No abstract

Decreasing batch sizes lead to an increasing demand for flexible automation systems in manufacturing industries. Robot cells are one solution for automating manufacturing tasks more flexibly. Besides the ongoing unifications in the hardware components, the controllers are still programmed application specifically and non-uniform. Only specialized experts can reconfigure and reprogram the controllers when process changes occur. To provide a more flexible control, this paper presents a new method for programming flexible skill-based controls for robot cells. In comparison to the common programming in logic controllers, operators independently adapt and expand the automated process sequence without modifying the controller code. For a high flexibility, the paper summarizes the software requirements in terms of an extensibility, flexible usability, configurability, and reusability of the control. Therefore, the skill-based control introduces a modularization of the assets in the control and parameterizable skills as abstract template class methodically. An orchestration system is used to call the skills with the corresponding parameter set and combine them into automated process sequences. A mobile flexible robot cell is used for the validation of the skill-based control architecture. Finally, the main benefits and limitations of the concept are discussed and future challenges of flexible skill-based controls for robot cells are provided.

This paper presents a brief introduction to competition-driven digital transformation in the machining sector. On this basis, the creation of a digital twin for machining processes is approached firstly using a basic digital twin structure. The latter is sub-grouped into information and data models, specific calculation and process models, all seen from an application-oriented perspective. Moreover, digital shadow and digital twin are embedded in this framework, being discussed in the context of a state-of-the-art literature review. The main part of this paper addresses models for machine and path inaccuracies, material removal and tool engagement, cutting force, process stability, thermal behavior, workpiece and surface properties. Furthermore, these models are superimposed towards an integral digital twin. In addition, the overall context is expanded towards an integral software architecture of a digital twin providing information system. The information system, in turn, ties in with existing forward-oriented planning from operational practice, leading to a significant expansion of the initially presented basic structure for a digital twin. Consequently, a time-stratified data layer platform is introduced to prepare for the resulting shadow-twin transformation loop. Finally, subtasks are defined to assure functional interfaces, model integrability and feedback measures.

Additive manufacturing (AM), often referred to as 3D printing, is a generic term describing the layered build-up of material in near net shape frequently attributed with a freedom of design that cannot be achieved otherwise. AM focuses basically on the fabrication of parts for different fields in complex high-tech applications. Examples include components for jet engines, turbines blades, and implants in the medical sector. This is often justified with tool cost savings, shorter lead-time, and overcoming the “design for manufacture” paradigm. On the other hand, a machining allowance is frequently required to counteract the inherent surface roughness and the widespread challenge of part distortion due to residual stresses. At this point, geometrical complexity and small batch sizes transform into strong cost drivers compared to conventional subtractive processing. In fact, these parts are simply hard-to-clamp and hard-to-probe. Moreover, iterative processing is frequently required due to remaining residual stresses in order to reach the target geometry; even the part envelope changes unintentionally. The current paper explores the novel approach of semiautonomous postprocessing of AM parts and components based on flexible clamping, geometry acquisition in the as-clamped position using cooperating laser profile sensors, and an adaptive milling path planning strategy to counteract unforeseen change of the part envelope.

During machining occurring losses conduct into the machine tool and lead to deformations that im-pair the machining accuracy. Thus, high effort is invested into the compensation of thermo-elastic er-rors. One new approach is to use the information extractable from part programs in combination with CNC controller systems to forecast occurring losses in terms of a look ahead function. The method al-lows the evaluation of part programs with respect to its thermal influence on the machine tool. There-fore, the method is applied on selected part programs, resulting loss distributions are discussed in terms of their thermal impact, and potential follow-up strategies are stated.