturers only become aware of system issues only after a breakdown occurs. This is unacceptable in 2020. Much like we expect our personal vehicles to alert us to upcoming issues — think of your service engine light or oil-life indicator —we need insight into the operation and performance of our manufacturing equipment.

Of course, joining the next industrial revolution comes at a cost, but if we put a dollar value on downtime and evaluate the cost benefit of the expected outcomes, it is hard to argue with the figures.

While we don’t need the start of a new year to make major changes, the flipping of the calendar page can give us the push we need to evaluate where we are and where we want to be. So, what are you waiting for?

Define your vision – Determine what you want to accomplish. Be clear and concise in articulating what you want to accomplish.

Set an objective for 2020 – You don’t have to change everything at once. Growth can come slower. What can you accomplish in the coming year?

Identify tactics and projects – Break down your vision into bite-size goals and projects. Prioritize realistic goals and set deadlines.

Link to KPIs – Make sure your smart manufacturing goals tie to key performance indicators. Having measurable results demonstrates just how effective the changes are and how they are improving business overall.

Assign responsibility – Designate owners to each step of the process. Make it someone’s responsibility to implement, track and report on the efforts. If it is everyone’s job, then it is no one’s job.

Environmental Impacts – Choosing the Right Sensor for the Conditions

Last week’s blog spoke about reducing waste and downtime by implementing LEAN manufacturing procedures. This involves taking a proactive approach to improving efficiencies. This post will focus on selecting the right part for the job to reduce failure rates that lead to avoidable machine downtime and increased costs.

Hardly a day passes by where we are not contacted by a desperate end-user or equipment manufacturer seeking assistance with a situation of sensors failing at an unacceptably high rate.  Once we get down to the root cause of the failures, in most cases it’s a situation where the sensors are being applied in a manner which all but guarantees premature failure.

Not all sensors are created equal.  Some are intentionally designed for light-duty applications where the emphasis is more on economic cost rather than the ability to survive in rough service conditions.  Other sensors are specifically designed to meet the challenges of specific application environments, and as a result may carry a higher initial price.

Some things to think about when choosing a sensor for a new application:

  • What kind of environmental conditions will the sensor be exposed to?  For example:
    • Very low or very high temperatures
    • Constant exposure to or immersion in liquid
    • Continuous vibration
    • Extreme shock
    • Disruptive electrical noise (hand-held radios, welding fields, etc.)
    • Chemical contamination
    • Physical abuse or impact
    • Abrasion
    • High pressure washdown procedures
    • Exposure to outdoor conditions of UV sunlight, rain, ice, temperature swings, and condensing humidity
  • Is it possible to relocate the sensor to move it away from the difficult condition?
  • Is the sensor technology the best choice given the kind of application environment that it must operate in?
  • Is there a way to protect the sensor from exposure to the worst of the damaging effects?

When you reach for a catalog or jump on the internet to look for a sensor, it’s a good practice to just stop a moment first and make a list of the environmental challenges that the sensor could face.  Then you will be prepared to make an appropriate selection that best meets your expected application conditions.

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Heavy metal parts being loaded into a welding cell can damage specialty nut detection sensors designed to stick through a hole in a part.  Plunger probes are a better solution.

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Unprotected and non-bunkered sensors in damage prone areas result in premature sensor failure.