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.

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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.

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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.

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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.

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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.

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

 

 

RFID for Improved Operator Accountability

One of the most fascinating parts of my job is making site visits to manufacturing plants across the country. Getting a first-hand look at how things are made in a modern manufacturing facility is nothing short of amazing. Robots whirling, automatic guided vehicles (AGV’s) navigating the floor, overhead cranes and gantries lifting tons of material over-head, flames shooting from ovens, and metal chips flying create an exciting, but sometimes dangerous, work environment. To some people this may seem like a good reason to avoid these places, but if you are fitted with the appropriate personal protective equipment (PPE) the chances for injury are minimal.

The safety of every human in the plant is the top priority.  This is why there are requirements to wear PPE that is suitable for the environment and the hazards within. The challenge is confirming that everyone is aware of the required equipment, and that they indeed are wearing that equipment.

This can be accomplished with a simple RFID kiosk system. When an operator scans their ID they are asked a series of questions to ensure they are wearing the correct PPE. If the operator confirms they are wearing all the required gear, they can begin work in the area they are assigned. If not, a supervisor will be notified so the correct equipment can be obtained. This method can serve as a daily reminder for what needs to be worn while holding the operator accountable.

Ultimately, it is up to the plant and occupational safety organizations to define what needs to be worn and where it should be worn, but it is the responsibility of the operator to actually wear it. The same system can be used for vendors, visitors or anyone else who ventures out on the plant floor.

Using RFID to Create Transparency in Production

To meet today’s requirements for fast delivery and infinite flexibility, many productions are already set up as flow production with work steps distributed to workstations. As a result, products can be individually adapted in order to optimally meet customer requirements.

The basic prerequisite for this is to continuously know where a product is in the process. Additionally, information should be available about the next workstation and the subsequent work step. Without technical assistance, the required information can only be generated by the employee with much effort. Additionally, you run the risk of production steps being confused and time delays occurring in the production process. One solution to meet the requirements with minimum effort and maximum reliability is to install automated product recognition by using an RFID system.

 
Automated product recognition with an RFID system

To install an RFID system one important prerequisite must be fulfilled. Each product that is planned to be tracked needs a compatible RFID data carrier. This enables an individual connection between the order number and the product, which is then stored in a database.

During the product creation, the stored connection is called up multiple times. Each time it is supplemented by further information. In this way product traceability can be ensured. The connection is initiated by an antenna of the RFID system, which recognizes the data carrier and its ID. The resulting data shows which product is at the workplace, the time stamp, the place of recognition and the order number, all of which are noted in the database.

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Communication between RFID system, database and production employee

 

Reduction of error rate and increase of efficiency in the production

In addition to ensuring traceability, the installation of an RFID system can also significantly reduce the failure rate in the production. The connection to the database allows information to move in two ways. On one hand additional information is provided, while on the other further information is created that can be processed by other systems.

The storage of the time stamp enables an analysis of the duration of each work step. This makes the identification of potential ways to improve in the production possible. If this analysis and the implementation of the system is done consequently, the efficiency in the production can be improved continuously.

 

Tackle Quality Issues and Improve OEE in Vision Systems for Packaging

Packaging industries must operate with the highest standards of quality and productivity. Overall Equipment Effectiveness (OEE) is a scoring system widely used to track production processes in packaging. An OEE score is calculated using data specifying quality (percent of good parts), performance (performance of nominal speed) and equipment availability (percent of planned uptime).

Quality issues can directly impact the customer, so it is essential to have processes in place to ensure the product is safe to use and appropriately labeled before it ships out. Additionally, defects to the packaging like dents, scratches and inadequate labeling can affect customer confidence in a product and their willingness to buy it at the store. Issues with quality can lead to unplanned downtime, waste and loss of productivity, affecting all three metrics of the OEE score.

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Traditionally, visual inspections and packaging line audits have been used to monitor quality, however, this labor can be challenging in high volume applications. Sensing solutions can be used to partly automate the process, but complex demands, including multiple package formats and product formulas in the same line, require the flexibility that machine vision offers. Machine vision is also a vital component in adding traceability down to the unit in case a quality defect or product recall does occur.

