Understanding packaging dynamics and identifying hazards in physical distribution are essential for reducing damage risk to packages and products, thus improving their integrity. Evaluating the mechanical properties of package structures in different designs and environments through experimental tests is time-consuming and costly. While mechanics-based simulation models like the Finite Element Method have been used to address these challenges, they often rely on simplified assumptions due to the complex mechanical responses of composite structures.Recent advancements in artificial intelligence, particularly machine learning, offer innovative solutions for efficient problem-solving, optimization, and predictive insights. In package dynamics and distribution, machine learning models provide significant cost and time advantages over traditional experimental testing by predicting performance without extensive physical trials. Machine learning learns from data and generates predictive models using advanced algorithms. This dissertation explores the use of machine learning techniques to predict damage to package structures and products after transportation vibrations, offering insights and predictive tools for future package designs. The machine learning solution is time and cost-efficient, eliminating the need for additional experimental tests once predictions are made. The application is demonstrated through three studies: a) analyzing and predicting the buckling behavior of corrugated paperboards with cutouts, b) predicting bruising damage to packaged apple fruits from vibrational forces during transportation, and c) forecasting the loss of compression strength in corrugated paperboard boxes post-transportation vibrations. Chapter 1 introduces the use of machine learning in package dynamics, outlines the algorithms used, and presents the research goals and case studies. While the studies are related to packaging dynamics, they differ in parameters, so the literature review is presented separately in each chapter. Chapter 2 investigates the relationship between cutout characteristics and buckling loads in ventilated corrugated paperboard boxes using experimental tests and Finite Element Method simulations. The study found that larger cutouts reduce buckling resistance, while positioning holes closer to horizontal edges maintains higher strength. The machine learning model effectively predicts buckling strength, achieving 91.45 R2 accuracy on experimental data for plates with single cutouts and 94.68 R2 accuracy on simulation data for plates with multiple circular cutouts. Chapter 3 develops machine learning solutions to predict bruising damage to apples during transportation, identifying vibration intensity as the primary factor affecting damage. Chapter 4 examines the loss of compression strength in corrugated paperboard boxes due to transportation vibrations, using machine learning models that achieve an R2 score of 0.93 to predict this degradation accurately. The analysis reveals that vibration characteristics have a more significant impact on compression strength than package dimensions. Chapter 5 summarizes the findings and suggests directions for future research to enhance the durability and performance of packaging systems. The novelty of this dissertation lies in demonstrating the application of machine learning in predicting dynamic damage in packaging dynamics and distribution through three case studies. While machine learning has been used for optimizing packaging geometry, its application in predicting mechanical failures is relatively limited. This research enhances the understanding of damage to package structures and products after transportation and provides a machine learning-based tool for predicting damage, which can be adapted for similar tests. In summary, the dissertation highlights the significant potential of machine learning in addressing challenges within packaging dynamics and distribution.
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