Using vision-based sensors throughout a packaging process can dramatically reduce planned downtime, plus substantially shorten production and product changeover time. Vision sensors can also reduce unplanned downtime with reliable error proofing for inlin
Packaging is used across many industries and involves several stages. Each industry uses packaging to accomplish specific tasks that go well beyond simply holding a product. The pharmaceutical industry, for example, typically uses packaging to dispense as well as contain medication. The food and beverage industry uses packaging both to prevent contamination and create differentiation on store shelves. The consumer goods industry generally requires unique containment methods to protect product.
Within the packaging process itself, there are primary, secondary, and tertiary stages. In the primary stage, product is first placed into a package, such as form-fill-seal bagging or bottle filling and capping. Secondary packaging is typically what the consumer sees on the shelf — say, cereal boxes or bottle six packs. Finally, tertiary packaging or transport packaging groups the primary or secondary packaging together for storage and transportation. Each stage typically requires inspection to ensure that the process is running properly and products are correctly packaged. Vision-based sensing technology imparts greater flexibility and more reliable packaging operations. In the past — as well as today — discrete sensors have often been used to detect errors and manage product changeovers. However, these simple sensing solutions can limit flexibility, cause time-consuming fixture changeovers, and increase potential for errors, translating to thousands of dollars in rejected products and lost production time.
Reducing planned and unplanned downtime
Let's examine activities that decrease actual runtime. Each packaging line is scheduled to run a certain amount of time, for example, one shift of 8 hours, or 480 minutes. Total time can be deconstructed into planned downtime and planned runtime. Planned runtime, however, includes unplanned downtime and actual runtime. Reducing both planned and unplanned downtime directly increases actual runtime.
Planned downtime encompasses many activities, such as time needed to change to another product type or package, perform routine maintenance, sanitize the line, and allow operator breaks. For the sake of this article, we only consider procedures that affect changeover or line maintenance, such as routine calibration or verification and fixturing of discrete sensors. For example, a vision sensor can replace discrete sensors, reducing changeover time to only the moments needed to switch to a new software program and adjust the lighting, if necessary. Note: In most vision applications, lighting and fixturing don't usually require adjustment, so the total time for product changeover with a vision sensor is the time required to change the electronic program, typically less than one second.
Unplanned downtime occurs when the line is shut down due to a run-time error in the packaging process. Time usually accrues in minutes, unless a line configuration process was improperly followed. If jamming occurs, several hours may be required to correct the improper setup. Unplanned downtime is usually caused when a process jams or improperly packaged products are detected without dynamic or inline rejection. For every instance of unplanned downtime due to jamming, finished product may have to be discarded, thereby increasing overall operating costs. By using a vision sensor, this type of unplanned downtime can be prevented or detected right away, thus increasing actual runtime and reducing waste costs.
Implementing vision-based sensors improves scheduled line time in two key ways. The first is by reducing planned downtime during product changeovers that require fixturing changes. In fact, this is the area in which vision sensors can have the greatest effect on improving scheduled line time. What's more, this is a repeatable benefit that can dramatically reduce operating costs and increase planned runtime. The second way is to decrease planned downtime by catching errors right away and dynamically rejecting them, or bringing attention to line issues, thereby preventing large amounts of waste. Examples include line jams that occur because of incorrectly fed packaging materials, misaligned packages, or undetected open flaps on cartons. Other examples include improperly capped bottles that cause jams or spills and low ink levels that cause defective labeling. Implementing vision-based sensors in any of these cases improves scheduled line time.
Discrete vs. vision-based error proofing
The cost and reliability of any technology that improves the packaging process should be proportional to the benefit it provides. Unlike older, more expensive vision systems, today's vision-based sensors can replace an entire discrete sensor array, and in many cases the fixturing to boot. Vision sensors do this at the same — or lower — cost of a sensor array, while also providing greater flexibility. These sensors also significantly reduce labor costs for inspection. A vision sensing setup can typically be installed for less than $2,500, including fixturing, lighting, and labor.
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Case study: Beverage bottling
The food and beverage industry is one in which profit margins are low and the cost of mistakes — especially packaging errors — is high. Case in point: One mainstream dairy produces 130 half-gallon specialty drink bottles per minute, seven days a week using three shifts per day, pouring and capping about 150,000 bottles per day. Drink flavors are changed several times each day. When a single bottle of liquid is not properly capped and sealed, it becomes what's called a leaker. The leaker often contaminates a pallet of cased bottles, leading to entire loss of the pallet. The bad bottle is found either at the palletizer or worse yet, at the store, causing the entire pallet to be rejected at a great expense. The leaking bottle can also contaminate the delivery vehicle, requiring the vehicle to be taken out of service for cleaning. A single leaker can cost in excess of $5,000 if the pallet is not detected until it reaches the store, an event that occurred at one facility once a week before vision sensors were installed. According to dairy engineers, detecting just one leaker before it reached the palletizer paid for the entire vision sensor error-proofing station.
