Looking Into & Through Transparent Material With Photoelectric Sensors

Advance automated manufacturing relies on sensor equipment to ensure each step of the process is done correctly, reliably, and effectively. For many standard applications, inductive, capacitive, or basic photoelectric sensors can do a fine job of monitoring and maintaining the automated manufacturing process. However, when transparent materials are the target, you need a different type of sensor, and maybe even need to think differently about how you will use it.

What are transparent materials?

When I think of transparent materials, water, glass, plexiglass, polymers, soaps, cooling agents, and packaging all come to mind. Because transparent material absorbs very little of the emitted red LED light, standard photoelectric sensors struggle on this type of application. If light can make its way back to the receiver, how can you tell if the beam was broken or not? By measuring the amount of light returned, instead of just if it is there or not, we can detect a transparent material and learn how transparent it is.

Imagine being able to determine proper mixes or thicknesses of liquid based on a transparency scale associated to a value of returned light. Another application that I believe a transparent material photoelectric senor would be ideal for is the thickness of a clear bottle. Imagine the wall thickness being crucial to the integrity of the bottle. Again, we would measure the amount of light allowed back to the receiver instead of an expensive measurement laser or even worse, a time-draining manual caliper.

Transparent material sensor vs. standard photoelectric sensor

So how does a transparent material sensor differ from a standard photoelectric sensor? Usually, the type of light is key. UV light is absorbed much greater than other wavelengths, like red or blue LEDs you find in standard photoelectric sensors. To add another level, you polarize that UV light to better control the light back into the receiver. Polarized UV light with a polarized reflector is the best combination. This can be done on a large or micro scale based on the sensor head size and build.

Uses for transparent material sensor include packaging trays, level tubes, medical tests, adhesive extrusion, and bottle fill levels, just to name a few. Transparent materials are everywhere, and the technology has matured. Make sure you are looking into specialized sensor technologies and working through best set-up practices to ensure reliable detection of transparent materials.

Condition Monitoring & Predictive Maintenance: Addressing Key Topics in Packaging

A recent study by the Packaging Machinery Manufacturers Institute (PMMI) and Interact Analysis takes a close look at packaging industry interest and needs for Condition Monitoring and Predictive Maintenance. Customer feedback reveals interesting data on packaging process pain points and the types of machines and components which are best monitored, the data which should be gathered, current maintenance approaches, and the opportunity for a better way: Condition Monitoring and Predictive Maintenance.

What keeps customers awake at night?

The PMMI survey indicates that form, fill & seal machines are very critical to packaging processes and more likely to fail than many other machines. Also critical to the process and a common failure point are filling & dosing machines, and labeling machines.

These three categories of machines are in use in primary packaging and are often the key components in the production line; the downstream processes are usually less critical. They often process a lot of perishable products at high speeds, therefore, any downtime is a big problem for overall equipment effectiveness (OEE), quality, and profitability.

In terms of the components on these machines that are most likely to fail, the ones are pneumatic systems, gearboxes, motors/drives, and sensors.

How can customers reduce unplanned downtime and improve OEE?

Our data shows that the top customer issue is unplanned machine breakdowns, but many packaging firms use reactive or preventative maintenance approaches, which may not be effective for most failures. An ARC study found that only about 20% of failures are age-related. The 80% of failures that are non-age-related would likely not be addressed by reactive or preventative maintenance programs.

A better way to address these potential failures is to monitor the condition of critical machines and components. Condition monitoring can provide early detection of machine deterioration or impending failure and the data can be used for predictive maintenance. Many “smart sensors” can now measure vibration, temperature, humidity, pressure, flow, inclination, and many other attributes which may be helpful in notifying users of emerging problems. And some of these “smart sensors” can also “self-monitor” and help alert users to potential failures in the sensor itself.

What are packaging customers actually doing?

The good news is that the packaging industry is moving forward to find a better way and users understand that Condition Monitoring/Predictive Maintenance gives them the opportunity to prevent unplanned failures, reduce unplanned downtime, and improve OEE, quality and profitability. About 25% of customers have already implemented some sort of Condition Monitoring / Predictive Maintenance, while about 20% are piloting it and 30% plan to implement it. This means that 75% of customers are very interested in Condition Monitoring/Predictive Maintenance, by far the most interest in any technology discussed in the PMMI survey.

