Ensuring Optimal Performance and Longevity For Milling Technology

Abstract

This thesis examines the key factors that affect the performance, reliability, and service life of milling machines, emphasizing the importance of proper maintenance in modern manufacturing. It reviews the main types and components of milling machines and identifies common failures such as tool wear, chatter, overheating, and dimensional inaccuracies. The work evaluates several diagnostic and condition-monitoring techniques—including vibration analysis, acoustic emission monitoring, infrared thermography, spindle current sensing, and NDT methods—that enable early detection of faults. Various maintenance strategies, such as preventive, predictive, corrective maintenance, TPM, FMEA, RBM, and AHP, are compared to highlight how data-driven approaches reduce downtime and cost. The thesis also describes essential repair procedures like spindle repair, alignment, calibration, and electrical system troubleshooting. Overall, it concludes that predictive maintenance combined with continuous monitoring offers the most effective path to ensuring sustainable, accurate, and cost-efficient milling operations.