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2016
DOI: 10.1016/j.measurement.2016.02.011
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Optimization of micro milling of hardened steel with different grain sizes using multi-objective evolutionary algorithm

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Cited by 13 publications
(7 citation statements)
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References 26 publications
(32 reference statements)
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“…In general, the grain size of aluminum alloy is usually larger than that of titanium alloy and larger grain size corresponds to the larger minimum cutting thickness during micro-milling process, which results comparatively higher feed rate for size effect point. 5,25 From Figures 7 and 8, we can find that the position of size effect point changes with different vibration amplitudes. Under CM condition, as vibration amplitude of 0 mm, the size effect points for 6061T6 and TC4 were around feed rate of 40 and 30 mm/min, respectively.…”
Section: Size Effect Pointmentioning
confidence: 97%
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“…In general, the grain size of aluminum alloy is usually larger than that of titanium alloy and larger grain size corresponds to the larger minimum cutting thickness during micro-milling process, which results comparatively higher feed rate for size effect point. 5,25 From Figures 7 and 8, we can find that the position of size effect point changes with different vibration amplitudes. Under CM condition, as vibration amplitude of 0 mm, the size effect points for 6061T6 and TC4 were around feed rate of 40 and 30 mm/min, respectively.…”
Section: Size Effect Pointmentioning
confidence: 97%
“…In micro-milling, material removal in micro-milling is often characterized by significant plowing and rubbing compared to conventional scale machining. 4,5 For micro-milling, additional difficulties arise from the well-known machining size effect and the fragility of the miniature tool. 6,7 The main feature of size-effects in micro-machining is the flow stress of the material which is influenced by different types of size-effects and the most important effect in machining is the increase of the normalized cutting force with decreasing the uncut chip thickness.…”
Section: Introductionmentioning
confidence: 99%
“…In the captured images, the tool can be in any positions, and the algorithm can evaluate it correctly. This is based on the fact that the size of the tool (which perpendicular to the cutting edges of the tool) does [4], [33], [38], [46], [48], [51], [54], [83], [88], [89], [122], [133], [144], [164], [165], [166], [167], [168] AISI 1015: [169] AISI 1018: [125] AISI 1040: [77] AISI 1050: [37] AISI 4340: [119], [139], [170] AISI P20: [20], [145], [164] S960QL: [171] NAK80: [123], [172], [173], [174] AISI A2-annealed: [175] 42CrMo4: [167] X13NiMnCuAl4-2-1-1: [146] SK2: [176] Hardened steels AISI H13: [2], [30], [42], [72], [80], [164], [177], [178], [179],…”
Section: Micro-milling Process Monitoringmentioning
confidence: 99%
“…In follow-up research, Lauro et al 30 used an integrated least square model combined with a genetic algorithm to identify the optimal process parameters, that is, cutting speed, feed rate and grain size were considered. The genetic algorithm was chosen, because it is widely considered a robust and efficient optimisation method which can relatively easily be integrated into least squares algorithms.…”
Section: Literature Reviewmentioning
confidence: 99%