Where do you start?

    • Look for the machines which cause you the most frustration. PMMI identified form, fill & seal, filling & dosing, and labeling machines, but there are other machines, including bottling, cartoning, and case/tray handling, that could fail and cause production downtime or damaged product.
    • Consider where, when, and how equipment can fail. Look to your own experience, ask partners with similar machines or perhaps the equipment supplier to help you determine the most common failure points and modes.
    • Analyze which parts of the machine fail. Moving parts are usually the highest potential failure point. On packaging machines, these include motors, gearboxes, fans, pumps, bearings, conveyors, and shafts.
    • Consider what to measure. Vibration is common, and often assessed in combination with temperature and humidity. On some machines, pressure, flow, or amperage/voltage should be measured.
    • Determine the most appropriate maintenance program for each machine. Consider the costs/benefits of reactive, preventative, condition-based monitoring or predictive approaches. In some cases, it may be OK to let a non-critical, low-value asset “run-to-failure,” while in other cases it might be worth investing in Condition Monitoring or Predictive Maintenance to prevent a critical machine’s costly failure.
    • Start small by implementing condition monitoring on one or two machines, and then scaling up once you’ve learned what does and doesn’t work. Using a low-cost sensor, which can be easily integrated with existing controls architectures or added on externally, is also a great way to start.

Condition Monitoring and Predictive Maintenance offer packaging firms a “better way” to address key topics including machine downtime, failures, and OEE. Users can move from a reactive to a proactive maintenance approach by monitoring attributes such as vibration and temperature on critical machines and then analyzing the data. This will allow them to detect and predict potential failures before they become critical, and thereby, reduce unplanned downtime, improve OEE, and save money.

Why Use Ultrasonic Sensors?

by Nick Smith

When choosing what sensor to use in different applications, it is important to first look at how they operate. Capacitive sensors generate an electrical field that can detect various liquids or other materials, such as glass, wood, paper, ceramic, and more at a close. Photoelectric sensors emit a light beam that is either received by a light sensor or bounced back to the emitter to detect an object’s presence or measure the distance to an object. Ultrasonic sensors bounce a sound wave off objects to detect them, which can make them a good solution for a surprising variety of uses.

How ultrasonic sensors operate

Ultrasonic sensors operate by emitting an ultra-high frequency sound wave that ranges from 300 MHz to 3 GHz, which is well above the 15-17 kHz range that humans can hear that bounces off the target object. The sensor measures the amount of time that sound wave takes to return to calculate the distance to the object. Ultrasonic sensors send these sound waves in a wider beam than a photoelectric uses, so they can more easily detect objects in a dusty or dirty environment. And with a greater sensing distance than capacitive sensors, they can be installed at a safe distance and still function effectively

Common applications for ultrasonic sensors

These capabilities together make ultrasonic sensors a great choice for tasks like detecting fill level, stack height and object presence. Sound waves are unaffected by the color, transparency, or consistency of an object or liquid, which makes it an obvious contender in the packaging, food, and beverage industry and many other industries with similar manufacturing processes.

So to monitor glass bottles as they travel on a conveyor, an ultrasonic sensor could be a good choice. These sensors will consistently work well detecting clear or reflective materials such as water, paint, glass, etc., which can cause difficulties for photoelectric sensors. Another benefit of these sensors is the ability to mount them further away from their targets. For example, there are ultrasonics that can be mounted between 20 to 8000 mm away from the object. After tuning your setup, you can detect very small objects as easily as larger, more visible items.

Another common application for ultrasonic sensors is monitoring boxes. Properly implemented ultrasonic sensors can detect different sizes of boxes as they travel on a conveyor belt by constantly emitting and receiving sound waves. This means that each box or object will be measured by the sound wave. Different photoelectric and capacitive sensors may fail to detect the full presence of an object and may only be able to detect a specific point on an object.

