Model-Based Monitoring and Control of Continuous Dress Creep-Feed Form Grinding

Guo, Changsheng; Campomanes, M.; Mcintosh, D.; Becze, C.E.; Malkin, S.

doi:10.1016/s0007-8506(07)60694-5

Cited by 19 publications

(5 citation statements)

References 7 publications

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“…As shown in Figure 6A when comparing the cast and finished part, several millimeters of stock material have to be removed in order to manufacture the fir tree profiles. In consideration of maximizing productivity in serial production for components with a high batch volume, these profiles are usually transferred to the component with only few grinding strokes utilizing creep-feed grinding processes with porous corundum grinding wheels (Guo et al, 2003;Guo et al, 2004;Klocke et al, 2015). Since the rotating parts, such as the blades, are particularly safety relevant components of an aircraft engine, overloading of the component during the manufacturing process must be prevented.…”

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.

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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%

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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“…A simulation model is developed along with power signals to indicate the critical limit of the grinding process, where the forces generated are unable to initiate the crack formation in the grinding of the turbine blade. Hence, the forces are within the limit due to the simulation model (44). Therefore, the power signal generated from the grinding machine is also given as an input to the machine learning algorithms to predict the failure of the grinding wheel.…”

Section: Detect Machine Overloading By the Power Measurementmentioning

confidence: 99%

A Review on Advanced Monitoring and Identifying the Status of Grinding Machine Using Machine Learning Algorithms

Anand¹,

Sivakumar²,

Prabadevi³

2022

ECS Trans.

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Recently, the development of the grinding process is improved by including automatic sensors, actuators, control systems, artificial intelligence, and industrial internet of things in the grinding machine. Already existing contact type techniques are replaced with the automatic sensor system to incorporate artificial intelligence in the normal grinding process. Therefore, optical and laser technology is emerging as a smart device to observe the surface topography of the grinding wheel, surface finish of the ground surface, and automatic dressing process. Moreover, artificial intelligence consists of machine learning, which teaches the grinding machine based on the existing data available to improve the quality and the production rate of the grinding process. This can be achieved by controlling the process parameter, monitoring the machine health, and attaining optimum condition. The present review paper addresses the existing problems associated with the contact type and non-contact type measurement. The study also emphasizes the importance of machine learning algorithms to predict the failure of the grinding wheel and surface roughness of workpiece material in the grinding process.

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“…For instance, when grinding fir tree profiles on blade root sections, it is essential that resulting grinding forces be maintained within a critical threshold during the process in order to avoid damaging the special coating on the blade airfoils as well as to ensure acceptable surface integrity. Consequently, grinding force and heat flux can be indirectly estimated from the recorded spindle power, with deviations from the normal process signature profile indicating possible process issues [76]. Based on results from grinding models together with in-process data, it is possible to assess form errors through correlations between monitored tangential grinding forces/power and workpiece material characteristics as well as predictions from finite element (FE) simulations.…”

Section: Monitoring Of Aerospace Grinding Processesmentioning

confidence: 99%