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Vision systems can increase productivity in a packaging line by reducing the amount of planned and unplanned downtime for manual quality inspection. Vision can be reliably used to detect quality defects as soon as they happen. With this information, a company can make educated improvements to the equipment to improve repeatability and OEE and ensure that no defective product reaches the customers’ hands.

Some vision applications for quality assurance in packaging include:

  • Label inspection (presence, integrity, print quality, OCV/OCR)
    • Check that a label is in place, lined up correctly and free of scratches and tears. Ensure that any printed graphics, codes and text are legible and printed with the expected quality. Use a combination of OCR (Optical Character Recognition) to read a lot number, expiration date or product information, and then OCV (Optical Character Verification) to ensure legibility.
  • Primary and secondary packaging inspection for dents and damage
    Inspect bottles, cans and boxes to make sure that their geometry has not been altered during the manufacturing process. For example, check that a bottle rim is circular and has not been crushed so that the bottle cap can be put on after filling with product.
  • Safety seal/cap presence and position verification
    Verifying that a cap and/or seal has been placed correctly on a bottle, and/or that the container being used is the correct one for the formula / product being manufactured.
  • Product position verification in packages with multiple items
    In packages of solids, making sure they have been filled adequately and in the correct sequence. In pharmaceutical industries, this can be used to check that blister packs have a pill in each space, and in food industries to ensure that the correct food item is placed in each space of the package.
  • Certification of proper liquid level in containers
    For applications in which it can’t be done reliably with traditional sensing technologies, vision systems can be used to ensure that a bottle has been filled to its nominal volume.

The flexibility of vision systems allows for addressing these complex applications and many more with a well-designed vision solution.

For more information on Balluff vision solutions and applications, visit www.balluff.com.

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:

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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.

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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.

Tracking and Traceability in Mobility: A Step Towards IIoT

In today’s highly competitive automotive environment, it is becoming increasingly important for companies to drive out operating costs in order to ensure their plants maintain a healthy operating profit.

Improved operational efficiency in manufacturing is a goal of numerous measures. For example, in Tier 1 automotive parts manufacturing it is common place to have equipment that is designed to run numerous assemblies through one piece of capital equipment (Flexible Manufacturing). In order to accommodate multiple assemblies, different tooling is designed to be placed in this capital equipment. This reduces required plant floor real-estate and the costs normally required for unidimensional manufacturing equipment. However, with this flexibility new risks are introduced, such as running the machine with incorrect tooling which can cause increased scrap levels, incorrect assembly of parts and/or destruction/damage of expensive tooling, expedited freight, outsourcing costs, increased manpower, sorting and rework costs, and more.

Having operators manually enter recipes or tooling change information introduces the Human Error of Probability (HEP).  “The typical failure rates in businesses using common work practices range from 10 to 30 errors per hundred opportunities. The best performance possible in well managed workplaces using normal quality management methods are failure rates of 5 to 10 in every hundred opportunities.” (Sondalini)

Knowing the frequency of product change-over rates, you can quickly calculate the costs of these potential errors. One means of addressing this issue is to create Smart Tooling whereby RFID tags are affixed on the tooling and read/write antennas are mounted on the machinery and integrated into the control architecture of the capital equipment. The door to a scalable solution has now been opened in which each tool is assigned a unique ID or “license plate” identifying that specific tooling. Through proper integration of the capital equipment, the plant can now identify what tooling is in place at which OP station and may only run if the correct tooling is confirmed in place. In addition, one can then move toward predictive maintenance by placing process data onto the tag itself such as run time, parts produced, and tooling rework data. Collection and monitoring of this data moves the plant towards IIoT and predictive maintenance capabilities to inform key personnel when tooling is near end of life or re-work requirement thus contributing to improved OEE (Overall Equipment Effectiveness) rates.

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For more information on RFID, visit www.balluff.com.