When it comes to all types of different fill-level applications, there are many ways a sensor can monitor various liquids and solids. The width of an ultrasonic beam can be increased to detect a wider area of solid material in a hopper or decreased to give a precise measurement on liquid levels. This ability to detect a smaller or larger surface area gives the user more utility when deciding how to meet the requirements of an application. Although capacitive sensors can detect fill levels very precisely as well, factors like beam width and sensing distance might make ultrasonic a better choice.

With so many different sensor technologies available and factors like target material and sensing distance being such important factors, choosing the best sensor for an application can be demanding. A trusted expert who is familiar with these different technologies and the factors related to your applications and materials can help you confidently move toward the smart factory of the future.

The 5 Most Common Types of Fixed Industrial Robots

The International Federation of Robotics (IFR) defines five types of fixed industrial robots: Cartesian/Gantry, SCARA, Articulated, Parallel/Delta and Cylindrical (mobile robots are not included in the “fixed” robot category). These types are generally classified by their mechanical structure, which dictates the ways they can move.

Based on the current market situation and trends, we have modified this list by removing Cylindrical robots and adding Power & Force Limited Collaborative robots. Cylindrical robots have a small, declining share of the market and some industry analysts predict that they will be completely replaced by SCARA robots, which can cover similar applications at higher speed and performance. On the other hand, use of collaborative robots has grown rapidly since their first commercial sale by Universal Robots in 2008. This is why collaborative robots are on our list and cylindrical/spherical robots are not.

Therefore, our list of the top five industrial robot types includes:

    • Articulated
    • Cartesian/Gantry
    • Parallel/Delta
    • SCARA
    • Power & Force Limited Collaborative robots

These five common types of robots have emerged to address different applications, though there is now some overlap in the applications they serve. And range of industries where they are used is now very wide. The IFR’s 2021 report ranks electronics/electrical, automotive, metal & machinery, plastic and chemical products and food as the industries most commonly using fixed industrial robots. And the top applications identified in the report are material/parts handling and machine loading/unloading, welding, assembling, cleanrooms, dispensing/painting and processing/machining.

Articulated robots

Articulated robots most closely resemble a human arm and have multiple rotary joints–the most common versions have six axes. These can be large, powerful robots, capable of moving heavy loads precisely at moderate speeds. Smaller versions are available for precise movement of lighter loads. These robots have the largest market share (≈60%) and are growing between 5–10% per year.

Articulated robots are used across many industries and applications. Automotive has the biggest user base, but they are also used in other industries such as packaging, metalworking, plastics and electronics. Applications include material & parts handling (including machine loading & unloading, picking & placing and palletizing), assembling (ranging from small to large parts), welding, painting, and processing (machining, grinding, polishing).

SCARA robots

A SCARA robot is a “Selective Compliance Assembly Robot Arm,” also known as a “Selective Compliance Articulated Robot Arm.” They are compliant in the X-Y direction but rigid in the Z direction. These robots are fairly common, with around 15% market share and a 5-10% per year growth rate.

SCARA robots are most often applied in the Life Sciences, Semiconductor and Electronics industries. They are used in applications requiring high speed and high accuracy such as assembling, handling or picking & placing of lightweight parts, but also in 3D printing and dispensing.

Cartesian/Gantry robots

Cartesian robots, also known as gantry or linear robots, move along multiple linear axes. Since these axes are very rigid, they can precisely move heavy payloads, though this also means they require a lot of space. They have about 15% market share and a 5-10% per year growth rate.

Cartesian robots are often used in handling, loading/unloading, sorting & storing and picking & placing applications, but also in welding, assembling and machining. Industries using these robots include automotive, packaging, food & beverage, aerospace, heavy engineering and semiconductor.

Delta/Parallel robots

Delta robots (also known as parallel robots) are lightweight, high-speed robots, usually for fast handling of small and lightweight products or parts. They have a unique configuration with three or four lightweight arms arranged in parallelograms. These robots have 5% market share and a 3–5% growth rate.

They are often used in food or small part handling and/or packaging. Typical applications are assembling, picking & placing and packaging. Industries include food & beverage, cosmetics, packaging, electronics/ semiconductor, consumer goods, pharmaceutical and medical.

