## 6 Limitations and Future Research  
Despite the promising results of this study, there are several limitations that must be acknowledged to improve the effectiveness of machine learning (ML) techniques in the quality management of propeller shaft manufacturing. First, the dataset used in this study consisted of 565 datasets, which, while sufficient for preliminary modelling, may not be representative of the full complexity of the man ufacturing process. A larger and more diverse dataset would improve the reliability and generalisability of the models, making them more robust to different production environments. Another limitation is the potential risk of model overfitting, particularly with complex techniques such as neural networks. With smaller datasets, models may perform well during training but fail to generalise to unseen data. Although cross-validation was implemented to address this issue, additional regularisation techniques or access to larger datasets could help mitigate this risk. This study also focused on predicting certain types of defects, such as cracks and surface irregularities, which can limit the applicability of the models to other potential quality issues not included in the analysis. Future work should consider a broader range of defects to increase the versatility of ML-based solutions. Furthermore, the complexity of the manufacturing environment may not have been fully captured in this research. Factors such as machine downtime, operator behaviour, and external environmental conditions such as temperature and humidity, which could influence defect occurrence, were not included in the models. Finally, the computational demands of advanced machine learning models, particularly deep neural networks, can present a significant challenge for small and medium-sized enterprises (SMEs) with limited access to high-performance computing infrastructure. Although the presented approach demonstrates that standard hardware is sufficient for smaller datasets, the scalability of these models is constrained as the dataset size increases. For SMEs, this implies that while entry-level models can be effectively trained and deployed with typical computing resources, the adoption of more complex models for large datasets may be constrained by infrastructure limitations. Additionally, the implementation of ML in production environments requires the acquisition of specific employee skills in data handling, model application, and result interpretation. Consequently, although computational resources may not initially be a limiting factor for smaller datasets, both data volume and workforce competency are critical considerations for SMEs aiming to effectively scale ML solutions. In summary, these limitations highlight areas for further improvement, including expanding data collection, optimising models for broader applicability, and addressing practical constraints on computational resources. Future research on the application of machine learning (ML) to quality management in manufacturing should focus on several key areas to improve the effectiveness and scalability of current models. One of the most important directions is the expansion of the dataset. Collecting larger and more diverse data from multiple production cycles and different products would improve the generalisability of the models, allowing them to predict defects more accurately in different manufacturing environments. Another important path for future research is to explore additional ML techniques. While this study used neural networks, bagged trees, and support vector machines (SVMs), other algorithms such as Random Forests, gradient boosting machines, or reinforcement learning could provide new insights and further improve prediction accuracy, especially when dealing with more complex, nonlinear datasets. The integration of real-time data into predictive models is another promising area.