The advancement of Artificial Intelligence (AI) in manufacturing begins with the Fourth Industrial Revolution. AI allows manufacturing with efficiency optimization, product quality improvement, cost reduction, and ease of real-time predictive maintenance. The topic is clearly stated in the literature. However, it fails to note the turning operation and quality optimization. This review attempts to clarify the factors influencing the quality of the finished product, related challenges, and the integration of AI to address these issues. The article highlights several methods for developing AI models and their real-world implementation. The paper systematically assesses the available literature, categorizing process characteristics and AI techniques based on data sources and management methodologies. The key result demonstrates that artificial neural networks and regression analysis are widely used in machining and optimization procedures, with fuzzy logic proving advantageous. Data management and filtration are essential for a reliable AI model. This paper offers insights into pre-processing, algorithm choice, and optimization methodologies, guiding researchers in constructing successful AI models for quality optimization in turning operations.
Science Journal 