Power & Force Limiting Collaborative robots

We add the term “Power & Force Limiting” to our Collaborative robot category because the standards actually define four collaborative robot application modes, and we want to focus on this, the most well-known mode. Click here to read a blog on the different collaborative modes. Power & Force Limiting robots include models from Universal Robots, the FANUC CR green robots and the YuMi from ABB. Collaborative robots have become popular due to their ease of use, flexibility and “built-in” safety and ability to be used in close proximity to humans. They are most often an articulated robot with special features to limit power and force exerted by the axes to allow close, safe operation near humans or other machines. Larger, faster and stronger robots can also be used in collaborative applications with the addition of safety sensors and special programming.

Power & Force Limiting Collaborative robots have about 5% market share and sales are growing rapidly at 20%+ per year. They are a big success with small and mid-size enterprises, but also with more traditional robot users in a very broad range of industries including automotive and electronics. Typical applications include machine loading/unloading, assembling, handling, dispensing, picking & placing, palletizing, and welding.


The robot market is one of the most rapidly growing segments of the industrial automation industry. The need for more automation and robots is driven by factors such as supply chain issues, changing workforce, cost pressures, digitalization and mass customization (highly flexible manufacturing). A broad range of robot types, capabilities and price points have emerged to address these factors and satisfy the needs of applications and industries ranging from automotive to food & beverage to life sciences.

Note: Market share and growth rate estimates in this blog are based on public data published by the International Federation of Robotics, Loup Ventures, NIST and Interact Analysis.

Protecting photoelectric and capacitive sensors

Supply chain and labor shortages are putting extra pressure on automation solutions to keep manufacturing lines running. Even though sensors are designed to work in harsh environments, one good knock can put a sensor out of alignment or even out of condition. Keep reading for tips on ways to protect photoelectric and capacitive sensors.

Mounting solutions for photoelectric sensors

Photoelectric sensors are sensitive to environmental factors that can cloud their view, like dust, debris, and splashing liquids, or damage them with physical impact. One of the best things to do from the beginning is to protect them by mounting them in locations that keep them out of harm’s way. Adjustable mounting solutions make it easier to set up sensors a little further away from the action. Mounts that can be adjusted on three axes like ball joints or rod-and-mount combinations should lock firmly into position so that vibration or weight will not cause sensors to move out of alignment. And mounting materials like stainless steel or plastic can be chosen to meet factors like temperature, accessibility, susceptibility to impact, and contact with other materials.

When using retroreflective sensors, reflectors and reflective foils need similar attention. Consider whether the application involves heat or chemicals that might contact reflectors. Reflectors come in versions, especially for use with red, white, infrared, and laser lights, or especially for polarized or non-polarized light. And there are mounting solutions for reflectors as well.

Considering the material and design of capacitive sensors

Capacitive sensors must also be protected based on their working environment, the material they detect, and where they are installed. Particularly, is the sensor in contact with the material it is sensing or not?

If there is contact, pay special attention to the sensor’s material and design. Foods, beverages, chemicals, viscous substances, powders, or bulk materials can degrade a sensor constructed of the wrong material. And to switch perspectives, a sensor can affect the quality of the material it contacts, like changing the taste of a food product. If resistance to chemicals is needed, housings made of stainless steel, PTFE, and PEEK are available.

While the sensor’s material is important to its functionality, the physical design of the sensor is also important. A working environment can involve washdown processes or hygienic requirements. If that is the case, the sensor’s design should allow water and cleaning agents to easily run off, while hygienic requirements demand that the sensor not have gaps or crevices where material may accumulate and harbor bacteria. Consider capacitive sensors that hold FDA, Ecolab, and CIP certifications to work safely in these conditions.

Non-contact capacitive sensors can have their own special set of requirements. They can detect material through the walls of a tank, depending on the tank wall’s material type and thickness. Plastic walls and non-metallic packaging present a smaller challenge. Different housing styles – flat cylindrical, discs, and block styles – have different sensing capabilities.

Newer capacitive technology is designed as an adhesive tape to measure the material inside a tank or vessel continuously. Available with stainless steel, plastic, or PTFF housing, it works particularly well when there is little space available to detect through a plastic or glass wall of 8mm or less. When installing the tape, the user can cut it with scissors to adjust the length.

