A fuzzy-regression-PSO based hybrid method for selecting welding conditions in robotic gas metal arc welding

Abstract

Purpose This paper aims to propose fuzzy-regression-particle swarm optimization (PSO) based hybrid optimization approach for getting maximum weld quality in terms of weld strength and bead depth of penetration. Design/methodology/approach The prediction of welding quality to achieve best of it is not possible by any single optimization technique. Therefore, fuzzy technique has been applied to predict the weld quality in terms of weld strength and weld bead geometry in combination with a multi-performance characteristic index (MPCI). Then regression analysis has been applied to develop relation between the MPCI output value and the input welding process parameters. Finally, PSO method has been used to get the optimal welding condition by maximizing the MPCI value. Findings The predicted weld quality or the MPCI values in terms of combined weld strength and bead geometry has been found to be highly co-related with the weld process parameters. Therefore, it makes the process easy for setting of weld process parameters for achieving best weld quality, as there is no need to finding the relation for individual weld quality parameter and weld process parameters although they are co-related in a complicated manner. Originality/value In this paper, a new hybrid approach for predicting the weld quality in terms of both mechanical properties and weld geometry and optimizing the same has been proposed. As these parameters are highly correlated and dependent on the weld process parameters the proposed approach can effectively analyzing the ambiguity and significance of each process and performance parameter.

Application of fuzzy logic in multi-sensor-based health service robot for condition monitoring during pandemic situations

“…Then fuzzy values are formulated by performing fuzzy reasoning with the fuzzy rules operated by the inference engine. Then the crisp output values from the fuzzy values have been obtained from defuzzification by the defuzzifier (Choi and Yoon, 2010b; Kahraman, 2006; Rout et al , 2020; Taylor et al , 2010).…”

Observer‐based event and self‐triggered adaptive output feedback control of robotic manipulators

“…The use of filtered virtue signal will bring complexity to the control system since the additional filtered error has been introduced into the evolution of state, which can be seen from (13). Borrowing from the ideal of the study in Reference 7, a self-adaption variable 𝜉 1 is designed to compensate for the filtered error as follows:…”

“…Next, the control signal u c will be designed for (13) to ensure closed-loop stability. Whereas, the second tracking error z 2 cannot be directly implemented since the velocity signal x 2 is hard to be acquired directly.…”

“…To solve the above problem, a model-estimated event-triggered output feedback control has been developed in this article, where a plant model is applied at the controller's side to approximate the evolution of feedback error state during the non-triggered period. In account of (13), the dynamics of model can be constructed in the following form:…”

“…the design of torque for trajectory tracking has been widely addressed. [3][4][5][6] With the diversification of manufacturing tasks, the advanced structure of manipulator also tends to be strongly nonlinear and integrated, and more advanced technologies, such as backstepping control, 5,7,8 sliding model control (SMC), 9,10 fuzzy control, [11][12][13] and so on need to be explored to achieve the expected performance. Among them, the backstepping method is taken as one of the widespread and valid control approaches to deal with nonlinear systems with a triangular structure or strict feedback form like the manipulator, and no matching condition is required.…”