Precision Assessment of Tactile On-Machine Inspection for Milling Operations

The manufacturing industry faces customer demands for increased product quality and individuality to be economically successful. Established processes on the shop floor cannot overcome resulting challenges. Due to the increased quality requirements, potentially more products must be checked regarding their requirement fulfilment. In addition, customer individuality increases the number and rate of product releases. During product releases, the quality of the product is checked. Coordinate measuring machines are usually used for the quality assessment of milling processes. However, these are only suitable in the area of high quantities per batch due to downtimes of the milling machine while assessing product quality. On-machine inspection systems show particular strengths when a high proportion of the manufactured products have to be inspected and potentially reworked. These systems are criticized for their poor accuracy compared to coordinate measuring machines. This paper demonstrates the accuracy and repeatability of a tactile system in a field test at a tool manufacturer. Based on the test results, the tactile on-machine inspection system is compared with conventional coordinate measuring machines. Finally, the application area and its limits are identified for tactile on-machine inspection systems

ASAP3\mathrm{ASAP^3}: Neue Datennahme und Datenanalyse für PETRAIII

Erfahrungen mit Elastic Storage/Elastic Storage Server für die Datenaufnahme und Datenanalyse in der Photon Science

Application Of Ki-67 Analysis In A Distributed Computing Infrastructure

Introduction/ Background Over the last few years, the protein Ki-67 [1] has been established as one of the most important biomarkers for cell proliferation in breast cancer. High Ki-67 values indicate high tumor growth and have direct impact on the patient’s treatment. Several automated image anal- ysis methods for identifying Ki-67-positive and negative tumor cells have been presented. Aims For small regions of a virtual slide, the Ki-67 analysis can be realized within an acceptable period of time. However, to analyse an entire whole slide image (WSI [2])most of the current methods are not sufficient yet. On a typical office computer, the processing time of 3,752 tiles, which were extracted from a H–DAB stained WSI, exceeded 24 hours. Therefore, we propose an approach to significantly speed up the process of analysing entire WSIs by using a distributed computing infrastructure. Methods To evaluate the approach, an unmodified and validated [3] [4] analysis software for Ki-67 was deployed on a six node setup supporting two different software engines: Hadoop Streaming [5] and Apache Spark [6] . Both tools support the MapReduce methodology whereas Apache Spark offers alternative programing models. In addition, heat maps visualizing the Ki-67 scores for an entire slide were generated which can provide additional informa- tion for clinical research. Results First results on automated and reproducible tests have been produced. By processing 3,752 tiles the speedup turned out to increase linearly with the number of tiles. The overall processing time was improved by a factor of 10, more precisely from 28 hours on a typical office computer to three hours on a distributed environment. Further optimization strategies besides WSI partitioning will be considered. To achieve additional improvements in processing speed, the underlying algorithm of a Ki-67 analysis can be examined with focus on how to adapt it towards distributed processing workflows

ASAP3\mathrm{ASAP^3}: New data taking and analysis infrastructure for PETRA III

PETRA III is DESY's largest ring accelerator and the most brilliant storage-ring-based X-ray radiation source in the world. With its recent extension, new and faster detectors are used for the data acquisition. They exceed previous detectors in terms of data rate and volume; this is highly demanding for the underlying storage system. This talk will present the challenges we faced, the new infrastructure and services based on IBM's GPFS and our first experiences with it