An improved thermal model for SPH metal cutting simulations on GPU

Afrasiabi, Mohamadreza; Klippel, Hagen; Roethlin, M.; Wegener, Konrad

doi:10.1016/j.apm.2021.08.010

Cited by 22 publications

(10 citation statements)

References 40 publications

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“…Secondly, the simulation results presented in [40] are low resolution (as the SPH runtime is not accelerated) and taken only after a cut distance of 0.3 mm, which seems insufficient to establish a steady-state temperature distribution. The very recent publication of [41] addresses these issues and presents a complete thermal model for metal cutting simulation.…”

Section: Methodological Challenges In Metal Cutting Simulationmentioning

confidence: 99%

“…As elaborated upon by Childs et al [30], some form of strain-softening (or failure law) is necessary to account for sawtooth shaped and serrated chip formation in cutting titanium alloys. A constitutive model with this capability exists, e.g., the so-called TANH flow stress rule presented in [59], and has already been used in both SPH [41] and FEM [59] metal cutting simulations. However, we did not follow this approach here as the number of unknown material parameters in such a model is generally high.…”

Section: Prediction Of Thermal Loadsmentioning

confidence: 99%

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A Numerical-Experimental Study on Orthogonal Cutting of AISI 1045 Steel and Ti6Al4V Alloy: SPH and FEM Modeling with Newly Identified Friction Coefficients

Afrasiabi

Saelzer

Berger

et al. 2021

Metals

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Numerical simulation of metal cutting with rigorous experimental validation is a profitable approach that facilitates process optimization and better productivity. In this work, we apply the Smoothed Particle Hydrodynamics (SPH) and Finite Element Method (FEM) to simulate the chip formation process within a thermo-mechanically coupled framework. A series of cutting experiments on two widely-used workpiece materials, i.e., AISI 1045 steel and Ti6Al4V titanium alloy, is conducted for validation purposes. Furthermore, we present a novel technique to measure the rake face temperature without manipulating the chip flow within the experimental framework, which offers a new quality of the experimental validation of thermal loads in orthogonal metal cutting. All material parameters and friction coefficients are identified in-situ, proposing new values for temperature-dependent and velocity-dependent friction coefficients of AISI 1045 and Ti6Al4V under the cutting conditions. Simulation results show that the choice of friction coefficient has a higher impact on SPH forces than FEM. Average errors of force prediction for SPH and FEM were in the range of 33% and 23%, respectively. Except for the rake face temperature of Ti6Al4V, both SPH and FEM provide accurate predictions of thermal loads with 5–20% error.

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Section: Methodological Challenges In Metal Cutting Simulationmentioning

confidence: 99%

Section: Prediction Of Thermal Loadsmentioning

confidence: 99%

A Numerical-Experimental Study on Orthogonal Cutting of AISI 1045 Steel and Ti6Al4V Alloy: SPH and FEM Modeling with Newly Identified Friction Coefficients

Afrasiabi

Saelzer

Berger

et al. 2021

Metals

Self Cite

View full text Add to dashboard Cite

show abstract

“…Other numerical models for simulating the machining process include meshless and particle-based methods, the discrete element method, and the molecular dynamics (MD) simulation method. Meshless methods such as Smoothed-Particle Hydrodynamics (SPH) have been adopted as an alternative to the widely used FEM to handle large deformations in the workpiece [61,62]. Röthlin et al [63] conducted high-resolution SPH simulations using scientific computing on a Graphics Processing Unit, GPU.…”

Section: Efficient Multi-scale Modelling For Process Optimizationmentioning

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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“… 33 developed the GPU parallel computing program for simulating transient thermal stress and welding deformation. The GPU-based parallel algorithm has been successfully used to improve the efficiency in fluid dynamics simulations, 34 , 35 , 36 Eulerian-Lagrangian simulations, 37 phase field simulations, 38 the combined discrete-finite element simulations, 39 the meshfree simulations, 40 , 41 etc. However, for the process EMF, the large deformation of workpiece and the electromagnetic field are intimately coupled, which are solved with the explicit method and implicit method, respectively.…”

Section: Introductionmentioning

confidence: 99%

High-efficiency computation for electromagnetic forming process: An explicit-implicit GPU approach

Pei,

Tang,

et al. 2024

iScience

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An improved thermal model for SPH metal cutting simulations on GPU

Cited by 22 publications

References 40 publications

A Numerical-Experimental Study on Orthogonal Cutting of AISI 1045 Steel and Ti6Al4V Alloy: SPH and FEM Modeling with Newly Identified Friction Coefficients

A Numerical-Experimental Study on Orthogonal Cutting of AISI 1045 Steel and Ti6Al4V Alloy: SPH and FEM Modeling with Newly Identified Friction Coefficients

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

High-efficiency computation for electromagnetic forming process: An explicit-implicit GPU approach

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