*Source: Mike Sondalini, Managing Director, Lifetime Reliability Solutions, Article: Unearth the answers and solve the causes of human error in your company by understanding the hidden truths in human error rate tables

Why RFID is the VIP of 2019

The “most popular” annual lists don’t usually come out until the end of the year, but I think it is worth mentioning now three applications that have gained substantial momentum this year. With the Smart Factory concept being driven around the globe, RFID has emerged from the shadows and taken its place in the spotlight. The demand for a larger amount of data, more security, and increased visibility into the production process has launched RFID into a leading role when it comes to automation.

Machine Access Control

When considering RFID being utilized for access control, they think about readers located near doorways either outside the building or within the plant. While those readers operate much like the industrial readers, they typically cannot communicate over an industrial communication protocol like Ethernet/IP, Profinet, or IO-Link.  With an industrial access control reader one can limit access to HMIs, PLCs, and various control systems by verifying the user and allowing access to the appropriate controls.  This extra layer of security also ensures operator accountability by identifying the user.

Machine Tool ID

RFID has been used in machining centers for decades. However, it was used mostly in larger scale operations where there were acres of machines and hundreds of tools. Today it’s being used in shops with as few as one machine. The ROI is dependent on the number of tool changes in a shift; not necessarily just the number of machines and the number of tools in the building. The greater the number of tool changes, the greater the risk of data input errors, tool breakage, and even a crash.

Content verification

Since RFID is capable of reading through cardboard and plastic, it is commonly used to verify the contents of a container. Tags are fixed to the critical items in the box, like a battery pack or bag of hardware, and passed through a reader to verify their presence. If, in this case, two tags are not read at the final station then the box can be opened and supplied with the missing part before it ships. This prevents an overload on aftersales support and ensures customers get what they ordered.

While RFID is still widely used to address Work in Process (WIP), asset tracking, and logistics applications, the number of alternative applications involving RFID has skyrocketed due to an increase in demand for actionable data.  Manufacturing organizations around the world have standardized on RFID as a solution in cases where accountability, reliability and quality are critical.

 

When to use optical filtering in a machine vision application

Industrial 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.

So when should you use optical filtering in your machine vision applications? ALWAYS. Image filtering increases contrast, usable resolution, image quality and most importantly, it dramatically reduces ambient light interference, which is the number one reason a machine vision application doesn’t work as expected.

Different applications require different types of filtering. I’ve highlighted the most common.

Bandpass Filtering

Different light spectrums will enhance or de-emphasize certain aspects of the target you are inspecting. Therefore, the first thing you want to do is select the proper color/wavelength that will give you the best contrast for your application. For example, if you are using a red area light that transmits at 617nm (Figure 1), you will want to select a filter (Figure 3) to attach to the lens (Figure 2) that passes the frequency of the area light and filters out the rest of the color spectrum. This filter technique is called Bandpass filtering reference (Figure 4).

This allows only the light from the area light to pass through while all other light is filtered out. To further illustrate the kinds of effects that can be emphasized or de-emphasized we can look at the following images of the same product but with different filters.

Another example of Bandpass filtering can be seen in (Figure 9), which demonstrates the benefit of using a filter in an application to read the LOT code and best before sell date. A blue LED light source and a blue Bandpass filter make the information readable, whereas without the filter it isn’t.

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Figure 9

Narrow Bandpass Filtering

Narrow bandpass filtering, shown in (Figure 10), is mostly used for laser line dimensional measurement applications, referenced in (Figure 11). This technique creates more ambient light immunity than normal Bandpass filtering. It also decreases the bandwidth of the image and creates a kind of black on white effect which is the desired outcome you want for this application.

Shortpass Filtering

Another optical filtering technique is shortpass filtering, shown in (Figure 12), which is commonly used in color camera imaging because it filters out UV and IR light sources to give you a true color image.

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Figure 12

Longpass Filtering

Longpass filtering, referenced in (Figure 13), is often used in IR applications where you want to suppress the visible light spectrum.

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Figure 13

Neutral Density Filtering

Neutral density filtering is regularly used in LED inspection. Without filtering, light coming from the LEDs completely saturates the image making it difficult, if not impossible, to do a proper inspection. Deploying neutral density filtering acts like sunglasses for your camera. In short, it reduces the amount of full spectrum light the camera sees.

