Deflection error modeling during thin-wall machining

Llanos, Iñigo; Robles, Ainhoa; Condón, Javier; Arizmendi, C. M.; Beristain, Arkaitz

doi:10.1016/j.procir.2023.03.030

Cited by 3 publications

(1 citation statement)

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“…The determination and control of tool/workpiece deflections are crucial, especially when the compliance of the tool or workpiece is high. High-speed rough-end milling [209] and the machining of thin-wall workpieces [210] are examples for cases in which the deflections of the tool and workpiece are crucial for obtaining the desired geometric accuracy of the final part while maintaining a high productivity. It is usually constrained to avoid a sudden tool failure, the deterioration of the surface integrity of machined part, or part scrapping.…”

Section: Geometric Accuracymentioning

confidence: 99%

Cyber–Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era—A Review

Gohari,

Hassan,

Shi

et al. 2024

Sensors

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The fifth Industrial revolution (I5.0) prioritizes resilience and sustainability, integrating cognitive cyber-physical systems and advanced technologies to enhance machining processes. Numerous research studies have been conducted to optimize machining operations by identifying and reducing sources of uncertainty and estimating the optimal cutting parameters. Virtual modeling and Tool Condition Monitoring (TCM) methodologies have been developed to assess the cutting states during machining processes. With a precise estimation of cutting states, the safety margin necessary to deal with uncertainties can be reduced, resulting in improved process productivity. This paper reviews the recent advances in high-performance machining systems, with a focus on cyber-physical models developed for the cutting operation of difficult-to-cut materials using cemented carbide tools. An overview of the literature and background on the advances in offline and online process optimization approaches are presented. Process optimization objectives such as tool life utilization, dynamic stability, enhanced productivity, improved machined part quality, reduced energy consumption, and carbon emissions are independently investigated for these offline and online optimization methods. Addressing the critical objectives and constraints prevalent in industrial applications, this paper explores the challenges and opportunities inherent to developing a robust cyber–physical optimization system.

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Section: Geometric Accuracymentioning

confidence: 99%