## 1 Motivation
The combination of optimization, operating-speed-dependent motion profiles, and the novel control system leads to a significant increase in the achievable operating speed. At the same time, varying the operating speed over a wide range is possible. A disadvantage is that using more than one single motion specification, as in conventional systems, leads to a significantly higher amount of data needed to describe a single process. This is particularly disadvantageous against the background of real-time processing, as many controllers do not have a large data memory. Using a very fast controller means that only a short computing time (125 \(\upmu\)s in our example) is available online. The online motion processing of the specifications must, therefore, be as efficient as possible. To achieve this, the approach is to carry out online calculations into the offline field wherever possible. Such calculations include, in particular, computationally complex operations like kinematic  
Figure 1: Example process. (**a**) Intermittent transport of pieced goods along a horizontal path with a servo-driven five-bar linkage. (**b**) Principles of a motion approach for the transport of pieced goods. (**c**) Characteristic map of operating speed-dependent motion profiles. Based on [2; 4]. transformations. Even though computers and control systems are becoming faster and faster, other aspects speak in favor of outsourcing the calculation effort to the offline preliminary field. On the one hand, there is an increasing trend to use small, inexpensive, networked controllers with low computing capacity (\"Industry 4.0\", \"Smart Manufacturing\"). On the other hand, the idea of relieving the controller and keeping resources free for other (new) functions underlines the relevance of the approach. At the same time, the demand for individualized products increases (e.g., customized products, often in the context of \"Industry 4.0\" or \"Smart Manufacturing\"). Individualized products, in turn, require individual motion specifications that must be prepared for the control approach. This increases the amount of work needed for the offline motion preparation. Figure 2 shows the integration of the motion preparation in the proposed workflow. In the first step, the motion specification is numerically calculated and optimized under consideration of operating-speed-dependent effects. Due to the optimization algorithm used, point lists are generated. The specifications are combined to a map of operating speed-dependent motion profiles. Subsequently, a map of splines is generated from these point lists during the motion preparation, which can be processed very efficiently later. This is done in the area of online motion processing. Previous work examined aspects of motion optimization and online motion processing, Figure 2, gray. The present work discusses offline preparation aspects. In order to reduce downtime on the machine, the motion specifications have to be prepared quickly. To be able to assess this, the work deals with the performance evaluation of different approaches for offline motion preparation known from literature. The paper is structured as follows: first, requirements on the results of the offline motion preparation and the performance evaluation are defined (Section 2). After evaluating conventional approaches for offline motion preparation, optimization potentials are deduced (Section 3). Furthermore, a very simple approach to increase the performance is discussed and evaluated (Section 4). Finally, an overview on the approaches is given (Section 5) followed by conclusions (Section 6).