Whatever the setting, environmental factors and installation factors can affect the functionality of photoelectric and capacitive sensors, sometimes bringing them to an untimely end. Details like mounting systems and sensor materials may not be the first requirements you look for, but they are important features that can extend the life of your sensors.


Add Automation to Gain Safety and Control in Manufacturing

Industry automation not only has a positive effect on the improvement of production processes, it also significantly improves employee safety. New technologies can minimize the need for employees to work in dangerous situations by replacing them all together or by working cooperatively alongside them.

Overcoming fears of automation
Many workers fear technological progress due to the generally accepted view that robots will replace people in their workplaces. But their fears are conjecture. According to a study published in 2017 by scientists at the Universities of Oxford and Yale, AI experts predict a 50% chance of AI outperforming humans at all tasks within 45 years. But, instead of replacing all workers, there is a stronger chance AI will eliminate dangerous manual labor and evolve other roles. Following are a few examples.

    • Automation in palletizing systems
      Before automation-based solutions entered factories, laborers had to do most work by hand. A work system based on the strength of the human body, however, does not bring good results. Workers tire quickly, causing a decrease in their productivity. And with time, health problems related to regularly carrying heavy daily loads also begin to appear. Until recently, employees of the palletizing departments struggled with these problems. But today, robots are carrying out the work of moving, stacking, and transporting products on pallets.
    • Automation forging processes
      Also, until recently, forging processes in the metallurgical industry were performed with the help of human workers. There are still factories today in which blacksmiths are responsible for putting the hot metal element under the hammer to form the final shape of the product. Such a device hits with a force of several dozen tons, several times a minute. Being at the hammer is therefore extremely dangerous and may cause permanent damage to the worker’s health. Elevated temperatures in the workplace can also have negative effects on the body.

      most businesses, forging processes are now fully automated. Robots specially prepared for such work feed the elements to the automatic hammer with their grippers. And sensory solutions help make the job safer by detecting the presence of people or undesirable elements within the working machine. The quality control of manufactured products is also extremely important and more easily controlled with an automated system.
    • Automation in welding processes
      Welding processes are another dangerous activity in which automation is starting to play a key role. During welding work, toxic fumes are released from the gas lagging, which the welder regularly inhales. This can result in serious poisoning or chronic respiratory diseases. Welding also produces sparks which can lead to severe burns and worker blindness.

      Again, automation makes the process safer. High-class welding machines exist on the market that can work continuously, under human control. With such solutions, it is necessary to use appropriate protection systems to protect employees against possible contact with machines during work. Automation in this situation eliminates a dangerous role, and creates a new, safer, and, some would say, better work role.

Skillful design of automation systems
While factory automation eliminates some threats to workers, others often arise, creating the need for strict design plans prepared by specialists in this field. It is necessary to prepare the automation system in such a way that it not only ensures safety, it does so without reducing productivity or creating downtime which can cause the employee to bypass security systems. The systems blocking the working space of the machine should not interfere with the worker and the worker should not interfere with the system. Where possible, instead of a mechanical lock, an optical curtain at the feeding point should be used to stop the machine’s operation if a foreign object breaks the curtain’s beam of the light. Mechanical locks blocking access to the working space should be in places where it is not necessary to open the door frequently.

Successful human-machine collaboration
When designing automation systems in production companies, it is also necessary to remember that often a human is working alongside the robot. In palletizing systems, for example, a person is responsible for preparing the place for packing and cleaning the working area. For the work to go smoothly, it may be worth creating two positions next to each other. Mechanisms on the market today allow you to control the work of robots at a given position, assigning them to the workspace. Special security scanners prevent the robots from moving to positions where someone is working.

Fork Sensors, the Best Choice for Range, Reliability, Ease of Installation

Photoelectric sensors are a staple within many industries when it comes to automation thanks to their non-contact detection over longer ranges than many other sensing types. Also available in a variety of housing types and protection classes to meet the specific demands of an application, they offer manufacturers many different variants and models. The range of styles can make selecting the perfect photoelectric sensor for your specific application challenging. This post highlights the benefits of through-beam sensors and why fork sensors specifically, are often the ideal sensor for the job.

