Macroscopic process simulation of surface and profile grinding processes estimating forces for the production of turbine blades

Grimmert, Adina; Wiederkehr, Petra

doi:10.1016/j.procir.2021.09.022

Cited by 6 publications

(6 citation statements)

References 17 publications

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“…The macroscopic grinding process simulation, which is presented in more detail in (Grimmert and Wiederkehr, 2021b), was implemented in the commercial finite element (FE) software COMSOL Multiphysics. For the simulation and evaluation of the local material removal the deformed geometry module was used, taking into account the macroscopic grinding wheel shape and neglecting the individual grains and pores of the wheel.…”

Section: Macroscopic Grinding Simulationmentioning

confidence: 99%

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Potentials of grinding process simulations for the analysis of individual grain engagement and complete grinding processes

Wiederkehr

Grimmert²,

Heining³

et al. 2023

Front. Manuf. Technol.

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Grinding processes are very complex due to the multitude of influencing parameters, resulting from the stochastic tool topography with numerous geometrically undefined abrasive cutting edges. Thus, the efficient design and optimization of these processes is a challenging task. Process simulations can be used as a flexible tool for analyzing interdependencies between several process parameters and identifying suitable process parameter values. For a precise process analysis, the choice of a process model with a corresponding model scale as well as the representation of optimization-relevant process effects are necessary. While macroscopic model approaches can be used to estimate the thermo-mechanical loads occurring in the contact zone, explicit modeling of the individual abrasive grains is required to predict the resulting surface topographies. In this paper, the use of simulation approaches for different scales for the analysis of different process parameters is discussed on the basis of selected application examples. The analysis of surface structuring in NC form grinding processes, e.g., was conducted by using an explicit geometric modeling of the individual abrasive grains in a geometric-physically based simulation approach to estimate wear-dependent resulting surface topographies. The parameterization of the empirical models used was based on numerical approaches for the detailed analysis of individual grain interventions. Using the complex production process of a turbine blade as an example, the utilization of a macroscopic simulation model for estimating the thermo-mechanical loads and the resulting temperatures in the workpiece during profile grinding processes is discussed.

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Section: Macroscopic Grinding Simulationmentioning

confidence: 99%

“…For the transient process simulation, the three force components were applied to the contact zone as grinding pressure, as shown in Figure 8B. The distribution of the grinding pressure over the contact length was analyzed by comparing the measured and simulated transient force curves (Grimmert and Wiederkehr, 2021b).…”

Section: Macroscopic Grinding Simulationmentioning

confidence: 99%

Potentials of grinding process simulations for the analysis of individual grain engagement and complete grinding processes

Wiederkehr

Grimmert²,

Heining³

et al. 2023

Front. Manuf. Technol.

Self Cite

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“…Therefore, a possibility is being sought to automate this step in process development and optimization to detect flooding early and more accurately. Since artificial intelligence (AI) has already been applied to other processes and engineering challenges, , the idea of using AI is obvious to detect the different states in the extraction column.…”

Section: Introductionmentioning

confidence: 99%

“…For image classification, an artificial neural network (ANN) can be used to recognize the features of object classes and to learn to distinguish them from others and then ideally applying them to unseen data . Among others, DL has been applied in medical technology in image recognition of cancer cells and in vehicle design of self-driving cars …”

Section: Introductionmentioning

confidence: 99%

“…10 Among others, DL has been applied in medical technology in image recognition of cancer cells and in vehicle design of self-driving cars. 8 The growing demand for small batch production of highly individualized fine chemical products creates increasing demand on the flexibility of equipment. Therefore, the demand for "Plug & Produce" ready plants continuously increases.…”

Section: Introductionmentioning

confidence: 99%

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Implementation of a Control Strategy for Hydrodynamics of a Stirred Liquid–Liquid Extraction Column Based on Convolutional Neural Networks

et al. 2022

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Online process supervision is enabled by smart sensor development, hence attracting increasing attention in pharmaceutical and chemical process engineering. Additional sensor data enable more precise process control as additional process parameters can be monitored. They are easy to integrate into modular plants, and their provided additional process parameters enable a more flexible operation of the apparatus due to the quick and more sensitive reaction to changing circumstances. An artificial intelligence-based optical sensor for the investigation of different operating states and droplet sizes within a liquid−liquid stirred DN32 extraction column in counter current flow is developed and presented in this work. Two operating states, the flooding state and the column's regular operating state, are differentiated as observable states. Additionally, the diameter of the rising liquid droplets of the disperse phase is categorized into different diameter classes. A control strategy for the extraction column is derived based on the results of the convolutional neural network-based image analysis. Thus, a robust soft sensor controlling the hydrodynamics of an extraction column was developed. The developed control strategy automatically leads the extraction column into a favorable hydrodynamically stable operation state.

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Grinding force during profile grinding of powder metallurgy superalloy FGH96 turbine disc slots structure using CBN abrasive wheel

Ding

Zhu

et al. 2022

Int J Adv Manuf Technol

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Macroscopic process simulation of surface and profile grinding processes estimating forces for the production of turbine blades

Cited by 6 publications

References 17 publications

Potentials of grinding process simulations for the analysis of individual grain engagement and complete grinding processes

Potentials of grinding process simulations for the analysis of individual grain engagement and complete grinding processes

Implementation of a Control Strategy for Hydrodynamics of a Stirred Liquid–Liquid Extraction Column Based on Convolutional Neural Networks

Grinding force during profile grinding of powder metallurgy superalloy FGH96 turbine disc slots structure using CBN abrasive wheel

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