Autor: Daniel Weimer

In the last few years, quantum computing has achieved new successes, such as Google’s quantum supremacy experiment [1], and has been showing adoption by large industrial firms to tackle complex problems. But what has led up to these developments? What kinds of problems can we expect to be able to solve in the near term with quantum computing? What are the challenges that we encounter with this technology and deploying within industrial settings?

Customers in online shops return at least half of the placed orders. This huge amount of return shipments results in high costs e.g. from a logistic point of view. To predict the return rate based on customer data and order information, machine learning techniques can be applied which are able to learn a powerful model for return prediction based on historical order data. This article introduces a hands-on approach for successfully applying machine learning in real world processes and shows a case study to predict the return shipment probability in an e-commerce scenario.

The major components of a machine vision system are image acquisition and image processing. The complexity of image acquisition is based on the huge number of degrees of freedom. This article introduced an extension of a sensor simulation tool which enables a user to simulate the current sensor positions and field of views. The results are registered point clouds from different sensor positions merged to a final object point cloud. Now it is possible to evaluate image processing techniques based on the simulated 3D data, without explicit results from the real sensor behaviour. The benefits of the tool were demonstrated in a real micro cold forming scenario. Future work focuses on additional sensor noise models. Adding these models results in a more realistic sensor simulation framework.

Automation is an interdisciplinary technology which has a significant impact on the industry in Germany. Especially in micro-production as a niche market, there are numerous applications that require highly automated production technologies. The biggest challenge is the control of size effects. Size effects occur in the production of micro components because the ratio of volume to surface forces shifts and adhesion appears. Furthermore, micro forming has batches with high volume, which require high flexible and ideally universal automated handling systems for efficient multi-stage manufacturing processes. This paper deals with the adhesion forces and their reduction by minimizing the contact surface. In addition, requirements and approaches to automated allocation of micro-components are discussed.

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.