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Gearboxes are mechanical transmission systems that provide speed and torque conversions from a rotating power source. Being a central element of the drive train, they are relevant for the efficiency and durability of motor vehicles. In this work, we present a new approach for gearbox design: Modeling the design problem as a mixed-integer nonlinear program (MINLP) allows us to create gearbox designs from scratch for arbitrary requirements and—given enough time—to compute provably globally optimal designs for a given objective. We show how different degrees of freedom influence the runtime and present an exemplary solution.
This chapter introduces performance and acceptance testing and describes state-of-the-art tools, methods, and instruments to assess the plant performance or realize plant acceptance testing. The status of the development of standards for performance assessment is given.
The coupling of charged molecules, nanoparticles, and more generally, inorganic/organic nanohybrids with semiconductor field-effect devices based on an electrolyte–insulator–semiconductor (EIS) system represents a very promising strategy for the active tuning of electrochemical properties of these devices and, thus, opening new opportunities for label-free biosensing by the intrinsic charge of molecules. The simplest field-effect sensor is a capacitive EIS sensor, which represents a (bio-)chemically sensitive capacitor. In this chapter, selected examples of recent developments in the field of label-free biosensing using nanomaterial-modified capacitive EIS sensors are summarized. In the first part, we present applications of EIS sensors modified with negatively charged gold nanoparticles for the label-free electrostatic detection of positively charged small proteins and macromolecules, for monitoring the layer-by-layer formation of oppositely charged polyelectrolyte (PE) multilayers as well as for the development of an enzyme-based biomolecular logic gate. In the second part, examples of a label-free detection by means of EIS sensors modified with a positively charged weak PE layer are demonstrated. These include electrical detection of on-chip and in-solution hybridized DNA (deoxyribonucleic acid) as well as an EIS sensor with pH-responsive weak PE/enzyme multilayers for enhanced field-effect biosensing.
Cyber-physical systems are ever more common in manufacturing industries. Increasing their autonomy has been declared an explicit goal, for example, as part of the Industry 4.0 vision. To achieve this system intelligence, principled and software-driven methods are required to analyze sensing data, make goal-directed decisions, and eventually execute and monitor chosen tasks. In this chapter, we present a number of knowledge-based approaches to these problems and case studies with in-depth evaluation results of several different implementations for groups of autonomous mobile robots performing in-house logistics in a smart factory. We focus on knowledge-based systems because besides providing expressive languages and capable reasoning techniques, they also allow for explaining how a particular sequence of actions came about, for example, in the case of a failure.
Engineers and therefore engineering education are challenged by the increasing complexity of questions to be answered globally. The education of future engineers therefore has to answer with curriculums that build up relevant skills. This chapter will give an example how to bring engineering and social responsibility successful together to build engineers of tomorrow. Through the integration of gender and diversity perspectives, engineering research and teaching is expanded with new perspectives and contents providing an important potential for innovation. Aiming on the enhancement of engineering education with distinctive competencies beyond technical expertise, the teaching approach introduced in the chapter represents key factors to ensure that coming generations of engineers will be able to meet the requirements and challenges a changing globalized world holds for them. The chapter will describe how this approach successfully has been implemented in the curriculum in engineering of a leading technical university in Germany.
The production and assembly of customized products increases the demand for flexible automation systems. One approach is to remove the safety fences that separate human and industrial robot to combine their skills. This collaboration possesses a certain risk for the human co-worker, leading to numerous safety concepts to protect him. The human needs to be monitored and tracked by a safety system using different sensors. The proposed system consists of a RGBD camera for surveillance of the common working area, an array of optical distance sensors to compensate shadowing effects of the RGBD camera and a laser range finder to detect the co-worker when approaching the work cell. The software for collision detection, path planning, robot control and predicting the behaviour of the co-worker is based on the Robot Operating System (ROS). A first prototype of the work cell shows that with advanced algorithms from the field of mobile robotics a very flexible safety concept can be realized: the robot not simply stops its movement when detecting a collision, but plans and executes an alternative path around the obstacle.