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We present a highly miniaturized endoscopic point distance sensor based on a spatial confocal measurement principle. The sensor uses a new technique called spatial confocal point distance measurement. A special feature of the proposed sensor design is the high degree of miniaturization through femtosecond direct laser writing and the use of optical fiber bundles, which enable an endoscopic application. We show the complete sensor measurement principle, sensor head design, experimental setup, and experimental results.
Increasing miniaturization requires improved and highly miniaturized optical 3D metrology systems. In this paper a basic measurement principle and a proposed optical design of a highly miniaturized endoscopic spatial confocal point distance sensor are presented. The sensor uses a, to our knowledge new technique called spatial confocal point distance measurement. A special feature of the proposed sensor design is the high degree of miniaturization, through femtosecond direct laser writing and the use of optical fiber bundles, which enable an endoscopic application.
Punching is a process that is sensitive to a multitude of parameters. The estimation of part and punch quality is often based on expert knowledge and trial-and-error methods, mostly carried out as a separate offline process analysis. In a previous study, we developed an optical inline monitoring system with subsequent image processing which showed promising results in terms of capturing every manufactured part, but was limited by slow image processing. Here, we present a more efficient image processing technique based on neural networks. For our approach, we manually identify the burnish parts in images based on criteria established via an expert survey in order to generate a training dataset. We then employ a combination of region-based and boundary-based losses to optimize the neural network towards a segmentation of the burnish surface which allows for an accurate measurement of the burnish height. The hyperparameter optimization is based on custom evaluation metrics that reflect the requirements of the burnish surface identification problem as well. After comparing different neural network architectures, we focus on optimizing the backbone of the UNet++ structure for our task. The promising results demonstrate that neural networks are indeed capable of an inline segmentation that can be used for measuring the burnish surface of punching parts.
Punching is a wide-spread production process, applied when massive amounts of the ever-same cheap parts are needed. The punching process is sensitive to a multitude of parameters. Unfortunately, the precise dependencies are often unknown. A prerequisite for optimal, reproducible and transparent process alignment is the knowledge of how exactly parameters influence the quality of a punching part, which in turn requires a quantitative description of the quality of a part. We developed an optical inline monitoring system, which consists of a combined imaging and triangulation sensor as well as subsequent image processing. We show that it is possible to capture images of the cutting surface for every part within production. We automatically derive quality parameters using the example of the burnish height from 2D images. In addition, the 3D parameters are calculated and verified from the triangulation images. As an application, we show that the status of tool wear can be inferred by monitoring the burnish height, with immediate consequences for predictive maintenance. Although limited by slow images processing in our prototype, we conclude that connecting machine and process parameters with quality metrics in real time for every single part enables data-driven process modelling and ultimately the implementation of intelligent punching machines.