Intelligent modelling of back-side weld bead geometry using weld pool surface characteristic parameters

2013 ◽  
Vol 25 (6) ◽  
pp. 1301-1313 ◽  
Author(s):  
XueWu Wang ◽  
RuiRui Li
Keyword(s):  
Weld Pool ◽  
Surface Characteristic ◽  
Weld Bead ◽  
Bead Geometry ◽  
Back Side ◽  
Characteristic Parameters ◽  
Weld Bead Geometry ◽  
Pool Surface ◽  
Weld Pool Surface

scholarly journals 3D Weld Pool Surface Geometry Measurement with Adaptive Passive Vision Images

2019 ◽  
Vol 98 (12) ◽  
pp. 379s-386s
Author(s):  
ZONGYAO CHEN ◽  
◽  
JIAN CHEN ◽  
ZHILI FENG
Keyword(s):  
Real Time ◽  
Weld Pool ◽  
Three Dimensional ◽  
Bead Geometry ◽  
Monitoring And Control ◽  
Surface Geometry ◽  
Weld Bead Geometry ◽  
Pool Surface ◽  
Weld Pool Geometry ◽  
Geometry Measurement

Monitoring weld pool geometry without the appropriate auxiliary light source remains challenging due to the interference from the intense arc light. In this work, a new software framework was developed to measure the key features related to welding pool three-dimensional (3D) geometry based on the two-dimensional (2D) passive vision images. It was found that the interference of the arc light on the weld pool image can be effectively controlled by adjusting the camera exposure time based on the decision made from machine learning classifier. Weld pool width, trailing length, and surface height (SH) were calculated in real time, and the result agreed with the measurement of the weld bead geometry. The method presented here established the foundation for real-time penetration monitoring and control.

Estimation of Weld Bead Geometry in Gas Metal Arc Welding of Aluminum Using Electrical Signals and Liquid Surface Model

2015 ◽  
Author(s):  
Hao Du ◽  
S. Jack Hu ◽  
Pei-Chung Wang ◽  
Jingjing Li
Keyword(s):  
Weld Pool ◽  
Liquid Surface ◽  
Gas Metal Arc Welding ◽  
Weld Bead ◽  
Surface Model ◽  
Bead Geometry ◽  
Weld Bead Geometry ◽  
Monitoring Method ◽  
Metal Arc Welding ◽  
Effective Contact

While gas metal arc welding of aluminum has been widely implemented in automotive industry, it is prone to process disturbances. Therefore, an effective monitoring method is necessary to ensure the weld quality (e.g., weld bead height, width and penetration). In this study, an online method was developed to estimate the weld bead geometry using the combination of the real time welding signals, characteristics of weld bead geometry and a liquid surface model. The liquid surface model can represent the steady-state geometry of the weld pool with varying characteristics, such as volume, boundary conditions and surface tension. To model the geometry of the weld pool, the weld pool moved and updated its geometry in response to the merging of each new droplet. The detachment time and size of the new droplet were obtained by analyzing real time welding voltage and current. The surface tension was characterized by the effective contact angle measured under various welding conditions. Moreover, the effective contact angles and radius of arc-affected zone were introduced to significantly simplify the complicated thermal behavior during welding. Finally, the weld bead geometry was estimated using real welding signals in both globular and spray transfer modes and validated with the experimental results. This method predicts weld bead geometry with satisfied accurancy for engineering use and in a comparable speed as the welding speed; and thus can be applied as an online monitoring method for the weld bead quality.

A modified version of MATLAB application window for predicting the weld bead profile and stress–strain plot of AA5052 CMT weldment using ER4043

SIMULATION ◽  
2021 ◽  
pp. 003754972110315
Author(s):  
B Girinath ◽  
N Siva Shanmugam
Keyword(s):  
Strain Curve ◽  
Weld Bead ◽  
Welding Parameters ◽  
Bead Geometry ◽  
Hybrid Technique ◽  
Stress Strain Curve ◽  
Stress Strain ◽  
Weld Bead Geometry ◽  
Inference System ◽  
Engineering Stress

The present study deals with the extended version of our previous research work. In this article, for predicting the entire weld bead geometry and engineering stress–strain curve of the cold metal transfer (CMT) weldment, a MATLAB based application window (second version) is developed with certain modifications. In the first version, for predicting the entire weld bead geometry, apart from weld bead characteristics, x and y coordinates (24 from each) of the extracted points are considered. Finally, in the first version, 53 output values (five for weld bead characteristics and 48 for x and y coordinates) are predicted using both multiple regression analysis (MRA) and adaptive neuro fuzzy inference system (ANFIS) technique to get an idea related to the complete weld bead geometry without performing the actual welding process. The obtained weld bead shapes using both the techniques are compared with the experimentally obtained bead shapes. Based on the results obtained from the first version and the knowledge acquired from literature, the complete shape of weld bead obtained using ANFIS is in good agreement with the experimentally obtained weld bead shape. This motivated us to adopt a hybrid technique known as ANFIS (combined artificial neural network and fuzzy features) alone in this paper for predicting the weld bead shape and engineering stress–strain curve of the welded joint. In the present study, an attempt is made to evaluate the accuracy of the prediction when the number of trials is reduced to half and increasing the number of data points from the macrograph to twice. Complete weld bead geometry and the engineering stress–strain curves were predicted against the input welding parameters (welding current and welding speed), fed by the user in the MATLAB application window. Finally, the entire weld bead geometries were predicted by both the first and the second version are compared and validated with the experimentally obtained weld bead shapes. The similar procedure was followed for predicting the engineering stress–strain curve to compare with experimental outcomes.

