• Bored complex cavity in deep-hole part with L/D ratio over 32 and variable diameter ratio over 1.74. • Identified segmented path with optimal parameters, produced short spiral and fragmented chips. • Achieved dimensional control with diameter errors under ±0.2 mm, roundness errors below 0.03 mm. • Reaching roughness surface Sa of 1.387 μm without the need for secondary finishing processes. High-performance industrial applications increasingly require the rapid and precise machining of specialized internal cavities within deep-hole parts; however, achieving high efficiency and precision remains a significant challenge for parts with large length-to-diameter (L/D) ratios, particularly when fabricated from difficult-to-cut materials such as titanium alloys. This study investigates a titanium alloy specimen with a 42 mm internal diameter and a 1360 mm length (L/D > 32), utilizing a customized variable-diameter boring tool and a dedicated numerical control (NC) system to machine both trapezoidal and arc-shaped cavities. To optimize chip breaking and evacuation in confined spaces, a segmented tool-path strategy was developed in conjunction with process parameter optimization via finite element analysis (FEA) and the Box-Behnken Design (BBD) response surface method (RSM). The influence of feed rate, spindle speed, and depth of cut on chip length, material removal volume, and tool wear was systematically evaluated, revealing that optimized parameters produce manageable short spiral curls and maintain minimal tool wear over four hours of continuous operation. Validation through Coordinate Measuring Machine (CMM) detection and surface topography analysis confirmed a maximum adjustable diameter ratio exceeding 1.74, diameter errors within ±0.2 mm, roundness errors below 0.03 mm, and superior surface integrity. These results demonstrate that the proposed cavity boring process provides a robust, high-precision solution for machining complex internal geometries in large L/D ratio deep hole parts.
Building similarity graph...
Analyzing shared references across papers
Loading...
Jintao Liang
Xiaotian Song
Xiaolan Han
Results in Engineering
Xidian University
Xi'an Shiyou University
Building similarity graph...
Analyzing shared references across papers
Loading...
Liang et al. (Fri,) studied this question.
synapsesocial.com/papers/69fd7d4abfa21ec5bbf05d85 — DOI: https://doi.org/10.1016/j.rineng.2026.110888