## 5 Conclusions  
The application of traditional quality management techniques, including quality control and quality control circles (QCCs), has historically proven to be an effective approach for enhancing production process efficiency, reducing the prevalence of defective products, and improving competitiveness in the marketplace. The implementation of QCCs has been shown to enhance production efficiency by up to 92%. However, despite their advantages, traditional methods are constrained by limitations in today's rapidly evolving production environments, which necessitate enhanced flexibility and rapid adaptation to changes. Maintaining high quality standards while increasing production speed has become a significant challenge, often resulting in elevated operating costs. The advent of machine learning (ML) techniques has been a significant advancement in the field of quality management. The implementation of machine learning (ML) techniques in drive shaft production has facilitated more precise identification of defects, reduced false alarm occurrence, and optimised the overall production process. In the company under examination, the most common defects were cracks (25.20%) and surface irregularities (21.85%), which markedly affected the quality of the final product. Furthermore, other defects, including scratches (15.63%), radial runout (14.04%), incorrect dimensions (12.28%), and incorrect hardness (11.00%), also played a significant role in affecting the overall quality and required effective solutions. A variety of machine learning (ML) techniques, including bagged trees (BT), neural networks (NNs), and support vector machines (SVMs), were employed to construct predictive quality models. The neural network model demonstrated the highest accuracy (94.7%) and the fastest prediction speed, processing 7800 observations per second, thus exhibiting the greatest efficiency in terms of computational time. Bagged trees, while exhibiting a slower processing speed (1400 observations per second), demonstrated consistent prediction accuracy. SVMs, although displaying slightly lower accuracy (94.0%), exhibited a higher training speed (1.67 s). The results of the confusion matrix analysis demonstrated that neural networks exhibited the highest efficacy in classifying defects such as surface irregularities (D3) and incorrect dimensions (D5), with classification accuracy rates of 97.4% and 97.2%, respectively. The SVMs demonstrated efficacy in classifying scratches (D1), radial rupture (D4), incorrect dimensions (D5), and hardness problems (D6). However, they exhibited suboptimal performance in classifying cracks (D2), with an observed accuracy rate of 84%. Although traditional quality management methods, such as QCCs, offer significant advantages, particularly in terms of improving production efficiency, they are constrained by limitations in terms of adaptability and speed. In rapidly evolving production environments, traditional methods are ill-equipped to accommodate real-time alterations in parameters and processes. The application of machine learning (ML) addresses these challenges by automating the detection of defects and enabling the prediction of quality issues before they occur. This makes ML a more adaptable and responsive method than traditional techniques. Furthermore, ML techniques facilitate the optimisation of critical production parameters, such as temperature, cooling speed, and tool wear. ML models continuously learn from production data, thereby improving their performance in predicting and preventing defects over time. The combination of traditional and modern approaches offers a more comprehensive and adaptable framework for quality control in the current production environment. To capitalise on these findings, the company recommends several improvement actions for its implementation. 1. It would be beneficial for the company to integrate traditional quality management tools, such as the Quality Control Circle (QCC), with modern machine learning (ML) techniques.