OPTIMIZATION OF THREAD CUTTING MACHINING PARAMETERS: FROM EMPIRICAL RULES TO AI-DRIVEN STRATEGIES

Abstract

This scientific article presents a comprehensive investigation into the optimization of machining parameters for thread cutting operations, with a particular focus on threaded components. Thread manufacturing represents a critical yet challenging domain in precision engineering, where the selection of cutting speed, feed rate, depth of cut, and tool geometry directly determines thread quality, tool life, production efficiency, and cost. Traditional parameter selection, heavily reliant on operator experience, handbook recommendations, and costly trial-and-error, often leads to suboptimal performance, premature tool wear, and inconsistent quality. This study systematically analyzes the limitations of empirical approaches and proposes a structured, multi-faceted optimization framework. This framework integrates modern methodologies including Taguchi Design of Experiments (DoE), physics-based predictive modeling of tool wear and cutting forces, and advanced Artificial Intelligence (AI) techniques such as machine learning (ML) and metaheuristic algorithms. We detail the procedural workflow for data acquisition, model development, and validation, demonstrating how a hybrid data-physics approach can transcend traditional constraints. The results indicate that optimized parameter sets derived from this framework can significantly enhance surface integrity, extend tool life by mitigating wear mechanisms, improve dimensional accuracy, and boost overall productivity. By bridging the gap between shop-floor practice and computational engineering science, this work provides a clear pathway toward intelligent, adaptive, and economically sustainable thread machining processes.