The Use of Virtual Sensors for Bead Size Measurements in Wire-Arc Directed Energy Deposition

Abstract

Having garnered significant attention in the scientific community over the past decade, wire-arc directed energy deposition (arc-DED) technology is at the heart of this investigation into additive manufacturing parameters. Singularly focused on Invar as the selected material, the primary objective revolves around devising a virtual sensor for the indirect size measurement of the bead. This innovative methodology involves the seamless integration of internal signals and sensors, enabling the derivation of crucial measurements sans the requirement for direct physical interaction or conventional measurement methodologies. The internal signals recorded, the comprising voltage, the current, the energy from the welding heat source generator, the wire feed speed from the feeding system, the traverse speed from the machine axes, and the temperature from a pyrometer located in the head were all captured through the control of the machine specially dedicated to the arc-DED process during a phase of optimizing and modeling the bead geometry. Finally, a feedforward neural network (FNN), also known as a multi-layer perceptron (MLP), is designed, with the internal signals serving as the input and the height and width of the bead constituting the output. Remarkably cost-effective, this solution circumvents the need for intricate measurements and significantly contributes to the proper layer-by-layer growth process. Furthermore, a neural network model is implemented with a test loss of 0.144 and a test accuracy of 1.0 in order to predict weld bead geometry based on process parameters, thus offering a promising approach for real-time monitoring and defect detection.

Virtual Sensor for On-Line Hardness Assessment in TIG Welding of Inconel 600 Alloy Thin Plates

“…Sensors 2024, 24, x FOR PEER REVIEW 3 of 18 [18], the detection of wear of the secondary cable of a welding gun [19], and the control of robotic welding [20].…”

“…In the realm of research incorporating ANNs into welding practices, the advent of the error back-propagation algorithm in the mid-1980s marked the beginning of the ANN’s extensive application across various welding methodologies. This includes utilizing ANNs for predicting the quality of welds based on control parameters, recommending optimal welding control parameters to achieve specific weld characteristics, deriving profiles from laser vision sensing images, and employing vision sensors for quality and seam tracking in welding processes [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. Furthermore, machine learning techniques have been extensively adopted in diverse welding procedures.…”

“…These sensors are favored because they are adaptable, cost-effective, and capable of measuring elements that are challenging to assess due to their complexity [ 16 ]. In welding practice, virtual sensors are applied to predict weld bead geometry based on process parameters [ 18 ], the detection of wear of the secondary cable of a welding gun [ 19 ], and the control of robotic welding [ 20 ].…”