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Optimization Study on the Process Parameters for Molybdenum Milling
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DOI：10.3390/met16080935.png)
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Abstract
En 中文
Molybdenum (Mo), owing to its excellent properties, is widely used as a plasma-facing material and is recognized as a typical difficult-to-machine material. Achieving high-quality, low-damage machining is essential for ensuring the service reliability of Mo components. However, studies on the milling of Mo remain limited. Therefore, this study investigates a high-quality, low-damage milling technique for Mo based on analyses of milling force, machined surface roughness, and white layer formation. First, the effects of machining parameters, including radial depth of cut (ae), spindle speed (n), and feed per tooth (fz), on the responses, namely milling force (F) and surface roughness (Ra), were investigated. The relationships between milling force, surface roughness, and white layer formation were analyzed. Subsequently, the response surface methodology (RSM) was employed to reveal the influence mechanisms of the machining parameters and their interactions on the response variables. Finally, a Kriging surrogate model integrated with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was adopted to identify the optimal machining parameter combination for high-quality, low-damage milling. The results indicate that the milling force and white-layer thickness exhibit consistent increasing trends with increasing feed per tooth under the investigated conditions, demonstrating that controlling the milling force is an effective approach for achieving high-quality, low-damage milling of Mo. For the simultaneous minimization of milling force and surface roughness, the optimal machining parameters were determined to be a radial depth of cut of 0.2101 mm, a spindle speed of 10,090.7 rpm, and a feed per tooth of 0.01 mm/z. These findings provide valuable process parameter guidance for the precision machining of Mo components.
Keywords:
milling
response surface methodology (RSM)
surface integrity
multi-objective optimization
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