Laser microwelding has been an essential tool with a reputation of rapidity and precision for joining miniaturized metal parts. In industrial applications, an accurate prediction of weld bead geometry is required in automation systems to enhance productivity of laser microwelding. The present work was conducted to establish an intelligent algorithm to build a simplified relationship between process parameters and weld bead geometry that can be easily used to predict the weld bead geometry with a wide range of process parameters through an artificial neural network (ANN) in laser microwelding of thin steel sheet. The backpropagation with the Levenberg-Marquardt training algorithm was used to train the neural network model. The accuracy of neural network model has been tested by comparing the simulated data with actual data from the laser microwelding experiments. The predictions of the neural network model showed excellent agreement with the experimental results, indicating that the neural network model is a viable means for predicting weld bead geometry. Furthermore, a comparison was made between the neural network and mathematical model. It was found that the developed neural network model has better prediction capability compared to the regression analysis model. 1. Introduction Laser microwelding has a great potential in the joining production development. This microjoining technology is expected to realize the demand for high quality and faster joining method of thin metal sheets. The advantages of laser microwelding such as precision control of heat input, deep weld penetration, and minimal distortion offer higher welding speed compared to the conventional welding method. The low cost of production has made laser microwelding essential in various industries, including electronics, medical instruments and automotive industry. In the joining process of microproducts, the technological advancement in the field of monitoring and control is required. Therefore, the concentration must be given to the control of the factors which affect the laser microwelding process. Primarily, a proper model needs to be constructed and tested before implementing for online control. The requirement to predict weld bead geometry as a function of welding performance in the laser microwelding has become more important to provide a basis for a computer-based control system in the future. The process parameters determine the weld bead geometry, due to the combination of these parameters control the heat input [1]. The effect of process parameters on weld bead geometry can be
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