Hardness Prediction of Grind-Hardening Layer Based on Integrated Approach of Finite Element and Cellular Automata

As an emerging composite processing technology, the grind-hardening process implements efficient removal on workpiece materials and surface strengthening by the effective utilization of grinding heat. The strengthening effect of grind-hardening on a workpiece surface is principally achieved by a hardened layer, which is chiefly composed of martensite. As a primary parameter to evaluate the strengthening effect, the hardness of the hardened layer mostly depends on the surface microstructure of the workpiece. On this basis, this paper integrated the finite element (FE) and cellular automata (CA) approach to explore the distribution and variation of the grinding temperature of the workpiece surface in a grind-hardening process. Moreover, the simulation of the transformation process of “initial microstructure–austenite–martensite” for the workpiece helps determine the martensite fraction and then predict the hardness of the hardened layer with different grinding parameters. Finally, the effectiveness of the hardness prediction is confirmed by the grind-hardening experiment. Both the theoretical analysis and experiment results show that the variation in the grinding temperature will cause the formation to a certain depth of a hardened layer on the workpiece surface in the grind-hardening process. Actually, the martensite fraction determines the hardness of the hardened layer. As the grinding depth and feeding speed increase, the martensite fraction grows, which results in an increase in its hardness value.

A Brief Review on Validation for Heat Treatment Simulation

Heat treatment simulation has progressed to the stage where several commercial software are available. Validations of simulation functions using experimental results contributed to this realization. Organizing information on the validations may be effective for maintaining the functions and educating users about the nature of the phenomena. For this reason, the author here briefly reviews mainly his validation cases. Since experiments using specimens having relatively simple shapes can reveal the essence of complex phenomena, the results have been used in the validations. When the basic functions such as heat transfer, phase transformation, latent heat, and hardness prediction were comprehensively validated in the early stages of software development, the author used experimental results of the inverse hardening in quenched steel cylinders. After that, his validations of the software at the stage where adding stress and strain analysis functions, used effectively measurement data of length and diameter changes, and residual stress distributions in normally quenched steel cylinders. While, it was also worth to validate curving in long specimens cooled unevenly, which included a case of specimens with a similar cross-section to the Japanese sword. In addition, the author validated distortions and residual stresses in carburized and quenched, induction hardened, and also nitrided specimens.

Hardness Prediction in Quenched and Tempered Nodular Cast Iron Using the Hollomon-Jaffe Parameter

The Hollomon-Jaffe parameter is usually used to stablish a equivalence between time and temperature in a tempering treatment, but not to predict the harness of the alloy after the treatment. In this paper this last possibility has been studied. A group of cast iron samples was annealed and cooled at different rates in order to obtain samples with three different hardness values. These samples were tempered using different times and temperatures. The Hollomon-Jaffe parameter was calculated for each case and a relationship based on a logistic function between that parameter and the final hardness was stablished. This relationship was found to depend on the initial hardness and the lowest hardness achievable.

Temperature-Based Prediction of Joint Hardness in TIG Welding of Inconel 600, 625 and 718 Nickel Superalloys

Welding is an important process in terms of manufacturing components for various types of machines and structures. One of the vital and still unsolved issues is determining the quality and properties welded joint in an online manner. In this paper, a technique for prediction of joint hardness based on the sequence of thermogram acquired during welding process is proposed. First, the correspondence between temperature, welding linear energy and hardness was revealed and confirmed using correlation analysis. Using a linear regression model, relations between temperature and hardness were described. According to obtained results in the joint area, prediction error was as low as 1.25%, while for HAZ it exceeded 15%. Future work on optimizing model and input data for HAZ hardness prediction are planned.

Deep Learning-Based Hardness Prediction of Novel Refractory High-Entropy Alloys with Experimental Validation

Hardness is an essential property in the design of refractory high entropy alloys (RHEAs). This study shows how a neural network (NN) model can be used to predict the hardness of a RHEA, for the first time. We predicted the hardness of several alloys, including the novel C0.1Cr3Mo11.9Nb20Re15Ta30W20 using the NN model. The hardness predicted from the NN model was consistent with the available experimental results. The NN model prediction of C0.1Cr3Mo11.9Nb20Re15Ta30W20 was verified by experimentally synthesizing and investigating its microstructure properties and hardness. This model provides an alternative route to determine the Vickers hardness of RHEAs.