WeiJie Zhang

Dynamic Neuro-Fuzzy-Based Human Intelligence Modeling and Control in GTAW

Human welders experiences and skills are critical for producing quality welds in manual gas tungsten arc welding (GTAW) process. In this paper, a neuro-fuzzy-based human intelligence model is constructed and implemented as an intelligent controller in automated GTAW process to maintain a consistent desired full penetration. An innovative vision system is utilized to real-time measure the specular 3D weld pool surface under strong arc light interference. Experiments are designed to produce random changes in the welding speed and voltage resulting in fluctuations in the weld pool surface. Adaptive neuro-fuzzy inference system (ANFIS) is proposed to correlate the human welders response to the 3D weld pool surface as characterized by its width, length and convexity. Closed-loop control experiments are conducted to verify the robustness of the proposed controller. It is found that the human intelligence model can adjust the current to robustly control the process to a desired penetration state despite different initial conditions and various disturbances. A foundation is thus established to explore the mechanism and transformation of human welders intelligence into robotic welding systems.

Analytical real-time measurement of a three-dimensional weld pool surface

The ability to observe and measure weld pool surfaces in real-time is the core of the foundation for next generation intelligent welding that can partially imitate skilled welders who observe the weld pool to acquire information on the welding process. This study aims at the real-time measurement of the specular three-dimensional (3D) weld pool surface under a strong arc in gas tungsten arc welding (GTAW). An innovative vision system is utilized in this study to project a dot-matrix laser pattern on the specular weld pool surface. Its reflection from the surface is intercepted at a distance from the arc by a diffuse plane. The intercepted laser dots illuminate this plane producing an image showing the reflection pattern. The deformation of this reflection pattern from the projected pattern (e.g. the dot matrix) is used to derive the 3D shape of the reflection surface, i.e., the weld pool surface. Based on careful analysis, the underlying reconstruction problem is formulated mathematically. An analytic solution is proposed to solve this formulated problem resulting in the weld pool surface being reconstructed on average in 3.04 ms during welding experiments. A vision-based monitoring system is thus established to measure the weld pool surface in GTAW in real-time. In order to verify the effectiveness of the proposed reconstruction algorithm, first numerical simulation is conducted. The proposed algorithm is then tested on a spherical convex mirror with a priori knowledge of its geometry. The detailed analysis of the measurement error validates the accuracy of the proposed algorithm. Results from the real-time experiments verify the robustness of the proposed reconstruction algorithm.

Real-time measurement of the weld pool surface in GTAW process

This study aims at real-time measurement /reconstruction of the specular three-dimensional (3D) weld pool surface under strong arc in gas tungsten arc welding (GTAW). A vision-based sensing system is used to project a dot-matrix laser pattern on the weld pool surface which is able to specularly reflect the laser pattern. This reflection pattern, deformed by the weld pool surface has been utilized to reconstruct the 3D shape of the weld pool surface. Based on careful analysis, the underlying reconstruction problem is formulated mathematically. An analytic algorithm is proposed resulting in the weld pool surface be reconstructed in real-time on average in 3.22 milliseconds. In order to verify the effectiveness of the proposed reconstruction algorithm, it is tested on objects with a priori knowledge of geometry having a specular surface. Then the robustness of the proposed reconstruction scheme is further validated using a real-time experiment.The detailed analysis of measurement error validates the accuracy of the proposed algorithm.

Neuro-fuzzy based human intelligence modeling and robust control in Gas Tungsten Arc Welding process

Human welders experiences and skills are critical for producing quality welds in Gas Tungsten Arc Welding (GTAW) process. Modeling of the human welders response to 3D weld pool surface can help develop next generation intelligent welding machines and train welders faster. In this paper, a neuro-fuzzy based human intelligence model is constructed and implemented as an intelligent controller in automated GTAW process to maintain a consistent desired full penetration. An innovative vision system is utilized to real-time measure the specular 3D weld pool surface under strong arc interference in GTAW process. Experiments are designed to produce random changes in the welding speed and voltage resulting in fluctuations in the weld pool surface. Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed to correlate the human welders response to the 3D weld pool surface. Control experiments are designed to start welding using different initial current and have various disturbances including variations of arc length and welding speed. It is found that the proposed human intelligence model can adjust the current to robustly control the process to a desired penetration state despite different initial conditions and various disturbances. A foundation is thus established to explore the mechanism and transformation of human welders intelligence into robotic welding system.

