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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