The aim of the error proofing station is to catch missing or improperly capped bottles before they reach the secondary case packaging or palletizing stage, when detection is virtually impossible. If a capping problem is detected on a bottle, the filling line is stopped so the bottle can be removed before secondary packaging. Once the vision-based sensing station began operation, it was almost immediately discovered that there were two slightly different sized bottles because of the multiple PET bottle suppliers. Without vision inspection, operators would have had to manually sort and identify the bottles, and the capper would have required difficult adjustments before production could begin. These adjustments increased planned downtime by more than 20 minutes, not including manual bottle sorting. Implementation of the vision sensor error-proofing station eliminated the few but costly leakers from reaching the casing machine or proceeding to the palletizer. This allowed the plant to run all the bottles without sorting or making any capper-machine adjustments, thereby significantly reducing planned downtime and lost product costs.
Sensors in automation
Inductive proximity sensors
A wide range of sensing technologies finds use in assembly and automation applications. That said, inductive proximity sensors are the foot soldiers of the sensor world. Relatively simple discrete (on/off) devices such as inductive proximity sensors determine parts presence, detect and confirm features, confirm hole presence or absence, and validate nesting. Inductive prox sensors are reliable and accurate. They can withstand wide temperature ranges and are one of the easiest sensors to deploy. If the target to be detected is metal, an inductive prox is hard to beat.
Discrete photoelectric sensors in thru-beam (energized pairs), retro-reflective (used with a dependable target, a reflector), and diffuse reflective (self-contained emitter-receiver pair) modes are all used to detect parts presence when metallic or non-metallic components must be detected. The most commonly used photoelectric sensors produce red, infrared, or laser emissions, depending on the application. Laser sensors provide a controlled and precise light beam. Red emission photoelectric sensors provide easily visible setup properties, whereas infrared emissions possess the greatest amount of excess gain — the ability to sense through industrial hostilities, such as smoke, oil, and steam.
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When a simple yes or no response is insufficient for successful assembly, an analog sensor can provide the additional data essential for error proofing in flexible manufacturing environments. Analog sensors provide part position information in the form of an analog signal that interfaces directly into the control system, allowing both actual measurements and infinite variability in yes or no decisions. In addition, some analog sensors offer one or more discrete outputs plus continuous voltage output for precise gauging, measuring, and positioning of parts in assembly and automation processes. Several models of both inductive and photoelectric technologies offer discrete set points that can be programmed anywhere in the sensor's range. This feature can help to establish “go, no go” parameters as well as position feedback in a single sensing device.
Laser-based sensors offer a high level of precision, ease of use, and cost effectiveness in error-proofing applications. They detect product details by either using diffuse, diffuse with background suppression methods, or breaking a beam using thru-beam or retro-reflective techniques. Beam-break versions are reliable, accurate, and capable of long-range position detection without regard to target color. Thru-beam sensors can error proof product details based on either missing parts or part shape differentiation. Easily seen laser spots assist operators by highlighting the specific product detail and are especially useful for informing the operator about error locations after detection. The long-range capability of lasers allows them to be positioned around operators or moving tooling, enabling more points of detection in smaller areas. In addition, laser-sensor precision often far exceeds what machine fixturing can provide. The better the machine tolerance, the better the laser performs in that operation.
When manufacturers need to validate color-matched components, color sensors are used to verify that the right color of component was put on the right device in production. Color sensors come in a wide range of styles. On the low-cost end, there are easy-to-program sensors available that detect and hold three colors or shades of color in memory. These solve 80% to 90% of the day-to-day color sensing requirements found in manufacturing processes. True-color sensors are suitable for highly specialized applications such as color matching and in-line vehicle sequence (ILVS) verification in the automotive industry. In this case, color sensors are tuned to sense the difficult darker shades typically found in automotive interiors. These sensors can learn three individual colors without the need for external lights or controllers. Setting the sensor is accomplished by teaching the intended color and then assigning a tolerance level to that color setting. Narrow tolerance levels allow detection of small shade differences, while widening the tolerance allows for acceptable shade variations due to color lot inconsistencies. Two decision modes are also available to handle shiny or matte surfaces.
UV tracing is the most reliable method of error proofing complex assembly tasks — even better than a vision system. The UV tracing process involves two steps: The first is to apply the luminescent tracer material to the parts in question; the second is to use a UV sensor to detect the tracer material. When the sensor sees a certain level of luminosity from the tracer, the part has been positively identified. The advantages of UV sensors include reliability, accommodation of loosely fixtured parts, use of fiber optic cabling for tight locations, simple teaching controls, and compatibility with any control system. The benefit is that target materials are invisible to the human eye, chemically inert, and have no negative impact on product aesthetics. Many lubricants inherently glow or react to UV sensors. In fact, many engine test stand and power train manufacturers use UV sensors to detect leaks and overflow to determine fill levels in lubrication tests prior to installation.