Reducing Planned/Unplanned Downtime with Vision Sensors; Part 3

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In parts one and two of this blog series, I described the typical packaging process, how actual runtime is defined, how vision is used to improve runtime, and how vision compares to the use of discrete sensors. In this last installment of this series, I will show some specific examples of how vision sensors have been used in packaging and show two case studies exemplifying the benefits customers achieved with the use of vision in their processes.

In the following examples, a vision-based sensor was used to solve a packaging problem or to replace an existing but ineffective sensing installation.

Detects improperly seated cabled carton cap

Detects wrong medication bottle cap type

Verifies cap presence and proper sealing

Verifies improperly closed freshness seal

Verifies proper PET below molding

Detects proper box flap closure theafhadfeea

Verifies proper label orientation

Verifies proper box orientation

Verifies box contents

Detects broken or missing bottles in case

 

 

 

 

 

 

 

 Case #1 – Medical Vial Manufacturer
A medical vial manufacturer produces caps with foil and paper liners inserted into several different caps of various sizes and colors to seal glass medical sample vials. This manufacturer can produce 100 vials and caps per minute with 2 shifts, 5 days a week. There are approximately 100 to 200 vials per case and they have been using manual inspection to error proof cap production. They found that in order to maintain these higher volumes, they would need to have automated inspection with in-line rejection to manage the planned downtime for setup of all the different cap types and to minimize unplanned downtime if the cap liner was not properly being cut or inserted into the cap. One bad cap could cause the rejection of an entire case of caps and vials costing over $200 in product, shipping, and manual re-inspection.

Aluminum cap with cut paper insert in top cap and missing insert in the bottom cap. A pattern matching tool was used for inspection.

The images above and below, show one good and one defective inspection being done by a Balluff Sharpshooter vision sensor. 100% error proofing of each cap is done before proceeding to bagging and then casing.

(Failed cap)

Because of the variations of cap materials and colors, using discrete sensors would be difficult to use and adjust every time a new cap type was run. Using a vision sensor minimized the changeover time to milliseconds and provided 100% inspection. These changes enabled them to completely eliminate manual inspection and product case rejections by customers. The pictures below show the in-line error proofing and rejection station.

Error-proofing station with Balluff Sharpshooter vision sensor above a mounted ring light

Close up of the cap inspection/rejection lane during vision inspection

Case #2 – Mainstream Dairy Bottler
A mainstream dairy produces 130 half gallon specialty drink bottles per minute, 7 days a week, 3 shifts per day up to 150,000 bottles per day. They change drink flavors as often as several times a day. When a single bottle of liquid is not properly capped and sealed, it becomes a “leaker”. The “leaker” contaminates a pallet of cased bottles that can cause the entire loss of the pallet. The “leaker” is found either at the palletizer or even worse, at the store, causing the entire pallet to be rejected at a greater expense. The leaking bottle can also contaminate the delivery vehicle requiring the vehicle to be taken out of service for cleaning. The estimated loss for a single “leaker” can be in excess of $5000 if the pallet was not detected until it reached the store. According to the dairy, detecting just one “leaker” before it reached the palletizer easily paid for the entire vision sensor error proofing station. They also noted they were experiencing a “leaker” as often as once a week before using a vision sensor to check for improperly capped bottles.

The images below illustrate the installation of the Sharpshooter vision sensor and the inspections being preformed just downstream of the rotary capping machine.

Cap inspection program

Vision error proofing station

The intention of the error proofing station was to catch missing or improperly capped bottles before they reached the secondary case packaging or palletizing stage, when detection would be 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 detected that there were two slightly different sized bottles because of the multiple PET bottle suppliers. Without vision inspection, they 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 over 20 minutes, not including manual bottle sorting.

The 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 them to run all the bottles without sorting or making any capper machine adjustments, thereby significantly reducing planned downtime and lost product costs.

Answering Today’s Packaging Challenges
As I hope this blog series as shown, using vision sensors within any stage of the packaging process can provide the flexibility to dramatically reduce planned downtime with a repeatable decrease in product changeover time. Vision sensors can also provide reliable and flexible error proofing that can significantly reduce unplanned downtime by providing inline detection and rejection to eliminate jams and prevent product losses, especially by customer rejections. In today’s competitive markets with constant pressure to reduce operating cost, increase quality, and minimize waste, vision sensors can make those differences for your packaging process.

Click here to learn more about vision-based sensors.
Click here for a full copy of a whitepaper summing up the complete subject of this blog series on “Reducing Downtime with Vision”.

About Mark Sippel

Mark Sippel is the North American Product Marketing Manager for Object ID and Photoelectric sensors with Balluff NA.
This entry was posted in Machine Vision, Photoelectric Sensors and tagged , , , , , , , . Bookmark the permalink.

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