Influence of heat treatment and densification on the load capacity of sintered gears

Scholzen, Philipp; Rajaei, Ali; Brimmers, Jens; Hallstedt, Bengt; Bergs, Thomas; Broeckmann, Christoph

doi:10.1080/00325899.2022.2138171

“…Figure 6d summarizes the calculation of the tooth root load-bearing capacity. The predicted residual stress and hardness profiles agree very well with experimental results from Scholzen et al (2022). In Fig.…”

Section: Sintered Gear -Surface Hardening and Load-bearing Capacitysupporting

confidence: 81%

Materials in the Drive Chain – Modeling Materials for the Internet of Production

Rajaei

¹

,

Becker

²

,

Deng

³

et al. 2023

Interdisciplinary Excellence Accelerator Series

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In this chapter, the focus lies on a predictive description of the material response to the thermomechanical loads within different process steps by means of physical and data-driven models. The modeling approaches are demonstrated in examples of innovative production technologies for components of a drive chain: Fine blanking of parts; powder metallurgical (PM) production of gears; open-die forging and machining of drive shafts. In fine blanking, material, process, and quality data are acquired to model interactions between process and material with data-driven methods. Interpretable machine learning is utilized to non-destructively characterize the initial material state, enabling an optimization of process parameters for a given material state in the long-term. The PM process chain of the gear includes sintering, pressing, surface densification, case hardening, and finishing by grinding. Several modeling and characterization approaches are applied to quantitatively describe the microstructure evolutions in terms of porosity during sintering, density profile after cold rolling, hardness and residual stresses after heat treating and grinding and the tooth root load bearing capacity. In the example of the open-die forging, a knowledge-based approach is developed to support the decision-making process regarding the choice of the proper material and optimized pass schedules. Considering the microstructure of the forged shaft, the elastoplastic material behavior is described by a dislocation-based, multiscale modeling approach. On this basis, process simulations could be carried out to predict the process forces, chip form, residual stresses, and the tool life among other output data.

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“…Some recent work even demonstrated the possibility to directly integrate FE-based heat-treatment models in calculation tools for fatigue limit prediction of case-hardened components, thus allowing to adjust mechanical design with heat-treatment routes. 5,34,35 An FE-based heat-treatment model for case hardening of 18CrNiMo7-6 steel parts was presented in Iss et al 25 based on the methods described in Eser et al 32,33 Along with the published thermal-physical parameters from the literature, 36 the researchers used extensive dilatometric investigations of the material's carbondependent mechanical and phase-transformation behavior. Modeling the strain development as a function of position and time during quenching and tempering of carburized components allowed for the macro-scale prediction of residual stresses caused by heat treatment.…”

Section: Simulation Of the Case-hardening Heat Treatmentmentioning

confidence: 99%

“…Since residual stress measurement data for in‐depth position are difficult to obtain, FE simulation belongs to the very few methods that enable the determination of the residual stress in carburized components with complex geometries or the investigation of the impact of the carburizing process conditions on the residual stresses. Some recent work even demonstrated the possibility to directly integrate FE‐based heat‐treatment models in calculation tools for fatigue limit prediction of case‐hardened components, thus allowing to adjust mechanical design with heat‐treatment routes 5,34,35 …”

Section: Development Of a Simulative And Probabilistic Approach For P...mentioning

confidence: 99%

Fatigue strength evaluation of case‐hardened components combining heat‐treatment simulation and probabilistic approaches

Iss,

Meis,

Rajaei

et al. 2023

Fatigue Fract Eng Mat Struct

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In order to raise the hardness and strength of the surface layer of mechanical components and induce favorable residual compressive stresses, case‐hardening procedures have become established in the heat treatment of steel. In this work, a calculation concept for the fatigue strength of components that have been case‐hardened through carburizing heat treatment is being developed. The residual stress and the load stresses in complex‐shaped, carburized materials are determined using a finite element (FE) model. The fatigue limit of the components is derived using probabilistic methods and taking into account hardness gradients, residual stresses, and non‐metallic inclusions. The model is validated with available axial bending fatigue test data and then used to predict the rotating bending fatigue limit of samples with various geometries and heat‐treatment conditions. This work demonstrates the capability of combining probabilistic and FE‐based modeling to represent complex interactions between variables that affect the fatigue of heat‐treated components, such as steel cleanliness, notch shape, case‐hardening depth, or loading conditions.

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Materials in the Drive Chain – Modeling Materials for the Internet of Production

Rajaei,

Becker,

Deng

et al. 2023

Interdisciplinary Excellence Accelerator Series

0

View full text Add to dashboard Cite

In this chapter, the focus lies on a predictive description of the material response to the thermomechanical loads within different process steps by means of physical and data-driven models. The modeling approaches are demonstrated in examples of innovative production technologies for components of a drive chain: Fine blanking of parts; powder metallurgical (PM) production of gears; open-die forging and machining of drive shafts. In fine blanking, material, process, and quality data are acquired to model interactions between process and material with data-driven methods. Interpretable machine learning is utilized to non-destructively characterize the initial material state, enabling an optimization of process parameters for a given material state in the long-term. The PM process chain of the gear includes sintering, pressing, surface densification, case hardening, and finishing by grinding. Several modeling and characterization approaches are applied to quantitatively describe the microstructure evolutions in terms of porosity during sintering, density profile after cold rolling, hardness and residual stresses after heat treating and grinding and the tooth root load bearing capacity. In the example of the open-die forging, a knowledge-based approach is developed to support the decision-making process regarding the choice of the proper material and optimized pass schedules. Considering the microstructure of the forged shaft, the elastoplastic material behavior is described by a dislocation-based, multiscale modeling approach. On this basis, process simulations could be carried out to predict the process forces, chip form, residual stresses, and the tool life among other output data.

show abstract

Influence of heat treatment and densification on the load capacity of sintered gears

Cited by 3 publications

References 16 publications

Materials in the Drive Chain – Modeling Materials for the Internet of Production

Materials in the Drive Chain – Modeling Materials for the Internet of Production

Fatigue strength evaluation of case‐hardened components combining heat‐treatment simulation and probabilistic approaches

Materials in the Drive Chain – Modeling Materials for the Internet of Production

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