Hybrid Process Prognosis for Metal Ultrasonic Welding - Pro²MUSS

在项目“金属超声波焊接的混合工艺预测”- Pro²MUSS中，RWTH亚琛大学焊接和连接技术研究所（ISF）和机械理论研究所，亚琛工业大学（IGMR）机器动力学与机器人共同开发了一种新的参数化模型，用于接头形成，以便更好地理解超声波金属焊接的工艺。超声波金属焊接特别适用于连接电子技术部件，并且由于电子系统的日益复杂而日益成为工业关注的焦点。尽管其在工业中广泛使用，但在金属超声焊接中可能发生工艺波动。这些波动通常无法解释，因为缺乏关于焊接过程中工具和连接部件之间复杂相互作用的科学知识，因此，在大多数经验研究中几乎没有考虑到这些波动。在联合研究项目的框架内（AiF IGF项目20.161）和ISF的基本调查（DFG项目号395129909 / GZ RE 2755/52-1），已经表明，关于在接合区域内发生的热机械过程的信息可以从整个机械系统的外部可测量的振动行为获得，由焊接工具和连接部件组成。整个系统的振动特性与接缝的形成相关。两个项目合作伙伴利用这一基础创建了接缝形成的参数化模型。为此，项目合作伙伴创建了两个子模型。通过对过程过程进行理论-确定性建模，将根据过程边界条件（如几何形状、焊接参数和材料特性）确定过程的基本关键参数（如焊接持续时间、性能过程，特别是过程阶段的特性）。因此，该子模型的目的是根据所述参数预测过程的典型过程。该模型可以理解为过程的宏观视图。第二，基于机器学习（ML）的建模旨在基于过程参数的测量提供关于单个焊缝的实际实现的接头质量的陈述。建模代表微观接头质量。通过连接这两个模型，将关节形成和过程阶段的各个机制分配给ML的组件，并尽可能以参数化的形式重写。整个模型描述了从参数到工艺顺序到可实现或实际实现的接头质量的焊接过程。

In the project "Hybrid Process Prognosis for Metal Ultrasonic Welding" - Pro²MUSS, the Institute of Welding and Joining Technology at RWTH Aachen University (ISF) and the Institute of Mechanism Theory, Machine Dynamics and Robotics at RWTH Aachen University (IGMR) are jointly developing a new parameterized model for joint formation for a better process understanding in ultrasonic metal welding.Ultrasonic metal welding is particularly suitable for joining electro technical components and is increasingly coming into industrial focus due to the increasing complexity of electronic systems. Despite its widespread use in industry, process fluctuations can occur in metal-ultrasonic welding. These fluctuations often cannot be explained, since there is a lack of scientifically sound knowledge about the complex interactions between tools and joining parts during the welding process and, as a result, they are hardly taken into account in the mostly empirical research. Within the framework of joint research projects (AiF IGF project 20.161) and basic investigations by the ISF (DFG project number 395129909 / GZ RE 2755/52-1), it has already been shown that information on the thermomechanical processes occurring within the joining zone can be obtained from the externally measurable vibration behaviour of the overall mechanical system, consisting of welding tools and joining parts. The vibration behaviour of the overall system correlates with the joint formation.The two project partners use this basis to create a parameterized model of joint formation. For this purpose, the project partners create two submodels. With a statistical-deterministic modelling of the process course, essential key figures of the process, such as welding duration, performance course and in particular the characteristics of the process phases are to be determined on the basis of process boundary conditions such as geometry, welding parameters and material properties. The aim of this sub-model is thus to predict a typical course of the process on the basis of the parameters mentioned. This model can be understood as a macroscopic view of the process. A second, machine learning (ML) based modelling is intended to provide a statement on the actually achieved joint quality of the individual weld based on measurements of process parameters. The modelling represents the microscopic joint quality. By linking both models, individual mechanisms of joint formation and process phases are to be assigned to components of the ML and, as far as possible, rewritten in a parameterized form. The overall model depicts the welding process from the parameters through the process sequence to the achievable or actually achieved joint quality.