A new model to distinguish welds performed by short-circuit GMAW based on FRESH algorithm and MLP ANN

Abstract

The short-circuit gas metal arc welding has been continuously studied over the years, due to its important role in manufacturing processes. Concerning the process, many kinds of research are carried out aiming to understand the influence of the shielding gas in welds quality. In this context, this work treats the voltage and current welding signals as time series and applies a feature extraction based on scalable hypothesis tests, which is called FRESH algorithm, in order to obtain the signal features. After that, these features are applied in a multilayer perceptron artificial neural network, trained by the scaled conjugate gradient method, which classifies the welds according to the flow rate and type of shielding gas used in the process. The model presented excellent performance, which shows that the proposal is suitable to be used in welding quality monitoring.

User-Level Handover Decision Making Based on Machine Learning Approaches

“…The methods applied for the HO decision (classification problem) are Autonomous Learning Multimodel System (ALMMo) [15] (which has no tuning parameters, since it extracts all the features and adjustments from data); Self-Organizing Fuzzy Logic Classifier (SOFL) [16] (using Mahalanobis distance and Granularity Level of 2.9); Type-2 Fuzzy Logic Classifier (T2FLS) [17], [18] (being the learning parameter = 0.01, tolerance = 10 − 8, 1 = 0.9 and 2 = 0.999); Support Vector Machine Classifier (SVM) [7], [19] (with linear kernel and penalty parameter of 10 and 100 for Scenarios 1 and 2, respectively); and Multilayer Perceptron Classifier (MLP) [20], [21] (with 6 neurons in hidden layers and solver lbfgs).…”

“…Regarding the regression problem (estimation of the completed download percentage and the download duration), six methods are compared: Multilayer Perceptron Regressor (MLP) [20], [21] (with 22 and 4 neurons in hidden layers, tanh and logistic activation functions and lbfgs solver for Scenarios 1 and 2, respectively); KNN [3], [20] with 4 and 6 neighbors considered for each Scenario; Random Forest (RF) [7], [22] with 94 and 106 trees in the forest, in each case; Gradient Boosting Machine (GBM) [23], with 84 and 120 trees in their ensemble; Extreme Gradient Boosting (XGBoost) [9], with 174 and 120 estimators each; and Light Gradient Boosting Machine (LightGBM) [8], which used 148 and 139 estimators in the ensemble for each Scenario.…”