Abstract
In the dynamic world of Polyethylene Terephthalate (PET) bottle production, ensuring the reliability of blow molding machines is paramount, particularly the locking mechanisms that endure significant cyclic loads during operation. This study investigates the fatigue degradation and crack propagation within the locking system of a two-cavity, 2-liter PET bottle blow molder. By modeling an elliptical crack in the most stressed component, we explore the complex stress distribution and the impact of repeated opening and closing cycles on the system’s integrity. Utilizing advanced finite element analysis (FEA), our research provides a comprehensive understanding of the stress intensity factors and safety margins under varying loading conditions. The results not only enhance predictive maintenance strategies but also offer valuable insights into extending the operational lifespan of these machines, thereby improving safety and reliability in industrial applications. This study opens new avenues in the field of component lifespan prediction. By combining artificial intelligence, machine learning, and innovative materials, it aims to improve the reliability and lifespan of PET blow molders.
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