Overlapping optimization of hybrid deposited and micro-rolling additive manufacturing

Purpose This paper aims to summarize the influence law of hybrid deposited and micro-rolling (HDMR) technology on the bead morphology and overlapping coefficient. A better bead topology positively supports the overlapping deposited in multi-beads between layers while actively assisting the subsequent layer's deposition in the wire and arc additive manufacturing (WAAM). Hybrid-deposited and micro-rolling (HDMR) additive manufacturing (AM) technology can smooth the weld bead for improved surface quality. However, the micro-rolling process will change the weld bead profile fitting curve to affect the overlapping coefficient. Design/methodology/approach Weld bead contours for WAAM and HDMR were extracted using line lasers. A comparison of bead profile curves was conducted to determine the influence law of micro-zone rolling on the welding bead contour and fitting curve. Aiming at the optimized overlapping coefficient of weld bead in HDMR AM, the optimal HDMR overlapping coefficient curve was proposed which varies with the reduction based on the best surface flatness. The mathematical model for overlapping in HDMR was checked by comparing the HDMR weld bead contours under different rolling reductions. Findings A fitting function of the bead forming by HDMR AM was proposed based on the law of conservation of mass. The change rule of the HDMR weld bead overlapping spacing with the degree of weld bead rolling reduction was generated using the flat-top transition calculation for this model. Considering the damming-up impact of the first bead, the overlapping coefficient was examined for its effect on layer surface flatness. Originality/value Using the predicted overlapping model, the optimal overlapping coefficients for different rolling reductions can be achieved without experiments. These conclusions can encourage the development of HDMR technology.

Extracting Weld Bead Shapes from Radiographic Testing Images with U-Net

Metals created by melting basic metal and welding rods in welding operations are referred to as weld beads. The weld bead shape allows the observation of pores and defects such as cracks in the weld zone. Radiographic testing images are used to determine the quality of the weld zone. The extraction of only the weld bead to determine the generative pattern of the bead can help efficiently locate defects in the weld zone. However, manual extraction of the weld bead from weld images is not time and cost-effective. Efficient and rapid welding quality inspection can be conducted by automating weld bead extraction through deep learning. As a result, objectivity can be secured in the quality inspection and determination of the weld zone in the shipbuilding and offshore plant industry. This study presents a method for detecting the weld bead shape and location from the weld zone image using image preprocessing and deep learning models, and extracting the weld bead through image post-processing. In addition, to diversify the data and improve the deep learning performance, data augmentation was performed to artificially expand the image data. Contrast limited adaptive histogram equalization (CLAHE) is used as an image preprocessing method, and the bead is extracted using U-Net, a pixel-based deep learning model. Consequently, the mean intersection over union (mIoU) values are found to be 90.58% and 85.44% in the train and test experiments, respectively. Successful extraction of the bead from the radiographic testing image through post-processing is achieved.

Representation of gas metal arc pulsed welding process behavior on bead geometry: a study of leading variables

Gas metal arc welding is one of the most influential processes in the production and repair of structures and equipment; therefore, the need to improve the productivity and quality of welded joints has led to the development of techniques for good control of welding parameters. Also, the development of semi-automatic welding processes led to the control of one of the variables such as pulsed current; this technique is characterized by a lower heat input and lower energy expenditure, which directly influences the structural quality of the welded joint and the geometry of the weld bead. This work focused on evaluating the effects of various welding operating parameters using the central composite design tool based on the response surface methodology; next, the experimental development employed an inverter type power source for weld depositions, a commercial grade Stargold clean 96% Ar and 4% CO2 shielding gas at the rate of 15 L/min stationary arc, a 1.2 mm metal cored wire for welding deposit and a carbon steel base plate with a thickness of 6 mm. During the welding process, the torch was kept at a 90° inclination and a 16 mm stroke. To examine the adequacy of the empirical models and the significance of the regression coefficients, the variance analysis was employed. Consequently, the graphs were obtained through the determination of the model; from the statistical results obtained, it was shown that the above models were adequate to predict the weld width, bead height, and penetration within the range of variables studied. Furthermore, it was observed that the wire feed rate it has a very marked effect on weld bead geometry, followed by frequency pulse and peak current; finally, the effectiveness of employing these methodologies for the management of variables attributing to the execution of welding tasks with higher accuracy was demonstrated.

Performance of weld bead profile during A-TIG welding on nitrogen alloyed stainless steel

Austenitic stainless steel (ASS) is widely fabricated by tungsten inert gas (TIG) welding for aesthetic look and superior mechanical properties while compared to other arc welding process. Hitherto, the limitation of this process is low depth of penetration and less productivity. To overcome this problem activated tungsten inert gas (A-TIG) welding process is employed as an alternative. In this investigation the welding performance of conventional TIG welding is compared with A-TIG process using TiO2 and SiO2 flux with respect to weld bead geometry. The experimental investigation on A-TIG welding of ASS-201 grade shows TiO2 flux helps in achieve higher penetration as compared to SiO2 flux. While welding with SiO2 the hardness in HAZ and weld region higher than that of TIG welding process.