Detmar Zimmer

On design for additive manufacturing: evaluating geometrical limitations

Purpose – The purpose of this paper is to present Design Rules for additive manufacturing and a method for their development. Design/methodology/approach – First, a process-independent method for the development of Design Rules was worked out. Therefore, geometrical standard elements and attributes that characterize the elements’ shapes have been defined. Next, the standard elements have been manufactured with different attribute values with Laser Sintering, Laser Melting and Fused Deposition Modeling, and their geometrical quality was examined. From the results, Design Rules for additive manufacturing were derived and summarized in a catalogue. Findings – Due to the process independent method, Design Rules were developed that apply for the different considered additive manufacturing technologies equally. These Design Rules are completely function-independent and easily transferable to individual part designs. Research limitations/implications – The developed Design Rules can only apply for the considered...

Hierarchical optimization of coupled self-optimizing systems

In this work we present an approach for the optimization of distributed test rigs that are coupled only by information processing. Our application examples are an active suspension system and a linear drive with an active air gap adjustment which both represent a module of the rail-bound vehicle RailCab. Hierarchical optimization is used to combine the module-related optimal operating strategies, which are based on two distinct multiobjective optimizations. A Pareto front of the entire system is presented as a result of the hierarchical optimization.

Hybrid Planning for Self-Optimization in Railbound Mechatronic Systems

Self-optimizing mechatronic systems with inherent partial intelligence are the research objective of the Collaborative Research Centre Self-optimizing concepts and structures in mechanical engineering (CRC 614, 2010). A mechatronic system is called a self-optimizing system in this context, if it is not only able to adapt the system behavior to reach a set of given objectives or goals but also can adapt the objectives themselfes (or their weighting) on the basis of an analysis of the actual situation. Hence, the self-optimization approach promises to leave degrees of freedom in choosing objectives for the system open until runtime. This means that the system can decide upon internal objectives based on external user input and current environmental conditions while the system is running. This is in contrast to a system where all internal objectives are set before the system is started. Having such fixed parameters leads to complicated and overly pessimistic approximations of the parameters that are needed to be set over the course of action that the system will take. Using self-optimization, leaving that decision open is the key idea of our approach. The external objectives like quality of control, comfort or total energy consumption along with constraints (e.g. maximal peak powers or average power consumption) are still embedded or entered into the system. But settings like distribution of energy usage among subsystems can be determined during runtime based on the actual system conditions. One application of this approach to mechatronic system design is a novel transportation system that is developed in close collaboration with CRC 614. The core of the system consists of railbound vehicles called RailCabs that enable groups of up to 12 passengers to travel directly without intermediate stops. A test track in a scale of 1:2.5 and two RailCabs in the same scale have been built at the University of Paderborn under the name Neue Bahntechnik Paderborn (NBP; see Figure 1). The RailCab is equipped with a doubly-fed linear motor as well as several innovative subsystems that feature inherent intelligence. Among these subsystems are an Air Gap Adjustment System (AGAS) and an active suspension system described more closely below (see Section 3). Operating parameters of these (sub)systems need to be adapted depending on environmental conditions and properties of the track sections that are being travelled. Hybrid Planning for Self-Optimization in Railbound Mechatronic Systems 10

Using Active Patterns for the Conceptual Design of Self-Optimizing Systems Exemplified by an Air Gap Adjustment System

The conceivable evolution of information technology will enable mechatronic systems with inherent partial intelligence. We call this kind of systems self-optimizing. Self-optimizing systems are able to react autonomously and flexible to changing environmental conditions. They are capable of learning and optimizing their behavior at run-time. This paper presents the paradigm of self-optimization and shows how to develop self-optimizing systems. Focus is the conceptual design phase. The result of this phase is the principle solution. In this paper at first we explain how to describe the principle solution in a domain-spanning way. This is of high importance because a holistic description of the principle solution constitutes the basis for the communication and cooperation between the engineers from different domains who are engaged in developing a self-optimizing system. At second we introduce solution patterns for developing principle solutions. Solution patterns enable the reuse of design knowledge so that the development process will be more effective. This paper focuses on solution patterns including self-optimizing solutions. How the methods work we explain by the complex magnetic linear drive of a shuttle.Copyright

Funktionsintegration additiv gefertigter Dämpfungsstrukturen bei Biegeschwingungen

Schwingungen und Vibrationen sind in Technik und Alltag haufig anzutreffen. Meist sind sie unerwunscht und mussen durch Dampfung reduziert werden. Hierzu werden aktuell haufig zusatzlich zu montierende Dampfungselemente eingesetzt. Diese sind durch zusatzlichen Montageaufwand und erhohte Kosten gekennzeichnet. Durch die zusatzliche Masse wird Leichtbauansatzen widersprochen.

Intelligent operating strategy for an internal rubber mixer's Multi-Motor Drive System based on Artificial Neural Network

Multi-Motor Drive Systems (MMDS) consist of multiple motors that act together to fulfill one drive task. They inherently support modular product concepts and offer additional degrees of freedom towards conventional Single-Motor Drive Systems (SMDS) because of their mechanical structure. In order to fully utilize the benefits, resulting from these degrees of freedom, an intelligent operating strategy (IOS) is necessary. This paper presents an approach for an IOS which maximizes the energy efficiency of a MMDS. Optimal torque distributions among the motors and optimal switching states of the MMDS are identified by using optimization techniques. Additionally, machine learning techniques are used in order to establish an autonomously self-learning drive system. Using a MMDS test rig to simulate a production process of an internal rubber mixer as an application example, it is shown, that the proposed IOS increases the energy efficiency of the drive system.

Introduction to Self-optimization and Dependability

This chapter gives an introduction to self-optimizing mechatronic systems and the risks and possibilities that arise with these. Self-optimizing mechatronic systems have capabilities that go far beyond those of traditional mechatronic systems. They are able to autonomously adapt their behavior and so react to outer influences, which can originate e.g. from the environment, changed user requirements or the current system status. The basic process of self-optimization, the procedures employed within and the main components of a self-optimizing system are explained here.

Examples of Self-optimizing Systems

In this chapter, the benefits resulting from self-optimization will be described based on application examples from the Collaborative Research Center 614 “Self-optimizing Concepts and Structures in Mechanical Engineering”. First, the autonomous rail vehicle RailCab developed at the University of Paderborn is introduced. Then, the RailCab subsystems Self-Optimizing Operating Point Control, Intelligent Drive Module, Active Suspension Module, Active Guidance Module and Hybrid Energy Storage System and their test rigs are described in detail as well as an overall approach for Energy Management. The chapter concludes with the presentation of other development platforms such as the BeBot, an intelligent miniature robot acting optimally in groups, and the X-by-wire vehicle Chameleon with independent single-wheel chassis actuators. All the above mentioned demonstrators are used to validate the methods and procedures developed in the Collaborative Research Center. The experiences gained, provide direct input into further development and optimization of the design as well as the self-optimization process.