image processing

In factory operations, measuring equipment is often used without automatic storage or further processing possibilities of the measured value. In this case, employees must capture and process the measured values manually. In this article, an approach for the optical detection and digitization of measured values with the help of machine learning methods is presented. This aims to reduce the workload of the employees, avoid reading errors and enable automated documentation.

A complex process in sheet metal processing is multi stage deep drawing. Once set up, it can be considered as a black box. Usually, after the sheet metal has been processes the quality is assessed. In the research project Smart Press a system is developed, incorporating inline pictures of the processed sheet metal. Pictures of failures are related to the actual state of the machine. Neural Networks are used to model the highly complex relations between parameters and product attributes. Based on the assessed real time data, the process gets adjusted to suit the needs of each individual sheet.

Surface inspection and defect detection during manufacturing is a fundamental task for industrial image processing. Based on reliable and accurate defect detection systems changing process behaviour, like blunt tools, can be detected. In many areas like Si-wafer production, optical quality control is still state of the art. Nevertheless, in many cases a completely new and specialised image processing technique will be developed for every new problem. This contribution demonstrates a method for a general image processing technique for industrial surface inspection which is not limited to a specific hardware or defect type. The system is part of the Collaborative Research Center (CRC) 747 “Micro Cold Forming” and will be evaluated in a real cold forming process.

Due to the increasing globalization of commodity flow a growth of mass good transportation is perceptible. Therefore automatic unloading of goods and their automatic transfer to logistic systems is one of the technical challenges. Opportunities arising from automatic goods unloading are explored in [1]. A system for unloading cubic goods automatically was developed and its introduction to market started last year. Changing environments and various operational areas are the motivation for research on a cognitive system for unloading goods. Hence, a concept for a cognitive system for unloading containers is introduced in this article. The components for such a system are image processing, robot control handling system and kinematics. For each of these components cognitive methods and technologies are proposed to be integrated in an overall system.