Polarization Filtering

Polarization filtering is best to use when you have surfaces that are highly reflective or shiny. Polarization filtering can be deployed to reduce glare on your target. You can clearly see the benefits of this in (Figure 14).

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Figure 14

How flexible inspection capabilities help meet customization needs and deliver operational excellence

As the automotive industry introduces more options to meet the growing complexities and demands of its customers (such as increased variety of trim options) it has rendered challenges to the automotive manufacturing industry.

Demands of the market filter directly back to the manufacturing floor of tier suppliers as they must find the means to fulfill the market requirements on a flexible industrial network, either new or existing. The success of their customers is dependent on the tier supplier chain delivering within a tight timeline. Whereby, if pressure is applied upon that ecosystem, it will mean a more difficult task to meet the JIT (just in time) supply requirements resulting in increased operating costs and potential penalties.

Meeting customer requirements creates operational challenges including lost production time due to product varieties and tool change time increases. Finding ways to simplify tool change and validate the correct components are placed in the correct assembly or module to optimize production is now an industry priority. In addition, tracking and traceability is playing a strong role in ensuring the correct manufacturing process has been followed and implemented.

How can manufacturing implement highly flexible inspection capabilities while allowing direct communication to the process control network and/or MES network that will allow the capability to change inspection characteristics on the fly for different product inspection on common tooling?

Smart Vision Inspection Systems

Compact Smart Vision Inspection System technology has evolved a long way from the temperamental technologies of only a decade ago. Systems offered today have much more robust and simplistic intuitive software tools embedded directly in the Smart Vision inspection device. These effective programming cockpit tools allow ease of use to the end user at the plant providing the capability to execute fast reliable solutions with proven algorithm tools. Multi-network protocols such as EthernetIP, ProfiNet, TCP-IP-LAN (Gigabit Ethernet) and IO-LINK have now come to realization. Having multiple network capabilities delivers the opportunity of not just communicating the inspection result to the programmable logic controller (via process network) but also the ability to send image data independent of the process network via the Gigabit Ethernet network to the cloud or MES system. The ability to over-lay relevant information onto the image such as VIN, Lot Code, Date Code etc. is now achievable.  In addition, camera housings have become more industrially robust such as having aluminum housings with an ingress protection rating of IP67.

Industrial image processing is now a fixture within todays’ manufacturing process and is only growing. The technology can now bring your company a step closer to enabling IIOT by bringing issues to your attention before they create down time (predictive maintenance). They aid in reaching operational excellence as they uncover processing errors, reduce or eliminate scrap and provide meaningful feedback to allow corrective actions to be implemented.

Traceability in Manufacturing – More than just RFID and Barcode

Traceability is a term that is commonly used in most plants today. Whether it is being used to describe tracking received and shipped goods, tracking valuable assets down to their exact location, or tracking an item through production as it is being built, traceability is usually associated with only two technologies — RFID and/or barcode. While these two technologies are critical in establishing a framework for traceability within the plant, there are other technologies that can help tell the rest of the story.

Utilizing vision along with a data collection technology adds another dimension to traceability by providing physical evidence in the form of an image. While vision cameras have been widely used in manufacturing for a long time, most cameras operate outside of the traceability system. The vision system and tracking system often operate independently. While they both end up sending data to the same place, that data must be transported and processed separately which causes a major increase in network traffic.

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Current vision technology allows images to be “stamped” with the information from the barcode or RFID tag. The image becomes redundant traceability by providing visual proof that everything happened correctly in the build process. In addition, instead of sending image files over the network they are sent through a separate channel to a server that contains all the process data from the tag and has the images associated with it. This frees up the production network and provides visual proof that the finished product is what we wanted it to be.

Used separately, the three technologies mentioned above provide actionable data which allows manufacturers to make important decisions.  Used together, they tell a complete story and provide visual evidence of every step along the way. This allows manufacturers to make more informed decisions based on the whole story not just part of it.