Prediction of Weld Reinforcement Based on Vision Sensing in GMA Additive Manufacturing Process

Abstract

In the gas-metal-arc (GMA) additive manufacturing process, the shape of the molten pool, the temperature field of the workpiece and the heat dissipation conditions change with the increase of cladding layers, which can affect the dimensional accuracy of the workpiece; hence, it is necessary to monitor the additive manufacturing process online. At present, there is little research about formation-dimension monitoring in the GMA additive manufacturing process; in this paper, weld reinforcement prediction in the GMA additive manufacturing process was conducted, the visual-sensing system for molten pool was established, and a laser locating system was designed to match every frame of the molten pool image with the actual weld location. Extracting the shape and location features of the molten pool as visual features, on the basis of a back-propagation (BP) neural network, we developed the prediction model for weld reinforcement in the GMA additive manufacturing process. Experiment results showed that the model could accurately predict weld reinforcement. By changing the cooling time between adjacent cladding layers, the generalization ability of the prediction model was further verified.

Additive Manufacturing Research and Applications

“…This Special Issue offers a wide scope in the research field around 3D printing, including the following [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]: the use of 3D printing in system design, AM with binding jetting, powder manufacturing technologies in 3D printing, fatigue performance of additively manufactured metals such as the Ti-6Al-4V alloy, 3D-printing method with metallic powder and a laser-based 3D printer, 3D-printed custom-made implants, laser-directed energy deposition (LDED) process of TiC-TMC coatings, Wire Arc Additive Manufacturing, cranial implant fabrication without supports in electron beam melting (EBM) additive manufacturing, the influence of material properties and characteristics in laser powder bed fusion, Design For Additive Manufacturing (DFAM), porosity evaluation of additively manufactured parts, fabrication of coatings by laser additive manufacturing, laser powder bed fusion additive manufacturing, plasma metal deposition (PMD), as-metal-arc (GMA) additive manufacturing process, and spreading process maps for powder-bed additive manufacturing derived from physics model-based machine learning.…”

Application of Convolutional Neural Networks for Classifying Penetration Conditions in GMAW Processes Using STFT of Welding Data

“…Additionally, various methods such as fuzzy logic, neurofuzzy, image processing, D-S evidence, and physical models have been used to predict welding quality [21]. Due to the challenges associated with incorporating disturbances into GMAW processes, recent studies have explored ANN-based prediction of welding quality using diverse sensor-based data [20][21][22][23].…”

Research on Automated Defect Classification Based on Visual Sensing and Convolutional Neural Network-Support Vector Machine for GTA-Assisted Droplet Deposition Manufacturing Process

“…Zhuang et al [12] proposed k-nearest neighbor (KNN) classification algorithms based on contour curve-KNN (CC-KNN) and locality preserving projection-KNN (LPP-KNN) effectively performed in vision and spectral analysis. Yu et al [13] established the visual sensing system to capture every frame of the molten pool images matched for the actual weld location in the GMA AM process. A back propagation (BP) neural network was used to extract the shape and location features of the molten pool.…”

“…The classification accuracy of the finally developed model on the UB-Moog dataset is 0.82 by optimizing hyper parameters. Wang et al [29], based on previous work [13], developed a prediction network (PredNet) to predict the change of molten pool shape 140ms in advance. Through regression network (SERes), the predicted results were regressed to the accurate weld reinforcement information of the deposited layer in advance.…”