e-journal

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

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Human welder’s 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 welder’s 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 welder’s intelligence into robotic welding systems.
Note to Practitioners—Welding is the final stage for manufacturing of many high value added products. While industrial welding robots have been in use for several decades, they are preprogrammed actuators with limited, if any, intelligence. Given that manufacturing is moving towards more customized production,
next generation welding robots that can intelligently adjust to various welding tasks are urgently needed. Unfortunately, equipping robots with intelligence is challenging. This paper establishes
a method to rapidly transform human welder intelligence into welding robots by using 3D weld pool surface as information source, fitting human welder response to the information by a neuro-fuzzy model, and using the neuro-fuzzy model as a replacement of human welder intelligence in automated GTAW systems.
A foundation is thus established to mechanize the human welder’s intelligence and develop next generation welding robots.
Index Terms—Adaptive neuro-fuzzy inference system (ANFIS), human welder’s behavior, manual gas tungsten arc welding (GTAW), neuro-fuzzy modeling, weld pool geometry, 3D.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING

No. Panggil

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Penerbit

New York : IEEE., 2015

Deskripsi Fisik

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, VOL. 12, NO. 1, JANUARY 2015

Bahasa

English

ISBN/ISSN

1545-5955

Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

VOL. 12, NO. 1, JANUARY 2015

Subjek

TEKNIK

Info Detail Spesifik

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Pernyataan Tanggungjawab

Yuli/Agus

Versi lain/terkait

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