Through-beam sensors can detect anything, regardless of color, texture or reflectivity. This makes them highly efficient in any application where material or parts need to be detected during the process. They require an emitter and receiver. The emitter sends a light beam toward the receiver. When this light beam is blocked, the sensor will trigger. A common example of this is the sensor system on a garage door that detects obstructions and keeps the door from closing. (The software can also inverse this, so the sensor triggers when the light beam is not obstructed. Read more about these light-on/dark-on modes).

Traditional Through-Beams vs. Fork Sensors

Through-beam photoelectric sensors are simple technology that are non-contact, reliable and can operate over distances up to 100 meters, making them a go-to for many applications. But they aren’t without fault. Because the emitter and receiver are typically in separate housings, the two parts must line up perfectly to work. This alignment takes extra time during assembly and is prone to problems in the future if the emitter or receiver move,  even slightly. Machine vibrations can cause a misalignment.

Fork sensors, also called C slot or U slot sensors, incorporate both the emitter and the receiver into a single body, providing the benefits of a through-beam sensor without the installation issues.

This allows for reduced installation and maintenance time of the sensor in several ways:

    • Mounting a single sensor instead of two
    • Half as many cables needed for networking
    • No touchy alignment needed when installing the sensor
    • No maintenance needed re-aligning the sensors in the future

Photoelectric fork sensors come with sensing windows widths up to 220 mm and a range of light sources to accommodate many application needs. Check them out the next time you are considering a photoelectric sensor and see if they’re the best choice for your application.

Ensure Food Safety with Machine Vision

Government agencies have put food manufacturers under a microscope to ensure they follow food safety standards and comply with regulations. When it comes to the health and safety of consumers, quality assurance is a top priority, but despite this, according to The World of Health Organization, approximately 600 million people become ill each year after eating contaminated food, and 420,000 die.

Using human manual inspection for quality assurance checks in this industry can be detrimental to the company and its consumers due to human error, fatigue and subjective opinions. Furthermore, foreign particles that should not be found in the product may be microscopic and invisible to the human eye. These defects can lead to illness, recalls, lawsuits, and a long-term negative perception of the brand itself. Packaging, food and beverage manufacturers must realize these potential risks and review the benefits of incorporating machine vision. Although machine vision implementation may sound like a costly investment, it is small price to pay when compared to the potential damage of uncaught issues. Below I explore a few benefits that machine vision offers in the packaging, food and beverage industries.

Consumers expect and rely on safe products from food manufacturers. Machine vision can see through packaging to determine the presence of foreign particles that should not be present, ensuring these products are removed from the production line. Machine vision is also capable of inspecting for cross-contamination, color correctness, ripeness, and even spoilage. For example, bruises on apples can be hard to spot for the untrained eye unless extremely pronounced. SWIR (shortwave infrared) illumination proves effective for the detection of defects and contamination. Subsurface bruising defects become much easier to detect due to the optimization of lighting and these defected products can be scrapped.

Uniformity of Containers
Brand recognition is huge for manufacturers in this industry. Products that have defects such as dents or uneven contents inside the container can greatly affect the public’s perception of the product and/or company. Machine vision can detect even the slightest deformity in the container and ensure they are removed from the line. It can also scan the inside of the container to ensure that the product is uniform for each batch. Vision systems have the ability optimize lighting intensity, uniformity, and geometry to obtain images with good contrast and signal to noise. Having the ability to alter lighting provides a much clearer image of the point of interest. This can allow you to see inside a container to determine if the fill level is correct for the specific product.

Packaging is important because if the products shipped to the store are regularly defected, the store can choose to stop stocking that item, costing the manufacturer valuable business. The seal must last from production to arrival at the store to ensure that the product maintains its safe usability through its marked expiration date. In bottling applications, the conveyors are moving at high speeds so the inspection process must be able to quickly and correctly identify defects. A facility in Marseille, France was looking to inspect Heineken beer bottles as they passed through a bottling machine at a rate of 22 bottles/second (80,000 bottles/hour). Although this is on the faster end of the spectrum, many applications require high-speed quality checks that are impossible for a human operator. A machine vision system can be configured to handle these high-speed applications and taught to detect the specified defect.


