Dr. Zohir Benrabah

Research Officer, Forming and Blow Molding Scientist

Speaker Biography:

Zohir Benrabah is a Forming and Blow Molding Simulation Scientist at NRC’s Automotive and Surface Transportation Research Center in Boucherville, Quebec, Canada, where he is involved in the development of the finite element software, BlowView, dedicated to simulating and optimizing blow molding processes such as: Conventional and Twin Sheet Extrusion, Stretch, Thermoforming, and most recently Suction Blow Molding. His principal interest is in process numerical modelling and problem-solving type work, typically combining numerical and experimental components. Zohir hold a Bachelor and Master degree in Mechanical Engineering, and he completed his Ph.D. in 2002 in computational structure at Laval University in Quebec City, Canada. He has written over 40 papers on the simulation of thermoplastic forming processes.

Presentation Description:|

Accurate prediction of pressure evolution during stretch blow molding is essential for reliable simulation of preform deformation, material distribution, and final container geometry. Conventional pressure-volume (pV) relationships commonly used in commercial simulation tools are often based on simplified assumptions that may not adequately capture the complex pressure variations occurring during industrial blowing cycles, particularly when multiple blowing stages are involved.

This presentation introduces a high-order pressure-volume (pV) formulation developed at the National Research Council Canada (NRC) for the simulation of stretch blow molding processes. The proposed approach provides a more accurate representation of the thermodynamic behavior of compressed air during cavity expansion while preserving computational efficiency required for industrial applications.

The model was implemented within the BlowView simulation platform and validated using experimental data obtained from industrial stretch blow molding trials conducted under various processing conditions. Predicted pressure histories were compared with measured cavity pressure data, and the impact of the improved pV formulation on thickness distribution prediction was evaluated.

Results demonstrate that the proposed model significantly improves the agreement between predicted and measured pressure evolution throughout the blowing cycle. The enhanced pressure representation also leads to improved prediction of material distribution along the bottle profile, particularly in regions experiencing rapid deformation and strong biaxial stretching.

The presentation will discuss the theoretical basis of the formulation, implementation aspects, and validation results obtained on representative industrial bottle geometries. The proposed high-order pV model provides a practical and robust framework for improving the predictive capabilities of stretch blow molding simulations and supports more reliable process optimization and lightweight container design.