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.

What to Ask Before You Build an RFID System to Meet Your Traceability Needs

An industrial RFID system is a powerful solution for reliably and comprehensively documenting individual working steps in manufacturing environments. But an industrial RFID system that meets your application needs isn’t available off-the-shelf. To build the system you need, it is important to consider what problems you hope RFID will solve and what return on investments you hope to see.

RFID can deliver many benefits, including process visibility and providing data needed to better manage product quality. It can be used to improve safety, satisfaction and profit margins. It can even be used to help comply with regulatory standards or to manage product recalls. And RFID can be used in a wide range of applications from broad areas like supply management to inventory tracking to more specific applications. These improvements can improve time, cost or performance—though not typically all three.

It is essential to understand and document the goal and how improvements will be measured to in order to plan a RFID system (readers, antennas, tags, cables) to best meet those goals.

Other important questions to consider:

Will the system be centralized or de-centralized? Will the system be license plate only or contain process data on the tag?

How will the data on the tags be used?  Will the information be used to interface with a PLC, database or ERP? Will it be used to provide MES or logical functionality? Or to provide data to an HMI or web browser/cloud interface?

Will the system be required to comply with any international regulations or standards? If so, which ones: EPC Global, Class 1 Gen 2 (UHF only), ISO 15693, or 14443 (HF only)?

What environment does the system need to perform in? Will it be used indoor or outdoor? Will it be exposed to liquids (cleaning fluids, coolants, machine oils, caustics) or high or low temperatures?

Does the RFID system need to work with barcodes or any other human readable information?

What are the performance expectations for the components? What is the read/write range distance from head to tag? What is the station cycle timing? Is the tag metal-mounted? Does the tag need to be reused or be disposable? What communication bus is required?

With a clear set of objectives and goals, the mechanical and physical requirements discovered by answering the questions above, and guidance from an expert, a RFID system can be configured that meets your needs and delivers a strong return on investment.

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

The 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.

On-Axis Ring Lighting

On-axis ring lighting is the most common type of lighting because in many cases it is integrated on the camera and available as one part number. When using this type of lighting you almost always want to be a few degrees off perpendicular (Image 1A).  If you are perpendicular to the object you will get hot spots in the image (Image 1B), which is not desirable. When the camera with its ring light is tilted slightly off perpendicular you achieve the desired image (Image 1C).

Off-Axes Bright Field Lighting

Off-axes bright field lighting works by having a separate LED source mounted at about 15 degrees off perpendicular and having the camera mounted perpendicular to the surface (Image 2A). This lighting technique works best on mostly flat surfaces. The main surface or field will be bright, and the holes or indentations will be dark (Image 2B).

Dark Field Lighting

Dark field lighting is required to be very close to the part, usually within an inch. The mounting angle of the dark field LEDs needs to be at least 45 degrees or more to create the desired effect (Image 3A).  In short, it has the opposite effect of Bright Field lighting, meaning the surface or field is dark and the indentations or bumps will be much brighter (Image 3B).

Back Lighting

Back lighting works by having the camera pointed directly at the back light in a perpendicular mount.  The object you are inspecting is positioned in between the camera and the back light (Image 4A).  This lighting technique is the most robust that you can use because it creates a black target on a white background (Image 4B).

Diffused Dome Lighting

Diffused dome lighting, aka the salad bowl light, works by having a hole at the top of the salad bowl where the camera is mounted and the LEDs are mounted down at the rim of the salad bowl, pointing straight up which causes the light to reflect off of the curved surface of the salad bowl and it creates very uniform reflection (Image 5A).  Diffused dome lighting is used when the object you are inspecting is curved or non-uniform (Image 5B). After applying this lighting technique to an uneven surface or texture, hotspots and other sharp details are deemphasized, and it creates a sort of matte finish to the image (Image 5C).

Diffused On-Axis Lighting

Diffused on-axis lighting, or DOAL, works by having a LED light source pointed at a beam splitter and the reflected light is then parallel with the direction that the camera is mounted (Image 6A).  DOAL lighting should only be used on flat surfaces where you are trying to diminish very shiny parts of the surface to create a uniformed image.  Applications like DVD, CD, or silicon wafer inspection are some of the most common uses for this type of lighting.

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Image 6A

 

Structured Laser Line Lighting

Structured laser line lighting works by projecting a laser line onto a three-dimensional object (Image 7A), resulting in an image that gives you information on the height of the object.  Depending on the mounting angle of the camera and laser line transmitter, the resulting laser line shift will be larger or smaller as you change the angle of the devices (Image 7B).  When there is no object the laser line will be flat (Image 7C).

Real Life Applications 

The images below, (Image 8A) and (Image 8B) were used for an application that requires the pins of a connector to be counted. As you can see, the bright field lighting on the left does not produce a clear image but the dark field lighting on the right does.

This next example (Image 9A) and (Image 9B) was for an application that requires a bar code to be read through a cellophane wrapper.  The unclear image (Image 9A) was acquired by using an on-axis ring light, while the use of dome lighting (Image 9B) resulted in a clear, easy-to-read image of the bar code.

This example (Image 10A), (Image 10B) and (Image 10C) highlights different lighting techniques on the same object. In the (Image 10A) image, backlighting is being used to measure the smaller hole diameter.  In image (Image 10B) dome lighting is being used for inspecting the taper of the upper hole in reference to the lower hole.  In (Image 10C) dark field lighting is being used to do optical character recognition “OCR” on the object.  Each of these could be viewed as a positive or negative depending on what you are trying to accomplish.

RFID: Using Actionable Data to Make Critical Decisions

While RFID technology has been in use since the 1950s, wide-spread implementation has come in waves over the years. Beginning with military applications where it was used to identify friend or foe aircraft, to inventory control in the retail industry, and now to the manufacturing space where it is being used to manage work in process, track assets, control inventory, and aid with automatic replenishment.

The bottom line is RFID is critical in the manufacturing process. Why? Because, fundamentally, it provides actionable data that is used to make critical decisions. If your organization has not yet subscribed to RFID technology then it is getting ready to. This doesn’t mean just in the shipping and receiving area.  Wide-spread adoption is happening on the production line, in the tool room, on dies, molds, machine tools, on AGV’s, on pallets, and so much more.

Not an RFID expert? It’s ok. Start with a quick overview.

Learn about the fundamentals of a passive RFID system here.

In the past, controls engineers, quality assurance managers, and maintenance supervisors were early adopters because RFID played a critical role in giving them the data they needed. Thanks to global manufacturing initiatives like Smart Factory, Industry 4.0, the Industrial Internet of things (IIOT) and a plethora of other manufacturing buzz words, CEOs, CFOs, and COOs are driving RFID concepts today. So, while the “hands-on” members of the plant started the revolution, the guys in the corner offices quickly recognized the power of RFID and accelerated the adoption of the technology.

While there is a frenzy in the market, it is important to keep a few things in mind when exploring how RFID can benefit your organization:

  • Choose your RFID partner based on their core competency in addressing manufacturing applications
  • Make sure they have decades of experience manufacturing and implementing RFID
  • Have them clearly explain their “chain of support” from local resources to experts at the HQ.
  • Find a partner who can clearly define the benefits of RFID in your specific process (ROI)
  • Partner with a company that innovates the way their customers automate