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8 Ways to Optimize Closure Design Using Predictive Modeling

Success in today’s business environment is frequently based on getting products to market quickly, and while minimizing development costs. To help achieve this objective, one of the tools that should be deployed is predictive modeling. Using intelligent software to predict how your package is likely to function before you’ve even created anything three-dimensional is an excellent way of cutting down iterations and improving your speed-to-market.

While the focus tends to be on bottle design, it is also very important not to overlook how predictive modeling can help you select the closure attributes that will meet your performance requirements. Here are eight tips on how predictive modeling can help you “build” an optimal closure.

1. Plug seal interaction. How well the closure seals to the bottle will determine its integrity. Poor seals and closure back-off create leakers which, in turn, create big problems for the distribution channel, retailers and consumers. Complicating the issue is that closure/bottle combinations will react differently depending on the filling process and/or contents — such as hot-fill or products under vacuum. Predictive modeling allows you to plug in variables such as geometry, material type, fill temps and more. The objective is to determine stress distribution, which will determine if the plug seal is displaced, thereby creating ovalization or gaps.

2. Snap lid opening/closing force. We’ve all experienced snap-lid closures (typically found on spice containers), which are too hard to open and close. Conversely, others open too easily which may cause them to pop open when we don’t want them to. Predictive modeling is an important tool to find that performance optimization “sweet spot.” By iterating the snap feature parameters and assessing force through simulation, optimization can occur before cutting steel, saving time and money on tooling modifications.

3. Venting of pressure during uncapping. The objective with certain liquids such as carbonated soft drinks is to have the internal pressure fully vented by the time the closure is fully unthreaded. Excess pressure can be a safety concern for the end consumer. Simulations can used to predict the volume of air that will escape during uncapping. This can help with determining the optimal closure design for the application, including venting of threads and number of thread turns and starts.

4. Closure torque (application/removal). The goal is to have a closure that secures the contents, but at the same time is not difficult for the consumer to remove. With an increasing number of seniors in the US population, this is an area that is increasingly important. Predictive modeling can be used to evaluate torque due to friction, interference fit and internal pressure. This will help in designing the closure parameters that will give you optimal performance. 

5. Child-resistant closures. These closure types typically require a force — either downward or squeeze — to open a closure. Simulation can be used to determine how much force is required to gain access to the contents as well as evaluating whether the internal components interact with each other properly. This approach will give you the information needed to optimize closure and bottle thickness as well as material to meet the desired performance attributes. 

6. Drop impact. When filled bottles are dropped, there is the possibility that the finish deforms enough so that the closure pops off and the contents spill —a worse-case scenario. By evaluating the bottle material, wall thickness, size, closure, etc., predictive modeling can be used to virtually drop the container from various angles and heights, inducing a virtual water-hammer effect when the bottle hits the ground. Evaluation of bottle and closure deformation will help determine if a redesign is necessary.

7. Injection Molding. For complex closures, injection molding may become problematic. Using predictive modeling, the injection process can be simulated and assessed for common issues such as short shots, sink and molded-in stresses. Modifying the design or materials up front can save a lot of headaches down the road.

8. Lightweighting. Of course, we can’t have a conversation about performance without including lightweighting. Simulation is an ideal way to determine if weight can be removed from a closure while still achieving desired performance parameters. Also, assessing the moldability as the part thins out can be critical in ensuring production success.

Paying attention to these key attributes and using predictive modeling to steer you in the right direction will help you get to the finish line faster, using fewer resources.

Aaron Bollinger is the manager of the simulation and application development department at PTI. He has more than a decade of experience using predictive modeling simulations for real-world packaging applications.

PTI is recognized worldwide as the preferred source for preform and package design, package development, rapid prototyping, pre-production prototyping, and material evaluation engineering for the plastic packaging industry. For more information:

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