پیش بینی خواص مکانیکی اتصالات جوش داده شده با توجه به رگرسیون برداری پشتیبانی

Support vector regression (SVR) networks were developed based on kernel functions of linear kernel, polynomial kernel, radial basis function (RBF) and Sigmoid in this paper. The input parameters of TC4 alloy plates include weld current, weld speed and argon flow while the output parameters include tensile strength, flexural strength and elongation. The SVR networks were used to build the mechanical properties model of welded joints and make predictions. A comparison was made between the predictions based on SVR and that based on adaptive-network based fuzzy inference system (ANFIS). The results indicated that the predicted precision based on SVR with radial basis kernel function was higher than that with the other three kernel functions and that based on ANFIS.

As an advanced connecting technique, welding has been widely used in many fields of industrial production for its high efficiency, energy conservation, high quality, automation and intelligentization. To better predict the mechanical properties of welded joints, it is very important to develop a model between welding parameters and the mechanical properties. In recent years, lots of studies have been done by researchers both in home and abroad, and some prediction methods have been developed which are mainly about neural network or modified neural network[1-3]. Although these methods can get better predictions, the prediction accuracy and training speed are still not enough. The contradiction between over-fitting and generalization can not be reconciled easily with these methods. Also, the neural network may converge to a local optimum rather than a global one. Therefore, it is quite necessary to find a faster,more accurate and more efficient prediction method. Support vector machine (SVM) is a new machine learning method developed by Vapnik based on statistical learning with succinct mathematical terms and good generalized applications[4]. Compared with other prediction methods, the rationale of SVM is more complete, and the parameters needed is relatively less, and it can better avoid getting stuck in local optimum. The author used experimental data of mechanical properties of TIG welded joints of TC4 alloys reported in reference[3], and developed the mechanical properties models of welded joints under different technological parameters based on SVR and made predictions. Compared with the prediction results with adaptive-network based fuzzy inference system (ANFIS) used in reference [3], the results showed that SVR was superior than neural network.

SVR is a kind of effective method to predict mechanical properties of welded joints. Different kernel functions have a great impact on the predictions based on SVR network. The predicted precisions based on SVR with radial basis kernel function was higher than that with the other three kernel functions and that based on ANFIS.SVR needs only a few measured data to build the model, and has such advantages as fast modeling,simple, high prediction precision and good generalization. This method provides a new effective way to better predict mechanical properties of welded joints with different parameters, and can provide theoretical optimal designs of parameters in the welding procedure test.