Dynamic Modeling of Weld Bead Geometry Features in Thick Plate GMAW Based on Machine Vision and Learning

Abstract

Weld bead geometry features (WBGFs) such as the bead width, height, area, and center of gravity are the common factors for weighing welding quality control. The effective modeling of these WBGFs contributes to implementing timely decision making of welding process parameters to improve welding quality and enhance automatic levels. In this work, a dynamic modeling method of WBGFs is presented based on machine vision and learning in multipass gas metal arc welding (GMAW) with typical joints. A laser vision sensing system is used to detect weld seam profiles (WSPs) during the GMAW process. A novel WSP extraction method is proposed using scale-invariant feature transform and machine learning. The feature points of the extracted WSP, namely the boundary points of the weld beads, are identified with slope mutation detection and number supervision. In order to stabilize the modeling process, a fault detection and diagnosis method is implemented with cubic exponential smoothing, and the diagnostic accuracy is within 1.50 pixels. A linear interpolation method is presented to implement sub pixel discrimination of the weld bead before modeling WBGFs. With the effective feature points and the extracted WSP, a scheme of modeling the area, center of gravity, and all-position width and height of the weld bead is presented. Experimental results show that the proposed method in this work adapts to the variable features of the weld beads in thick plate GMAW with T-joints and butt/lap joints. This work can provide more evidence to control the weld formation in a thick plate GMAW in real time.

Automatic weld joint type recognition in intelligent welding using image features and machine learning algorithms

“…Machine learning (ML) is a discipline of artificial intelligence (AI) and computer science that utilizes algorithms that learn from previous information to generalize new information, process input with noise and complicated data settings, use past know-how, and construct fresh notions. ML techniques have been prominently used in manufacturing like predictive analytics, Defect prognosis, tool/machine condition monitoring (Wuest et al, 2016;Vakharia et al, 2017;Wang et al, 2018), quality control through image recognition , weld bead diagnosis (Chen et al, 2018;Rodríguez-Gonzálvez and Rodríguez-Martín, 2019;Yang et al, 2019;He et al, 2020), weld quality monitoring (Sumesh et al, 2015;Mahadevan et al, 2021), weld joint Artificial Intelligence for Engineering Design, Analysis and Manufacturing optimization (Choudhury et al, 2020;Mongan et al, 2020), robot trajectory generation (Duque et al, 2019), welding monitoring (Cai et al, 2019), real-time weld geometry prediction (Lei et al, 2019), weld parameters prediction (Las-Casas et al, 2018), recognition of welding jointtype (Fan et al, 2017;Zeng et al, 2020;Chen et al, 2022), and Fault detection and diagnosis (He et al, 2019) are expanding daily. Therefore, we infer that the application of intelligent welding in the manufacturing sector must acknowledge the need for support in handling the high-dimensional data, difficulty, and interactions among the involved data to benefit from increased data availability.…”

Influence of Process Parameters in Gas-Metal Arc Welding (GMAW) of Carbon Steels

“…Studies on GMAW welding have developed a considerable understanding on how to address the challenges experienced in the GMAW process in many applications. Some studies have used statistical quality control tools, such as statistical models [12,13], numerical models [14][15][16][17][18], and artificial intelligent models [19,20], to predict the desired output parameters by refining the input process parameters [19,20]. This has led to process advancements in terms of equipment and process control for improvement in the weld quality and productivity, even though some challenges still exist [11].…”

Inspection Algorithm of Welding Bead Based on Image Projection

“…Therefore, welding quality inspection is essential to determine whether a product is good or bad. There are various methods to inspect the quality of welding bead, including visual inspection, radiographic inspection, liquid penetrant inspection, and ultrasonic inspection [2][3][4][5]. Visual inspection is direct examination by human eyes.…”