We describe two proof-of-concept approaches on the sonification of estimated operation states and conditions focusing on two scenarios: a laboratory setup of a manipulated 3D printer and an industrial setup focusing on the operations of a punching machine. The results of these studies form the basis for the development of an “intelligent” noise protection headphone as part of Cyber Physical Production Systems which provides auditorily augmented information to machine operators and enables radio communication between them. Further application areas are implementations in control rooms (equipped with multi-channel loudspeaker systems) and utilization for training purposes. As a first proof-of-concept, the data stream of error probability estimations regarding partly manipulated 3D printing processes were mapped to three sonification models, providing evidence about momentary operation states. The neural network applied indicates a high accuracy (> 93%) of the error estimation distinguishing between normal and manipulated operation states. None of the manipulated states could be identified by listening. An auditory augmentation, or sonification of these error estimations, provides a considerable benefit to process monitoring. For a second proof-of-concept, setup operations of a punching machine were recorded. Since all operations were apparently flawlessly executed, and there were no errors to be reported, we focused on the identification of operation phases. Each phase of a punching process could be algorithmically distinguished at an estimated probability rate of > 94%. In the auditory display, these phases were represented by different instrumentations of a musical piece in order to allow users to differentiate between operations auditorily.

我们描述了两种关于估计操作状态和条件的声波化的概念验证方法，重点是两个场景：操纵 3D 打印机的实验室设置和专注于冲压机操作的工业设置。这些研究的结果构成了开发“智能”噪声保护耳机的基础，作为网络物理生产系统的一部分，该系统为机器操作员提供听觉增强信息并实现他们之间的无线电通信。进一步的应用领域是在控制室（配备多声道扬声器系统）中的实施和用于培训目的。作为第一个概念验证，关于部分操纵的 3D 打印过程的错误概率估计的数据流被映射到三个超声模型，提供关于瞬时操作状态的证据。所应用的神经网络表明区分正常和操纵操作状态的误差估计具有高准确度 (> 93%)。通过聆听无法识别任何被操纵的状态。这些错误估计的听觉增强或声波化为过程监控提供了相当大的好处。对于第二个概念验证，记录了冲床的设置操作。由于所有操作显然都完美无缺地执行，并且没有错误报告，因此我们专注于操作阶段的识别。冲压过程的每个阶段都可以通过算法区分，估计概率 > 94%。在听觉显示中，