Control of the weld quality using welding parameters in a robotic welding process

Abstract

Purpose: Since the welding automations have widely been required for industries and engineering, the development of the predicted model has become more important for the increased demands for the automatic welding systems where a poor welding quality becomes apparent if the welding parameters are not controlled. The automated welding system must be modelling and controlling the changes in weld characteristics and produced the output that is in some way related to the change being detected as welding quality. To be acceptable a weld quality must be positioned accurately with respect to the joints, have good appearance with sufficient penetration and reduce low porosity and inclusion content. Design/methodology/approach: To achieve the objectives, two intelligent models involving the use of a neural network algorithm in arc welding process with the help of a numerical analysis program MATLAB have been developed. Findings: The results represented that welding quality was fully capable of quantifying and qualifying the welding faults. Research limitations/implications: Welding parameters in the arc welding process should be well established and categorized for development of the automatic welding system. Furthermore, typical characteristics of welding quality are the bead geometry, composition, microstructure and appearance. However, an intelligent algorithm that predicts the optimal bead geometry and accomplishes the desired mechanical properties of the weldment in the robotic GMA (Gas Metal Arc) welding should be required. The developed algorithm should expand a wide range of material thicknesses and be applicable in all welding position for arc welding process. Furthermore, the model must be available in the form of mathematical equations for the automatic welding system. Practical implications: The neural network models which called BP (Back Propagation) and LM (Levenberg-Marquardt) neural networks to predict optimal welding parameters on the required bead reinforcement area in lab joint in the robotic GMA welding process have been developed. Experimental results have been employed to find the optimal algorithm to predict bead reinforcement area by BP and LM neural networks in lab joint in the robotic GMA welding. The developed intelligent models can be estimated the optimal welding parameters on the desired bead reinforcement area and weld criteria, establish guidelines and criteria for the most effective joint design for the robotic arc welding process. Originality/value: In this study, intelligent models, which employed the neural network algorithms, one of AI (Artificial Intelligence) technologies have been developed to study the effects of welding parameters on bead reinforcement area and to predict the optimal bead reinforcement area for lab joint in the robotic GMA welding process. BP (Back Propagation) and LM (Levenberg-Marquardt) neural network algorithm have been used to develop the intelligent model.

Sustainable Hybrid Manufacturing of AlSi5 Alloy Turbine Blade Prototype by Robotic Direct Energy Layered Deposition and Subsequent Milling: An Alternative to Selective Laser Melting?

“…In addition, the wire feed mechanism supplies the filler wire atop the welding zone at a steady speed without interruption, so deposition is fast, and the quality of the weld is better than with manual arc welding [ 2 ]. The reverse movement of the wire improves the separation mechanism of the molten droplet during the short-circuit current [ 2 , 12 ]. The microprocessor synergistically automates the current source and enforces a low value, thus regulating the molten alloy transfer without spraying, splashing, or wider-scale splattering.…”

“…With an adequate combination of parameters in sample C3, a uniform 5.7 mm thick flat wall and 2.2 mm layer height, which is suitable for the purpose of the combined manufacturing process, could be processed, as shown in Figure 9 (green outline). It was also realized that at lower wire feed speeds, the corrugation of the wall is less pronounced due to the stabler arc generation and the more even melting of the material [ 12 ], and any minor deposition errors during melting can be mitigated by resurfacing in the next pass.…”

“…To achieve a shape of the design workpiece with inclined surfaces, we were required to avoid supporting structures that needed to be removed by processing. Robot welding allows for any orientation of the welding torch, and this was used to weld the sloping walls in a horizontal welding position [ 12 ]. The flat walls were deposited at different angles in two ways, as illustrated in Figure 10 , firstly, by orienting the burner perpendicular to the base plate by laterally shifting the layers as in Figure 10 a, and secondly by orienting the burner in the direction of wall growth, as shown in Figure 10 b.…”

“…Regarding the surfacing of metals, due to temperature differences, the base material experienced thermally induced deformation, which further complicated the exact positioning of the workpiece [ 2 , 12 ]. With a built-in cooling system for faster heat dissipation, this time could be reduced to 50%.…”

“…However, these Al alloys necessitate stricter production quality control, high electrical conductivity, short fatigue lifecycles, and difficult welding/joining processes due to the development of thermally induced residual stresses. In turn, these mechanisms may lead to structural distortion, restricted rigidity, porosity, joint softening, fatigue-induced wear, a low coefficient of strength, and, eventually, intergranular cracking [ 5 , 12 , 13 ]. Gas pores and coarse grains typically provide poorer mechanical properties in additively manufactured AlSi5 alloys, so Wang et al [ 14 ] identified low arc current and low pulse frequency ranges that could satisfy fine-grained uniform microstructures.…”