This guide offers a practical framework to identify machining defects, separating machine issues (e.g., tool wear) from process ones (e.g., wrong parameters). With sensors, machine learning, and real-world cases, it helps engineers boost precision and cut waste.
This article examines thermal expansion in machining across titanium, aluminum, and stainless steel alloys, using recent studies to predict and manage expansion patterns. It covers physics, modeling, and practical applications for manufacturing engineers.