Electric arc shape and weld bead geometry analysis under the electromagnetic constriction and expansion effect

2021 ◽  
Author(s):  
Miguel Guilherme Antonello ◽  
Alexandre Queiroz Bracarense ◽  
Régis Henrique Gonçalves e Silva ◽  
Ivan Olszanski Pigozzo ◽  
Marcelo Pompermaier Okuyama
Keyword(s):  
Electric Arc ◽  
Weld Bead ◽  
Bead Geometry ◽  
Weld Bead Geometry ◽  
Arc Shape ◽  
Geometry Analysis ◽  
Expansion Effect

A Model for In-Process Control of Thermal Properties During Welding

1989 ◽  
Vol 111 (1) ◽  
pp. 40-50 ◽  
Author(s):  
C. C. Doumanidis ◽  
D. E. Hardt
Keyword(s):  
Process Control ◽  
Thermal Response ◽  
Weld Bead ◽  
Distributed Parameter ◽  
Bead Geometry ◽  
Actual Process ◽  
Weld Bead Geometry ◽  
Thermally Activated ◽  
Real Time Tracking ◽  
Welding Processes

The control of welding processes has received much attention in the past decade, with most attention placed on real-time tracking of weld seams. The actual process control has been investigated primarily in the context of weld bead geometry regulation, ignoring for the most part the metallurgical properties of the weld. This paper addresses the latter problem through development of a model for in-process control of thermally activated material properties of weld. In particular, a causal model relating accessible inputs to the outputs of weld bead area, heat affected zone width, and centerline cooling rate at a critical temperature is developed. Since the thermal system is a distributed parameter, nonlinear one, it is modelled numerically to provide a baseline of simulation information. Experiments are performed that measure the thermal response of actual weldments and are used to calibrate the simulation and then to verify the basic dynamics predicted. Simulation results are then used to derive a locally linear transfer function matrix relating inputs and outputs. These are shown to be nonstationary, depending strongly upon the operating point and the boundary conditions.

A stereo vision method for tracking particle flow on the weld pool surface

2009 ◽  
Vol 105 (12) ◽  
pp. 123104 ◽  
Author(s):  
C. X. Zhao ◽  
I. M. Richardson ◽  
S. Kenjeres ◽  
C. R. Kleijn ◽  
Z. Saldi
Keyword(s):  
Weld Pool ◽  
Stereo Vision ◽  
Particle Flow ◽  
Pool Surface ◽  
Weld Pool Surface

Predicting Bead Geometry of 2F-Fillet Joint Welded by Small Wire SAW

2012 ◽  
Vol 576 ◽  
pp. 185-188 ◽  
Author(s):  
Shahfuan Hanif Ahmad Hamidi ◽  
Abdul Ghalib Tham ◽  
Yupiter H.P. Manurung ◽  
Sunhaji Kiyai Abas
Keyword(s):  
Welding Current ◽  
Weld Bead ◽  
Welding Parameter ◽  
Bead Geometry ◽  
Weld Bead Geometry ◽  
Absolute Deviation ◽  
Fillet Weld ◽  
Welding Condition ◽  
Code Of Practice ◽  
The Cost

The cost of development of WPS will be very expensive if the welding parameter is selected based on trial and error. Optimal welding condition cannot be easily guessed unless the operator has records of good welding. If a calculator that can predict the welding parameter for the desired bead geometry accurately, such tool will be extremely useful for any fabrication industry. This paper intends to investigate the correlation between the welding parameter and weld bead geometry of 2F position T-fillet carbon steel, when welded by 1.2 mm diameter wire submerged arc welding. Keeping only one parameter as variable, 2F fillet weld coupons are welded by SAW with a range of welding current, welding voltage and welding speed. Only weld bead geometry that complied with the quality requirement of code of practice AWS D1.1 is considered. The trendline graph is created to fit the correlation between the heat input and the fillet weld geometry. By incorporating the trendline formulas into the calculator, the weld bead geometry can be predicted accurately for any welding parameter. The mean absolute deviation (MAD) between the predicted geometry and the experimental results is less than 0.50mm.

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