Finite element modelling of papaya fruit under compressive loads

This thesis contributes to the improvement of postharvest practices and the reduction of damage during the handling of papaya fruits. Grading and sorting, critical postharvest tasks, can be performed more effectively using predictive models utilizing computer vision technology. Additionally, papaya fruits are highly susceptible to mechanical damage due to the loads imposed on them during harvesting, transportation, and packaging. Understanding the fruit's response to static and dynamic loads is vital to prevent postharvest losses due to mechanical damage. This study focuses on modelling the mass of papaya and its mechanical behaviour under compression loading to improve postharvest practices and minimise damage during handling. Seven regression models were developed for mass modelling based on the fruit’s geometric properties. The accuracy of the models for predicting papaya fruits' mass was evaluated using the coefficient of determination (R2) and Root Mean Square Error (RMSE) metrics based on the testing dataset. Results indicate that the power model (R2=0.94, RMSE=87.72 g) and quadratic model (R2=0.94, RMSE=88.21 g) had better performance compared to the linear model (R2=0.91, RMSE=108.59 g) and S-curve model (R2=0.82, RMSE=150.63 g) when mean diameter was employed as the sole predictor for mass estimation. While using the ordinary least square regression (OLS), linear Lasso (Least Absolute Shrinkage and Selection Operator) and SVR (Support Vector Regression) algorithm, the OLS outperforms other models with R2 and RMSE values of 0.95 and 81.72 g, respectively, using multiple geometric variables as predictors. Conventionally, human operators sort and grade papaya fruits manually. However, this method is subjective and can be quite laborious. In contrast, the mass models developed in this study offer a more objective solution as they can be integrated with computer vision technologies to automate sorting and grading papayas based on their mass. This will enhance grading accuracy and likely reduce workload. Next, finite element modelling (FEM) was used to characterize the mechanical behaviour of papaya under compression. The steps include geometry modelling, material properties modelling, mesh creation, boundary conditions, loading, and analysis solutions. Three material models were used to simulate its mechanical response under compressive loads. The stress response by these models was compared with experimental data. The model with the smallest deviation from the experimental results was considered as the best model to represent papaya’s mechanical behavior. Therefore, it was selected to was selected for simulating the mechanical response of papaya fruit under compression. Next, the stress responses under transverse and longitudinal compressive loads were compared. Lastly, a hypothesis was tested to evaluate the difference in the stress response of the geometry model created using laser scanning and sketching methods. The comparison of material models’ mechanical behaviour with its equivalent experimental data indicates that the viscoelastic model best captures the deformation behaviour of papaya fruit under compressive load. Therefore, the model was used to investigate the effect of loading direction on the fruit. The results show that loading in the transverse direction increases the susceptibility of the fruit to mechanical damage. Also, there was no significant difference in the stress response between the 3D models produced using scanning and sketching methods. Overall, the comprehensive understanding gained from this research can enhance the handling, transportation, and design of systems involving papaya fruit. The findings contribute to the field of postharvest engineering and can be valuable for researchers, agriculturalists, and industries involved in fruit processing and distribution. Further studies could build upon this research by exploring additional factors and optimising material models to improve the accuracy of fruit mechanical simulations.

Text FK 2024 2 - Declaration Form.pdf Restricted to Repository staff only Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (518kB) Text FK 2024 2 - Full Text.pdf Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (3MB) Text FK 2024 2.pdf Restricted to Repository staff only Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (3MB) Official URL or Download Paper: http://ethesis.upm.edu.my/id/eprint/18966

Actions (login required)

View Item