Data driven modeling of human welder intelligence: A neuro-fuzzy approach

Modeling of skilled human welders response to 3D weld pool surface can help develop next generation intelligent robotic welding systems and train welders faster. In this paper, neuro-fuzzy based data driven modeling of human welder intelligence is conducted. An innovative vision system is utilized to real-time measure the specular 3D weld pool surface under strong arc interference. Experiments are designed to produce random changes in the welding speed and voltage resulting in fluctuations in the weld pool surface. Adaptive Neuro-Fuzzy Inference System is proposed to correlate skilled human welder response to the fluctuating 3D weld pool surface. It is found that the proposed neuro-fuzzy model can model the human welder intelligence with good accuracy. Comparison of the novice and skilled human welder also reveals detailed adjustments made by the skilled human welder and help train the novice welder. A foundation is thus established to explore the mechanism and transformation of human welders intelligence into robotic welding system.

Dynamic neuro-fuzzy estimation of the weld penetration in GTAW process

The weld pool contains abundant information about the welding process and can thus be utilized to accurately monitor the weld penetration. This paper addresses the dynamic estimation of the weld penetration in GTAW process. A machine vision based weld pool sensing system is utilized and the 3D weld pool surface is reconstructed in real-time. Various dynamic experiments under different welding conditions are conducted to acquire data pairs for establishing the correlation between the front-side weld pool characteristic parameters and the weld penetration specified by its back-side bead width. Due to the substantial inertia of the welding process, the weld penetration can be more accurately estimated if the adjacent weld pools are used. Hence, a nonlinear dynamic Adaptive Neuro-Fuzzy Inference System (ANFIS) model is developed to estimate the weld penetration in real-time. It is found that the weld penetration can be estimated with satisfactory accuracy by the proposed online monitoring system.

Estimation of weld penetration using parameterized three-dimensional weld pool surface in gas tungsten arc welding

A skilled welder determines the weld joint penetration from his/her observation on the weld pool surface during the welding process. This paper addresses the estimation of weld joint penetration (i.e. determining back-side bead width that measures the degree of the weld joint penetration in full penetration welding) using the parameterized 3D weld pool surface in gas tungsten arc welding (GTAW). To this end, an innovative machine vision system is used to measure the specular weld pool surface in real-time. Various experiments under different welding conditions have been performed to produce full penetration welds with different back-side bead widths and acquire corresponding images for reconstructing the weld pool surface and calculating candidate estimation parameters. Through the least squares algorithm based statistic analyses, it was found that the width, length, and convexity of the 3D weld pool surface provides the optimal model to predict the back-side bead width with an acceptable accuracy. A foundation is thus established to effectively extract information from the weld pool surface to facilitate a feedback control of the weld joint penetration.

Neuro-fuzzy modeling of human welder's response to 3D weld pool surface in GTAW

Understanding and modeling of welder response to 3D weld pool surface help develop intelligent welding robotic systems and better train welders. In this paper, a welders adjustment on the welding current as a response to the 3D weld pool surface as characterized by its width, length, and convexity is studied. A vision sensing system is developed to real-time measure the specular 3D weld pool in gas tungsten arc welding (GTAW). Experiments are designed to produce random changes in the welding speed resulting in fluctuating 3D weld pool surface. Adaptive Neuro Fuzzy Inference System (ANFIS) model is developed for the human welder response in order to correlate the current adjustment to the 3D weld pool surface. To justify the use of the neuro-fuzzy model, linear model has also been fitted and compared. It is found that the proposed ANFIS modeling can derive detailed correlation between the human welders responses and the weld pool geometry and help better understand the nonlinear response of the human welder to 3D weld pool surfaces.