# Application of Artificial Neural Network (ANN) in Predicting Box Compression Strength (BCS)
## 4 Discussion  
The findings of this study demonstrate the application of ANN models to the real world by predicting BCS values for industrial application. Through training a dataset covering the large majority of box dimensions commonly used in the industry, a generalized ANN model was built with an accuracy level of around 10%. The training results using physically tested data in the lab also demonstrate the accuracy level of the ANN model. The model demonstrates a reliable performance, indicating its suitability for real-world implementation. Overall, this study advances the field in several important and novel ways, beyond the broader dataset coverage:  
1. Industry-Level Generalization: Unlike prior studies that often focus on limited box types, flute profiles, or experimental conditions, our model is trained on a real-world dataset covering approximately 90% of the box dimensions commonly used in industry. This gives the model practical applicability and scalability not demonstrated in earlier work. 2. Systematic Architecture Optimization: Our study introduces a structured, comparative evaluation of five different optimization methods--including AIC, BIC, Hebb's Rule, Optimal Brain Damage (OBD), and Bayesian Optimization--to determine the ideal hidden neuron configuration. This systematic comparison using exhaustive validation distinguishes our methodological approach from previous works, which often rely on heuristic or single-method tuning. Figure 15: Bland–Altman plot of predicted BCS versus tested BCS for all 10 dimensions of the box samples. However, the current ANN model has certain limitations. The presence of a residual error can largely be attributed to boundary data points, which introduce variability and potential inconsistencies in predictions. Although the model is trained on a generalized dataset covering 90% of commonly used box dimensions in the industry, the dataset volume remains insufficient--hindering the model's ability to achieve optimal prediction accuracy. The use of validation data limited to single-wall B-flute boxes may reduce the overall credibility of the validation. Extending the dataset to include other flute types could improve its generalizability and reliability. Furthermore, due to the high cost of generating real-world training data, data collection remains a significant challenge and may require sourcing from multiple avenues, warranting further exploration. For future studies aiming to improve the accuracy of the ANN model, one key approach is to enhance data quality and expand the dataset. This includes collecting real-world data from diverse sources, such as experimentally validated data from different companies, to increase the model's reliability and robustness. Additionally, techniques not explored in this study could be employed to further enhance performance. These include data transformation [28; 29], which adjusts the distribution of input variables to better align with output targets; data augmentation [30; 31], which can improve the model's robustness; and regularization methods such as weight decay [32; 33] and dropout [34; 35], which help to improve the generalization ability of the ANN. Moreover, incorporating real-time data and leveraging advanced machine learning techniques--such as reinforcement learning or other deep learning approaches--could further enhance predictive accuracy and optimization, contributing to a more automated and scalable solution for packaging engineering. To address data scarcity and boundary prediction errors, future work will explore data augmentation techniques to enhance model performance. Approaches such as smooth interpolation between existing samples and regression-adapted SMOTE can generate synthetic data points in underrepresented regions, particularly at BCS extremes. Additionally, small, domain-informed perturbations of box dimensions and ECT values can simulate realistic variability and expand the dataset without requiring costly physical tests.