Optimization of Machining Parameters for Material Removal Rate and Machining Time While Cutting Inconel 600 with Tungsten Carbide Textured Tools

Khan, M. Adam; Gupta, Kapil

doi:10.1007/978-3-030-50312-3_2

Cited by 5 publications

(1 citation statement)

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“…Analytical, numerical, and empirical approaches were used to model the cutting operation. The output of the cutting model, directly or indirectly, is used to estimate machining objective functions such as process productivity [14,17,[20][21][22], machined part quality [23][24][25][26][27], production cost [28][29][30][31][32], tool wear rate [33,34], chatter stability thresholds [35,36], energy consumption [37][38][39] and carbon emission [40][41][42]. Online process optimization is defined as a numerical control process in which the machining parameters are regulated based on time-varying feedback [43].…”

Section: Introductionmentioning

confidence: 99%

High-Performance Machining System: Process Optimization for a Cyber-Physical System – a Review

Gohari,

Hassan,

Shi

et al. 2024

Preprint

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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 the 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 the uncertainties can be reduced, resulting in improved process productivity. This paper reviews the recent advances in high-performance machining systems with a focus on the 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 the offline and online process optimization approaches have been presented. Process optimization objectives such as tool life utilization, dynamic stability, enhanced productivity, improved machined part quality, and reduced energy consumption and carbon emissions are independently investigated for the offline and online optimization methods. Addressing the critical objectives and constraints prevalent in industrial applications, the paper explores the challenges and opportunities inherent in developing a robust cyber-physical optimization system

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