It’s crucial for the labels to be printed correctly and placed on the correct product because of the food allergy threats that some consumers experience. Machine vision can also benefit this aspect of the production process as cameras can be taught to recognize the correct label and brand guidelines. Typically, these production lines move at speeds too fast for human inspection. An intuitive, easy to use, machine vision software package allows you to filter the labels, find the object using reference points and validate the text quickly and accurately.

These areas of the assembly process throughout packaging, food and beverage facilities should be considered for machine vision applications. Understanding what problems occur and the cost associated with them is helpful in justifying whether machine vision is right for you.

For more information on machine vision, visit https://www.balluff.com/local/us/products/product-overview/machine-vision-and-optical-identification/.



Reduce Packaging Downtime with Machine Vision

Packaging encompasses many different industries and typically has several stages in its process. Each industry uses packaging to accomplish specific tasks, well beyond just acting as a container for a product. The pharmaceutical industry for example, typically uses its packaging as a means of dispensing as well as containing. The food and beverage industry uses packaging as a means of preventing contamination and creating differentiation from similar products. Consumer goods typically require unique product containment methods and have a need for “eye-catching” differentiation.

The packaging process typically has several stages. For example, you have primary packaging where the product is first placed in a package, whether that is form-fill-seal bagging or bottle fill and capping. Then secondary packaging that the consumer may see on the shelf, like cereal boxes or display containers, and finally tertiary packaging or transport packaging where the primary or secondary packaging is put into shipping form. Each of these stages require verification or inspection to ensure the process is running properly, and products are properly packaged.


Discrete vs. Vision-Based Error Proofing

With the use of machine vision technology, greater flexibility and more reliable operation of the packaging process can be achieved. Typically, in the past and still today, discrete sensors have been used to look for errors and manage product change-over detection. But with these simple discrete sensing solutions come limitations in flexibility, time consuming fixture change-overs and more potential for errors, costing thousands of dollars in lost product and production time. This can translate to more expensive and less competitively priced products on the store selves.

There are two ways implementing machine vision can have a benefit toward improving the scheduled line time. The first is reducing planned downtime by reducing product change over and fixturing change time. The other is to decrease unplanned downtime by catching errors right away and dynamically rejecting them or bringing attention to line issues requiring correction and preventing waste. The greatest benefit vision can have for production line time is in reducing the planned downtime for things like product changeovers. This is a repeatable benefit that can dramatically reduce operating costs and increase the planned runtime. The opportunities for vision to reduce unplanned downtime could include the elimination of line jams due to incorrectly fed packaging materials, misaligned packages or undetected open flaps on cartons. Others include improperly capped bottles causing jams or spills and improper adjustments or low ink causing illegible labeling and barcodes.

Cost and reliability of any technology that improves the packaging process should always be proportional to the benefit it provides. Vision technologies today, like smart cameras, offer the advantages of lower costs and simpler operation, especially compared to the older, more expensive and typically purpose-built vision system counterparts. These new vision technologies can also replace entire sensor arrays, and, in many cases, most of the fixturing at or even below the same costs, while providing significantly greater flexibility. They can greatly reduce or eliminate manual labor costs for inspection and enable automated changeovers. This reduces planned and unplanned downtime, providing longer actual runtime production with less waste during scheduled operation for greater product throughput.

Solve Today’s Packaging Challenges

Using machine vision in any stage of the packaging process can provide the flexibility to dramatically reduce planned downtime with a repeatable decrease in product changeover time, while also providing reliable and flexible error proofing that can significantly reduce unplanned downtime and waste with examples like in-line detection and rejection to eliminate jams and prevent product loss. This technology can also help reduce or eliminate product or shipment rejection by customers at delivery. In today’s competitive market with constant pressure to reduce operating costs, increase quality and minimize waste, look at your process today and see if machine vision can make that difference for your packaging process.

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.


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.


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.


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.


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.


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