A multiscale simulation approach to grinding ferrous surfaces for process optimization

Eder, Stefan J.; Leroch, Sabine; Grützmacher, Philipp G.; Spenger, Thomas; Heckes, H.

doi:10.1016/j.ijmecsci.2020.106186

Cited by 33 publications

(22 citation statements)

References 51 publications

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“…Any atom that has moved in grinding direction faster than half of the grinding velocity was defined as part of a chip. Chip formation during grinding is a cumulative process, therefore the respective atom even remains part of the chip if its advection velocity should ever drop below this threshold velocity [49].…”

Section: Modeling Detailsmentioning

confidence: 99%

Experimentally validated atomistic simulation of the effect of relevant grinding parameters on work piece topography, internal stresses, and microstructure

et al. 2021

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In this work, we present a fully atomistic approach to modeling a finishing process with the goal to shed light on aspects of work piece development on the microscopic scale, which are difficult or even impossible to observe in experiments, but highly relevant for the resulting material behavior. In a large-scale simulative parametric study, we varied four of the most relevant grinding parameters: The work piece material, the abrasive shape, the temperature, and the infeed depth. In order to validate our model, we compared the normalized surface roughness, the power spectral densities, the steady-state contact stresses, and the microstructure with proportionally scaled macroscopic experimental results. Although the grain sizes vary by a factor of more than 1,000 between experiment and simulation, the characteristic process parameters were reasonably reproduced, to some extent even allowing predictions of surface quality degradation due to tool wear. Using the experimentally validated model, we studied time-resolved stress profiles within the ferrite/steel work piece as well as maps of the microstructural changes occurring in the near-surface regions. We found that blunt abrasives combined with elevated temperatures have the greatest and most complex impact on near-surface microstructure and stresses, as multiple processes are in mutual competition here.

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Section: Modeling Detailsmentioning

confidence: 99%

Experimentally validated atomistic simulation of the effect of relevant grinding parameters on work piece topography, internal stresses, and microstructure

et al. 2021

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“…Progress in high-performance computing has made molecular dynamics (MD) simulations and other meshless simulation methods viable tools for studying processes occurring in sliding or abrasive contacts [18,19]. Mesoscopic approaches such as smooth particle hydrodynamics or the material point method are computationally cheap and have already been successfully used to study tribological aspects of scratching [20], milling [21], and grinding [22]. However, their strengths lie mainly in the stable treatment of large deformations, but they fall short of being able to resolve microstructural developments near the mated surfaces.…”

Section: Introductionmentioning

confidence: 99%

Elucidating the Onset of Plasticity in Sliding Contacts Using Differential Computational Orientation Tomography

Eder

Grützmacher²,

Ripoll

et al. 2021

Tribol Lett

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Depending on the mechanical and thermal energy introduced to a dry sliding interface, the near-surface regions of the mated bodies may undergo plastic deformation. In this work, we use large-scale molecular dynamics simulations to generate “differential computational orientation tomographs” (dCOT) and thus highlight changes to the microstructure near tribological FCC alloy surfaces, allowing us to detect subtle differences in lattice orientation and small distances in grain boundary migration. The analysis approach compares computationally generated orientation tomographs with their undeformed counterparts via a simple image analysis filter. We use our visualization method to discuss the acting microstructural mechanisms in a load- and time-resolved fashion, focusing on sliding conditions that lead to twinning, partial lattice rotation, and grain boundary-dominated processes. Extracting and laterally averaging the color saturation value of the generated tomographs allows us to produce quantitative time- and depth-resolved maps that give a good overview of the progress and severity of near-surface deformation. Corresponding maps of the lateral standard deviation in the color saturation show evidence of homogenization processes occurring in the tribologically loaded microstructure, frequently leading to the formation of a well-defined separation between deformed and undeformed regions. When integrated into a computational materials engineering framework, our approach could help optimize material design for tribological and other deformation problems. Graphic Abstract .

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“…In the AMT literature, optimization has been mainly focused on modeling the process parameters and their interactions, which generally involves selecting the exact parameters settings that considerably affect its performance. Examples of process parameter optimization can be found in electrical discharge machining (EDM) [24,25], grinding processes [26,27], and cutting processes [28]. See Rao & Kalyankar for an in-depth review of process optimization [29].…”

Section: Introductionmentioning

confidence: 99%

Determining optimal laser-beam cutting equipment investment through a robust optimization modeling approach

et al. 2021

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The acquisition of Advanced Manufacturing Technologies (AMT), such as high-power fiber or CO2 laser cutting equipment, generally involves high investment levels. Its payback period is usually more extended, and there is a moderate-to-high risk involved in adopting these technologies. In this work, we present a robust model that optimizes equipment investing decisions, considers the process’s technical constraint and finds an optimal production plan based on the available machinery. We propose a linear investment model based on historical demand information and take physical process parameters for a LASER cutting equipment, such as cutting speed and gas consumption. The model is then transformed into a robust optimization model which considers demand uncertainty. Second, we determine the optimal production plan based on the results of the robust optimization model and assuming that demand follows a normal distribution. As a case study, we decided on the investment and productive plan for a company that offers Laser-Beam Cutting (LBC) services. The case study validates the effectiveness of the proposed model and proves the robustness of the solution. For this specific application of the model, results showed that the optimal robust solution could increase the company’s expected profits by 6.4%.

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A multiscale simulation approach to grinding ferrous surfaces for process optimization

Cited by 33 publications

References 51 publications

Experimentally validated atomistic simulation of the effect of relevant grinding parameters on work piece topography, internal stresses, and microstructure

Experimentally validated atomistic simulation of the effect of relevant grinding parameters on work piece topography, internal stresses, and microstructure

Elucidating the Onset of Plasticity in Sliding Contacts Using Differential Computational Orientation Tomography

Determining optimal laser-beam cutting equipment investment through a robust optimization modeling approach

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