Martensitic steels are tempered to increase the toughness of the metastable martensite, which is brittle in the as-quenched state, and to achieve a more stable microstructure. During the tempering of steels, several particular overlapping effects can arise. Classical dilatometric investigations can only detect effects by monitoring the integral length change of the sample. Additional in-situ diffractometry allowed a differentiation of the individual effects such as transformation of retained austenite and formation of cementite during tempering. Additionally, the lattice parameters of martensite and therefrom the tetragonality was analyzed. Two low-alloy steels with carbon contents of 0.4 and 1.0 wt.% and a high-alloy 5Cr-1Mo-steel with 0.4 wt.% carbon were investigated by dilatometry and in-situ diffractometry. In this paper, microstructural effects during tempering of the investigated steels are discussed by a comparative study of dilatometric and diffractometric experiments. The influence of the chemical composition on the tempering behavior is illustrated by comparing the determined effects of the three steels. The kinetics of tempering is similar for the low-alloy steels and shifted to much higher temperatures for the high-alloy steel. During tempering, the tetragonality of martensite in the steel with 1.0 wt% carbon shifts towards a low carbon behavior, as in the steels with 0.4 wt.% carbon.

This paper presents a digital model for the powder metallurgical (PM) production chain of high-performance sintered gears based on an integrated computational materials engineering (ICME) platform. Discrete and finite element methods (DEM and FEM) were combined to describe the macroscopic material response to the thermomechanical loads and process conditions during the entire production process. The microstructural evolution during the sintering process was predicted on the meso-scale using a Monte-Carlo Model. The effective elastic properties were determined by a homogenization method based on modelling a representative volume element (RVE). The results were subsequently used for the FE modelling of the heat treatment process. Through the development of multi-scale models, it was possible obtain characteristics of the microstructural features. The predicted hardness and residual stress distributions allowed the calculation of the tooth root load bearing capacity of the heat-treated sintered gears.

Grinding is one of the most important finishing processes in industrial production. During grinding, the workpiece is subjected to thermomechanical loads. Thermal damage can occur in terms of microstructure changes due to a critical temperature history. A holistic model of the relevant physical load fields and their interactions would help describe and predict the influence of grinding loads on the residual stresses in the surface zone of the workpiece. In this paper, a very promising approach is introduced to simulate grinding of the hardened and tempered bearing steel AISI 52100 using the Finite Element Method (FEM). A material model was developed to describe the thermomechanical and metallurgical changes of the bearing steel under the process loads. Material properties were modeled depending on the temperature and microstructure changes. Temperature gradients, microstructure evolution, thermal, and phase transformation strains were integrated in the model to predict the residual stress state after grinding. Experimental and simulative investigations were conducted for pendulum grinding, and the measured and simulated residuals stresses were compared. The depth of the subsurface zone, where thermally influenced microstructural changes occur, varied with changes of the process parameters. Experiments and simulations showed compressive stresses in the re-hardened zone and tensile stresses in the tempered area.

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.

Materials serve as the foundation of the technical framework on which modern society relies every day. Generations have developed new materials, tried to understand the origins of their properties, and found ways to predict them. Modern computational tools have vastly expanded our capabilities to make predictions, not only of material properties but also of component properties and of the component health status over its life cycle. Integrated Computational Materials Engineering (ICME) aims at simulating the material and component properties along the complete process chain and across the length scales from microstructure to component scale. In this way a digital twin of the material or component can be generated, which can be leveraged to facilitate gains in productivity and service life of technical systems. By reducing the complexity of models for the digital twin where necessary, combining them with in-process data using innovative sensor technology and suitable mathematically driven approximation procedures such as machine learning, it is possible to conceive a digital material shadow that resolves elements of the dilemma between data granularity, data volume, and processing speed to enable process monitoring and control for materials processing. To enable communication between humans and machines it is necessary to create a strictly defined language in the form of ontologies. Ontologies are typically domain-specific, but care must be taken to make them consistent across domains. Integrated Structural Health Engineering (ISHE) aims at predicting and monitoring the health state of components over their entire life cycle, enabling timely replacement of components and avoiding costly and possibly life-threatening failures. In particular when components are subjected to cyclic loading, their structural health does not primarily depend on the average material properties, but on the presence of more or less statistically distributed defects. These defects are intrinsic to materials processing, cannot be completely avoided, and evolve during various stages of the production process. The objective of ISHE is to predict their formation and evolution during the production process and their impact on the component structural health during its life cycle. It is clear that the material and component properties are strongly dependent on the process by which they are produced. Therefore, many of the topics discussed in this part have relational counterparts in Part IV, “Production”.

Materials serve as the foundation of the technical framework on which modern society relies every day. Generations have developed new materials, tried to understand the origins of their properties, and found ways to predict them. Modern computational tools have vastly expanded our capabilities to make predictions, not only of material properties but also of component properties and of the component health status over its life cycle. Integrated Computational Materials Engineering (ICME) aims at simulating the material and component properties along the complete process chain and across the length scales from microstructure to component scale. In this way a digital twin of the material or component can be generated, which can be leveraged to facilitate gains in productivity and service life of technical systems. By reducing the complexity of models for the digital twin where necessary, combining them with in-process data using innovative sensor technology and suitable mathematically driven approximation procedures such as machine learning, it is possible to conceive a digital material shadow that resolves elements of the dilemma between data granularity, data volume, and processing speed to enable process monitoring and control for materials processing. To enable communication between humans and machines it is necessary to create a strictly defined language in the form of ontologies. Ontologies are typically domain-specific, but care must be taken to make them consistent across domains. Integrated Structural Health Engineering (ISHE) aims at predicting and monitoring the health state of components over their entire life cycle, enabling timely replacement of components and avoiding costly and possibly life-threatening failures. In particular when components are subjected to cyclic loading, their structural health does not primarily depend on the average material properties, but on the presence of more or less statistically distributed defects. These defects are intrinsic to materials processing, cannot be completely avoided, and evolve during various stages of the production process. The objective of ISHE is to predict their formation and evolution during the production process and their impact on the component structural health during its life cycle. It is clear that the material and component properties are strongly dependent on the process by which they are produced. Therefore, many of the topics discussed in this part have relational counterparts in Part IV: Production.