Sensor Fusion to Estimate the Depth and Width of the Weld Bead in Real Time in GMAW Processes

Abstract

The arc welding process is widely used in industry but its automatic control is limited by the difficulty in measuring the weld bead geometry and closing the control loop on the arc, which has adverse environmental conditions. To address this problem, this work proposes a system to capture the welding variables and send stimuli to the Gas Metal Arc Welding (GMAW) conventional process with a constant voltage power source, which allows weld bead geometry estimation with an open-loop control. Dynamic models of depth and width estimators of the weld bead are implemented based on the fusion of thermographic data, welding current and welding voltage in a multilayer perceptron neural network. The estimators were trained and validated off-line with data from a novel algorithm developed to extract the features of the infrared image, a laser profilometer was implemented to measure the bead dimensions and an image processing algorithm that measures depth by making a longitudinal cut in the weld bead. These estimators are optimized for embedded devices and real-time processing and were implemented on a Field-Programmable Gate Array (FPGA) device. Experiments to collect data, train and validate the estimators are presented and discussed. The results show that the proposed method is useful in industrial and research environments.

Measurement and estimation of the weld bead geometry in arc welding processes: the last 50 years of development

“…The results from previous work are satisfactory, but the authors assume that the thermographic curve has a Gaussian behaviour and use this curve to obtain several parameters. However, the measures obtained in [136] prove that the thermographic curve can show several peaks and irregular contour, as is shown in Fig. 18.…”

“…An accurate estimator of the weld bead depth (D), based on a perceptron neural network that fusing the infrared features of the weld molten pool and the welding current, is obtained in [136,137] for the GMAW process. The artificial neural network has 8 neurons in the input layer; 12 neurons in hidden the layer and one in the output layer.…”

Bacterial Memetic Algorithm Trained Fuzzy System-Based Model of Single Weld Bead Geometry

“…Statistical methods are often used in the literature to provide the comparison base for different methods since their linear behavior is usually outperformed by methods capable of modeling the non-linear behavior [12], [21]. Computational intelligence (CI) techniques -such as artificial neural networks (ANN) [12], [22], [23], fuzzy inference systems (FS) [20], [24], [25], evolutionary algorithms (EA) [40], [41], and genetic programming (GP) [42] are widely used to describe the WBG. However, due to their limitations [26], several hybrid computing techniques were developed [26], [32], including the adaptive neurofuzzy inference systems (ANFIS) [25], [26], [43] and the evolutionary fuzzy systems (EFS) [44], [45].…”

“…Coefficients for the Multi-variable Regression analysis Bestard et al[22] (Table 81., 3., and 4.) are modeling methods based on analyzing the image of an IR camera to estimate the width of weld beads during welding.…”

A Study on the Optimal Offset Distance Between Welding Torch and the Infrared Thermometers

“…Sadek et al [Sadek and Fernand (2010)] presented an evaluation of an infrared sensor for monitoring the welding pool temperature in a GTA welding process to develop a real-time system control. Bestard et al [Bestard, Sampaio, Vargas et al (2018)] proposed a system developed to stimulate a GMA welding conventional process and collect values of the arc variables, infrared thermography and weld bead geometry. Zondi et al [Zondi, Tekane, Magidimisha et al (2017)] have reported on the temperature history of the welding cycle in the nozzle-toshell circumferential weld of the pressure vessel using IR thermography.…”