A weighted fuzzy C-means clustering method for hardness prediction

The hardness prediction model was established by support vector regression(SVR).In order to avoid exaggerating the contribution of very tiny alloying elements,a weighted fuzzy C-means(WFCM)algorithm was proposed for data clustering using improved Mahalanobis distance based on random forest importance values,which could play a full role of important features and avoid clustering center overlap.The samples were divided into two classes.The top 10 features of each class were selected to form two feature subsets for better performance of the model.The dimension and dispersion of features decreased in such feature subsets.Comparing four machine learning algorithms,SVR had the best performance and was chosen to modeling.The hyper-parameters of the SVR model were optimized by particle swarm optimization.The samples in validation set were classified according to minimum distance of sample to clustering centers,and then the SVR model trained by feature subset of corresponding class was used for prediction.Compared with the feature subset of original data set,the predicted values of model trained by feature subsets of classified samples by WFCM had higher correlation coefficient and lower root mean square error.It indicated that WFCM was an effective method to reduce the dispersion of features and improve the accuracy of model.

Hardness Profile Prediction for a 4340 Steel Spline Shaft Heat Treated by Laser Using a 3D Modeling and Experimental Validation

Laser surface transformation hardening becomes one of the most effective processes used to improve wear and fatigue resistance of mechanical parts. In this process, the material physicochemical properties and the heating system parameters have significant effects on the characteristics of the hardened surface. To appropriately exploit the benefits presented by the laser surface hardening, it is necessary to develop a comprehensive strategy to control the process variables in order to produce desired hardened surface attributes without being forced to use the traditional and fastidious trial and error procedures. The paper presents a study of hardness profile predictive modeling and experimental validation for spline shafts using a 3D model. The proposed approach is based on thermal and metallurgical simulations, experimental investigations and statistical analysis to build the prediction model. The simulation of the hardening process is carried out using 3D finite element model on commercial software. The model is used to estimate the temperature distribution and the hardness profile attributes for various hardening parameters, such as laser power, shaft rotation speed and scanning speed. The experimental calibration and validation of the model are performed on a 3 kW Nd:Yag laser system using a structured experimental design and confirmed statistical analysis tools. The results reveal that the model can provide not only a consistent and accurate prediction of temperature distribution and hardness profile characteristics under variable hardening parameters and conditions but also a comprehensive and quantitative analysis of process parameters effects. The modelling results show a great concordance between predicted and measured values for the dimensions of hardened zones.

A Predictive Modeling Based on Regression and Artificial Neural Network Analysis of Laser Transformation Hardening for Cylindrical Steel Workpieces

Laser surface hardening is a very promising hardening process for ferrous alloys where transformations occur during cooling after laser heating in the solid state. The characteristics of the hardened surface depend on the physicochemical properties of the material as well as the heating system parameters. To exploit the benefits presented by the laser hardening process, it is necessary to develop an integrated strategy to control the process parameters in order to produce desired hardened surface attributes without being forced to use the traditional and fastidious trial and error procedures. This study presents a comprehensive modelling approach for predicting the hardened surface physical and geometrical attributes. The laser surface transformation hardening of cylindrical AISI 4340 steel workpieces is modeled using the conventional regression equation method as well as artificial neural network method. The process parameters included in the study are laser power, beam scanning speed, and the workpiece rotational speed. The upper and the lower limits for each parameter are chosen considering the start of the transformation hardening and the maximum hardened zone without surface melting. The resulting models are able to predict the depths representing the maximum hardness zone, the hardness drop zone, and the overheated zone without martensite transformation. Because of its ability to model highly nonlinear problems, the ANN based model presents the best modelling results and can predict the hardness profile with good accuracy.

Single Track Laser Surface Hardening Model for AISI 4340 Steel Using the Finite Element Method

Laser surface hardening becomes one of the most effective techniques used to enhance wear and fatigue resistance of mechanical parts. The characteristics of the hardened surface depend on the physicochemical properties of the material as well as the heating system parameters. To adequately exploit the benefits presented by the laser heating method, it is necessary to develop a comprehensive strategy to control the process parameters in order to produce desired hardened surface attributes without being forced to use the traditional and fastidious trial and error procedures. This study presents a comprehensive approach used to build a simplified model for predicting the hardness profile. A finite element method based prediction model for AISI 4340 steel is investigated. A circular shape with a Gaussian distribution is used for modeling the laser heat source. COMSOL MULTIPHYSICS software is used to solve the heat transfer equations, estimate the temperature distribution in the part and consequently predict the hardness profile. A commercial 3 kW Nd:Yag laser system is combined to a structured experimental design and confirmed statistical analysis tools for conducting the experimental calibration and validation of the model. The results reveal that the model can effectively lead to a consistent and accurate prediction of the hardness profile characteristics under variable hardening parameters and conditions. The results show great concordance between predicted and measured values for the dimensions of hardened and melted zones.