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Self-Learning Fuzzy Neural Networks and Computer Vision for Control of Pulsed GTAW Neural network modeling and computer vision techniques are used to control the dynamics of the pulsed gas tungsten arc welding process BY S. B. CHEN, L. WU, Q. L. WANG AND Y. C. LIU ABSTRACT. The objective of this re- weld bead formation, and control of the of Mamdanl, et al. (Refs. 14-17), the search is to apply intelligent control size and changes in the weld pool, is still need for research to develop a fuzzy methodology to improve weld quality. a perplexing problem, whether for the logic controller that can learn from ex- Based on fuzzy logic and artificial neural control engineer or the welding technol- periences has been realized. The learn- network theory, a self-learning fuzzy and ogist (Refs. 1-7). In recent years, fuzzy ing task may include the identification of neural network control scheme has been logic controllers, which do not require the main control parameters or the de- developed for real-time control of pulsed analytical or accurate models of the con- velopment and tuning of the fuzzy mem- gas tungsten arc welding (GTAW). Using trolled process, have demonstrated a berships used in the control rules. an industrial TV camera as the sensor, the number of successful applications. Per- On the other hand, there has been an weld face width of the weld pool, i.e., the haps the most important application field increasing interest in studying artificial feedback signal in the closed loop sys- where fuzzy logic plays a significant role neural networks (ANN). The ANN is a tem, is obtained by computer image pro- is the control of complex industrial representation that attempts to mimic the cessing techniques. The computer vision processes. In general, these processes functionality of the brain. The present re- providing process status information in may be conventionally adjusted by a search on ANN shows that the method- real-time is an integral part of a self-learn- human operator due to their complexity. ology has been used on a more modest ing fuzzy neural control system. Such a Several industrial applications of fuzzy scale to develop nonlinear models and system enables adaptive altering of weld- logic control have been reported (Refs. controls. For example, recent studies ing parameters to compensate for chang- 8-13). These applications have mainly (Refs. 18-20) demonstrated the utility ing environments. The experiments on concentrated on simulating the perfor- and flexibility of the concept within the the control of the pulsed GTAW process mance of a skilled human operator in domain of process engineering. It has show that the scheme presented in this terms of linguistic rules. However, the been proven that any continuous func- paper can be used to control compli- process of learning and tuning linguistic tion can be approximated arbitrarily well cated variables such as encountered in rules to achieve the desired performance on a compact set by a feedforward artifi- welding processes. remains a difficult task. Starting with the cial neural network (Ref. 20). Generally, self-organizing control (SOC) techniques the ANN model and control system have Introduction the following characteristics: a strong ro- bustness, fault tolerance, universality, Owing to the uncertainties of phe- parallel distributed processing, and nomena such as metallurgy, heat transfer, learning and adaptive abilities. More- chemical reaction, arc physics and mag- KEY WORDS over, it is noted that Ref. 21 presented netization, the arc welding process is in- neural networks and fuzzy theory from a herently variable, nonlinear, time-vary- Self-Learning unified engineering perspective. The ing and strong coupling in its Fuzzy Logic combination of fuzzy logic and artificial input/output relationships. As a result, it Neural Networks neural networks oviously has great po- Intelligent Methodology is very difficult to obtain a practical and tential for various fields in industrial en- Real-Time Control controllable model of the arc welding gineering. Computer Vision process by classical modeling ap- The experiences of a skilled welder Topside Scanning proaches. Until now, control of weld could be summarized as a set of fuzzy Image Processing quality, such as weld joint penetration, logic control rules that are described in Weld Bead Width terms of IF (caused or conditions) THEN Pulsed GTAW S. B. CHEN, L. WU, Q. L. WANG and Y. C. (actions) rules. So we can develop a LIU are with Welding Division, Harbin Insti- fuzzy logic controller to imitate a skilled tute of Technology, Harbin, China. welder's operation of the welding WELDING RESEARCH SUPPLEMENT I 201-S ...................................... Fu~ ~o.t~ollcr ~ !fC ......................................... "'-. ""'.... i er I~K U~ U • 8(0 y(t) Fig. 1 -- Principle diagram for the self-learning fuzzy neural control of Fig. 2 -- Fuzzy controller architecture. the arc welding process. tem for detecting the weld face verting the crisp variables e, ec and erto width of pulsed GTAW in real- fuzzy variables E, EC, ER by the corre- time. Fuzzy and neural net- sponding quantizing factors Ke, Kcand Kr, work algorithms were used in respectively; the Fuzzy Calculator is used .... designing a controller with to complete fuzzy inference calculating; self-learning or adaptive regu- and the "Defuzzier" means converting O.8 lation of welding parameters, the fuzzy variable U to the crisp variable such as welding current and u by a quantizing factor K u. In our travel speed, to obtain the de- scheme, the Fuzzy Calculator in Fig. 2 is sired weld bead formation. described in terms of an analytical for- 0.4 The organization of this mula instead of common fuzzy rule ta- paper is as follows: the analyt- bles, that is 0.2 ical formulation of fuzzy logic control is introduced; a self- U(t) = -[a(t)b(t)E(t) + (1-a(t))b(t)EC(t) learning fuzzy neural control 0 , , , I , , , I , i , i I i , . • ' .... J + (1-b(t))ER(t)] (2.1) 0 5 10 15 20 25 scheme for the arc welding ~rne (~) process is presented and the where U, E, EC and ER denote the fuzzy corresponding algorithms of variables corresponding to their crisp the fuzzy controller and neural Fig. 3 -- Simulation of three-term and two-term con- variables; control action, u(t); control network model are developed; trollers. error, e(t) = D-y(t); change in error, ec(t) and experimental systems and -- e(t)-e(t-l); acceleration error, er(t) = the results of real-time control ec(t)-ec(t- l ); and a(t) and b(t) ~ [0,1] de- process. This paper will develop an ana- of the weld face width in pulsed GTAW note the tuning parameters between E, lytical formula for fuzzy control instead are shown. of using common fuzzy linguistic rules, EC and ER. The fuzzy variables and their corresponding crisp variables differ in and it will be shown how to combine the Self-Learning Fuzzy Neural ANN for modeling of the dynamic weld- the transformation factors relating to their Control Scheme for the ing process and fuzzy control to produce universes of discourse. Contrary to the a self-learning neural scheme for real- Arc Welding Process common approach, all universes of dis- time control of the pulsed gas tungsten course are considered continuously in A schematic of a self-learning fuzzy arc welding (GTAW) process. this study, i.e., the E, ECand ER are con- neural control system for the arc welding Another critical difficulty in the dy- tinuous value variables. process is shown in Fig. 1. Where the namic control of the arc welding process The analytical formula (2.1) can be fuzzy controller FC maps the regulated is how to detect reliable information explained as follows (Refs. 22-24): We about changes in the weld pool, such as error e(t) into control action u(t), the arc first take a two-term fuzzy controller as welding process is denoted by WP, the weld bead width and joint penetration, an example. Generally, fuzzy rules relate output signal y(t) of the process is de- especially viewed from the top of the three universes of discourse, such as weld pool and in real-time. Although var- tected by measurement parts MS, PMN error E, change in error EC and control denotes a neural network model of WP ious efforts have been made to sense the action U. We can describe these uni- and MS, D denotes the desired value. The weld pool size in real-time from the top verses as a fuzzy set that contains lin- outputs of WP and MS both are denoted with ultrasonic detection, infrared sens- guistic variables {PB, PM, PS, O, NS, NM, by y(t) in the sense of neglecting the dif- ing, pool image processing, and radi- NB}, where P, O and N are meant for pos- ference in their transformation coeffi- ographic sensing (Refs. 1-7), results for itive, zero and negative, and B, M and S cients. E(t) is the error between output of controlling weld quality have been met for big, medium and small. If we define PMN and MS. The structure and algo- with limited success. To achieve perfect these linguistic variables as follows: rithms of each component are explained control of weld quality, we should not below. only improve reliable measurement PB= 3, PM= 2, PS= 1,0=0, means, but also introduce advanced con- NS = -1, NM = -2, NB = -3 trol methodologies in real-time. Both The Fuzzy Controller should be given equal attention and de- then a simple fuzzy controller can be de- veloped simultaneously. In this study, a The fuzzy controller FC in Fig.

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