Abstract

The longitudinal-torsional mode horn has gained popularity recently for ultrasonic welding (USW) because it is more efficient than a conventional longitudinal horn. Addition of slanting grooves to the front mass to achieve torsionality is not a novel approach, and few works have already addressed this issue, but comparative studies about the effect of different groove parameters such as length, depth, angle, width, and distance upon the torsionality and resonance frequency are very rare. In the present work, only one parameter was varied at a time while others were kept constant to see their effect on horn attributes, i.e., torsionality and resonance frequency. The torsionality was maximized while keeping the value of resonance frequency as close to working frequency (i.e., 20 kHz) as possible. Depth is considered to be the most important parameter since its effect on torsionality was higher than the other four parameters. Multi-layer perceptron neural network was trained using the input features (i.e., groove parameters) which has the potential of transfer learning and would ease the process of finding the optimum parameters for torsionality maximization in later projects.

中文翻译：

---

有限元分析耦合人工神经网络方法设计纵扭模超声焊头

摘要

纵向扭转模式号筒由于其比传统的纵向号筒更有效而最近在超声波焊接（USW）中获得了普及。在前块上增加倾斜的凹槽以达到扭转性能并不是一种新颖的方法，很少有工作解决此问题，但是对不同凹槽参数（例如长度，深度，角度，宽度和距离）对轮胎的影响进行了比较研究。扭转性和共振频率非常罕见。在目前的工作中，一次仅改变一个参数，而其他参数保持不变，以查看它们对号角属性（即扭转度和共振频率）的影响。在使共振频率的值尽可能接近工作频率（即20 kHz）的同时，使扭转度最大化。深度被认为是最重要的参数，因为它对扭转性的影响高于其他四个参数。多层感知器神经网络是使用输入特征（即凹槽参数）进行训练的，该输入特征具有传递学习的潜力，并将简化在以后的项目中为扭力最大化找到最佳参数的过程。