8 ways to optimize closure design using predictive modeling


Success in today’s business environment often depends on getting products to market quickly while minimizing development costs. To help achieve this goal, one of the tools to deploy is predictive modeling. Using smart software to predict how your package is likely to perform before you’ve even created anything in three dimensions is a great way to reduce iterations and improve your speed to market.

While the focus tends to be on bottle design, it’s 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 closing.

1. Interaction of the plug seal. The quality of the seal of the closure to the bottle will determine its integrity. Poor seals and sealing plugs create leaks which in turn create big problems for the distribution channel, retailers and consumers. To complicate the problem, the closure / bottle combinations will react differently depending on the filling process and / or the contents, such as hot filling or vacuum products. Predictive modeling allows you to plug in variables like geometry, material type, fill times, etc. The objective is to determine the stress distribution, which will determine whether the plug seal is displaced, thus creating an ovality or gaps.

2. Opening / closing force of the pressure cover. We’ve all known snap lid closures (typically found on spice containers), which are too difficult to open and close. Conversely, others open too easily, which can cause them to open when we don’t want to. Predictive modeling is an important tool in finding that “sweet spot” of performance optimization. By iterating the parameters of the snap function and evaluating the force by simulation, optimization can occur before cutting the steel, saving time and money on modifications to the steel. tools.

3. Pressure release during unblocking. The goal with some liquids such as carbonated soft drinks is to have the internal pressure completely released by the time the closure is fully unscrewed. Excessive pressure can be a safety concern for the end consumer. Simulations can be used to predict the volume of air that will escape during unblocking. This can help determine the optimum closure design for the application, including thread ventilation and the number of thread turns and starts.

4. Closing torque (application / removal). The goal is to have a closure that secures the content, 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 assess torque due to friction, interference fit, and internal pressure. This will help to design the closing parameters that will give you optimum performance.

5. Child resistant closures. These types of closures generally require force – downward or compression – to open a closure. Simulation can be used to determine the force required to access content as well as to assess whether internal components are interacting properly with each other. This approach will give you the information you need to optimize bottle closure and thickness as well as the material to meet desired performance attributes.

6. Impact drop. When filled bottles fall, it is possible for the finish to warp enough for the closure to come off and the contents to spill – a worst-case scenario. By evaluating bottle material, wall thickness, size, closure, etc., predictive modeling can be used to virtually drop the container from different angles and heights, inducing a virtual water hammer effect when the bottle hits the ground. Evaluating bottle deformation and closure will help determine if an overhaul is needed.

7. Injection molding. For complex closures, injection molding can become problematic. Using predictive modeling, the injection process can be simulated and evaluated for common problems such as short shots, drop and mold stresses. Changing the design or materials ahead of time can save you a lot of headaches down the road.

8. Relief. Of course, we can’t have a performance conversation without including the weight reduction. Simulation is an ideal way to determine if weight can be removed from a closure while still achieving desired performance parameters. In addition, assessing moldability as the part thins can be essential to ensure successful production.

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

Aaron Bollinger is the Head of Simulation and Application Development at PTI. He has over a decade of experience using predictive modeling simulations for real world packaging applications.

PTI is recognized worldwide as the preferred source for preform and packaging design, packaging development, rapid prototyping, pre-production prototyping and materials evaluation engineering for industry. of the plastic packaging. For more information: www.pti-usa.com.

Leave A Reply

Your email address will not be published.