Cold metal transfer (CMT) welding is a sophisticated version of fusion welding process available with advanced features incorporated in it. During the process of joining metallic parts with CMT, the bead shape and size have significant effects on the weld quality. Typically, bead geometry is characterized by the three important parameters namely weld width, weld depth and reinforcement height. For rapid and intelligent welding, it is imperative to monitor the bead shape and size during the process. However, the measurement system of three parameters is often time consuming and complex. In this paper, an attempt is made to reduce the computational time of artificial intelligence models that are used for intelligent welding processes. Bead coefficient is modelled as an alternative for bead geometry properties. Computationally efficient artificial neural network models are developed for examining the feasibility of bead coefficient over bead geometry properties with and without online-temperature measurements. The predicted results of both forward and reverse neural network models are in good coherence with the experimental values. The outcome of this study is expected to provide a detailed understanding of effects of process parameters on bead geometry and bead coefficient, which facilitates online monitoring of welding processes.

冷金属转移（CMT）焊接是融合焊接工艺的高级版本，具有其先进的功能。在用CMT连接金属零件的过程中，焊缝的形状和大小对焊接质量有重要影响。通常，焊缝几何形状的特征在于三个重要参数，即焊接宽度，焊接深度和增强高度。为了实现快速智能的焊接，必须在加工过程中监控焊道形状和大小。然而，三个参数的测量系统通常是耗时且复杂的。在本文中，尝试减少用于智能焊接过程的人工智能模型的计算时间。珠子系数被建模为珠子几何特性的替代方法。开发了计算有效的人工神经网络模型，以检查在有和没有在线温度测量的情况下，磁珠系数相对于磁珠几何形状特性的可行性。正向和反向神经网络模型的预测结果与实验值具有良好的一致性。预期这项研究的结果将提供对工艺参数对焊缝几何形状和焊缝系数影响的详细理解，从而有助于在线监测焊接工艺。