Author:
Liu Hui,Kibireva Anna,Meurer Markus,Bergs Thomas
Abstract
AbstractCutting simulation is a crucial tool that enables engineers and operators to optimize machining processes virtually, before producing physical parts. The accuracy of these simulations relies heavily on validated models, encompassing both friction and material parameters. The prevalent technique for calibrating material models in cutting simulations is the inverse method. This state-of-the-art approach indirectly determines model parameters by comparing simulated outcomes with experimental data. However, the manual calibration process can be complex and time-consuming due to the intricacies of numerical simulation setups and the abundance of material model parameters. To address these challenges, this paper presents a novel fully-automated calibration approach utilizing multi-objective optimization algorithms. This approach integrates a modular design, simplifying the calibration process and enabling automatic calibration of any model parameters within cutting simulations. The approach has been successfully applied to calibrate the model parameters of AISI 1045 and X30CrMoN15-1 materials. Moreover, through a comparison of various optimization algorithms, this paper underscores the efficiency of the swarm optimizer in calibrating model parameters, particularly in scenarios with restricted computational resources.
Publisher
Springer Science and Business Media LLC
Subject
Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Software,Control and Systems Engineering
Reference40 articles.
1. Grand View Research (2020) Precision engineering machines market size, share & trends analysis report by end-use (automotive, non-automotive), by region (North America, Europe, Asia Pacific, Latin America, Middle East & Africa), and segment forecasts, pp 2021–2028
2. Verband Deutscher Machinen- und Anlagenbau eV (2022) Konjunkturgrafiken zur Jahrespressekonferenz
3. Pau J (ed) (2011) Finite element method in manufacturing processes. ISTE and Wiley, London and Hoboken, NJ
4. Mackerle J (2003) Finite element analysis and simulation of machining: an addendum. Int J Mach Tools Manuf 43(1):103–114. https://doi.org/10.1016/S0890-6955(02)00162-1
5. Klocke F (2018) Fertigungsverfahren 1. Springer, Berlin Heidelberg Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54207-1