Bei den Verfahren der partiellen Massivumformung, wie dem Bohrungsdrücken, erfährt der Werkstoff eine zyklische Plastifizierung. Dabei bedeutet „zyklisch“ einerseits, dass jedes Werkstoffsegment nur temporär im Umformeingriff ist und dass andererseits der Werkstoff alternierend plastifiziert wird. Inhalt der Arbeit ist die Beschreibung der Geometrie der Umformzone beim Bohrungsdrücken mit Hilfe der Computertomographie und die Untersuchung des zyklisch plastischen Werkstoffverhaltens mit verbleibendem Umforminkrement pro Umformzyklus an Hand von Torsionsuntersuchungen. Mit der Computertomographie ist es gelungen, eine Umformzone bei der partiellen Massivumformung zerstörungsfrei zu analysieren. Die Umformzone kann in zwei Verformungsbereiche aufgeteilt werden. Im Stempelbereich wird der Werkstoff unter einem hohen hydrostatischen Druckspannungsanteil einsinnig plastisch verformt. Im Walkbereich wird der Werkstoff zyklisch plastisch verformt mit einem verbleibenden Umforminkrement pro Zyklus. Das zyklisch plastische Werkstoffverhalten wird von der Verformungsamplitude, der Zyklenzahl, der Verformungsgeschwindigkeit und der Temperatur geprägt. Die Differenzen sowohl zum einsinnigen Werkstoffverhalten als auch bei verschiedenen Verformungsparametern sind in der unterschiedlich ausgeprägten Versetzungszell- und Subkorbildung begründet. Die Umformarbeit unter einsinniger Torsion steht in einem bestimmten Verhältnis zur Umformarbeit unter zyklischer Belastung. Dieses Verhältnis beschreibt der Bauschinger-Energieparameter. Er kann für die energetische Beschreibung zyklisch plastischer Verformungen verwendet werden.

The primary objective of wastewater treatment is the removal of pollutants to meet given legal effluent standards. To further reduce operators costs additional recovery of resources and energy is desired by industrial and municipal wastewater treatment. Hence the objective in early stage of planning of treatment facilities lies in the identification and evaluation of promising configurations of treatment units. Obviously this early stage of planning may best be supported by software tools to be able to deal with a variety of different treatment configurations. In chemical process engineering various design tools are available that automatically identify feasible process configurations for the purpose to obtain desired products from given educts. In contrast, the adaptation of these design tools for the automatic generation of treatment unit configurations (process chains) to achieve preset effluent standards is hampered by the following three reasons. First, pollutants in wastewater are usually not defined as chemical substances but by compound parameters according to equal properties (e.g. all particulate matter). Consequently the variation of a single compound parameter leads to a change of related parameters (e.g. relation between Chemical Oxygen Demand and Total Suspended Solids). Furthermore, mathematical process models of treatment processes are tailored towards fractions of compound parameters. This hampers the generic representation of these process models which in turn is essential for automatic identification of treatment configurations. Second, treatment technologies for wastewater treatment rely on a variety of chemical, biological, and physical phenomena. Approaches to mathematically describe these phenomena cover a wide range of modeling techniques including stochastic, conceptual or deterministic approaches. Even more the consideration of temporal and spatial resolutions differ. This again hampers a generic representation of process models. Third, the automatic identification of treatment configurations may either be achieved by the use of design rules or by permutation of all possible combinations of units stored within a database of treatment units. The first approach depends on past experience translated into design rules. Hence, no innovative new treatment configurations can be identified. The second approach to identify all possible configurations collapses by extremely high numbers of treatment configurations that cannot be mastered. This is due to the phenomena of combinatorial explosion. It follows therefrom that an appropriate planning algorithm should function without the need of additional design rules and should be able to identify directly feasible configurations while discarding those impractical. This work presents a planning tool for the identification and evaluation of treatment configurations that tackles the before addressed problems. The planning tool comprises two major parts. An external declarative knowledge base and the actual planning tool that includes a goal oriented planning algorithm. The knowledge base describes parameters for wastewater characterization (i.e. material model) and a set of treatment units represented by process models (i.e. process model). The formalization of the knowledge base is achieved by the Web Ontology Language (OWL). The developed data model being the organization structure of the knowledge base describes relations between wastewater parameters and process models to enable for generic representation of process models. Through these parameters for wastewater characterization as well as treatment units can be altered or added to the knowledge base without the requirement to synchronize already included parameter representations or process models. Furthermore the knowledge base describes relations between parameters and properties of water constituents. This allows to track changes of all wastewater parameters which result from modeling of removal efficiency of applied treatment units. So far two generic treatment units have been represented within the knowledge base. These are separation and conversion units. These two raw types have been applied to represent different types of clarifiers and biological treatment units. The developed planning algorithm is based on a Means-Ends Analysis (MEA). This is a goal oriented search algorithm that posts goals from wastewater state and limit value restrictions to select those treatment units only that are likely to solve the treatment problem. Regarding this, all treatment units are qualified according to postconditions that describe the effect of each unit. In addition, units are also characterized by preconditions that state the application range of each unit. The developed planning algorithm furthermore allows for the identification of simple cycles to account for moving bed reactor systems (e.g. functional unit of aeration tank and clarifier). The evaluation of identified treatment configurations is achieved by total estimated cost of each configuration. The planning tool has been tested on five use cases. Some use cases contained multiple sources and sinks. This showed the possibility to identify water reuse capabilities as well as to identify solutions that go beyond end of pipe solutions. Beyond the originated area of application, the planning tool may be used for advanced interrogations. Thereby the knowledge base and planning algorithm may be further developed to address the objectives to identify configurations for any type of material and energy recovery.

The primary objective of wastewater treatment is the removal of pollutants to meet given legal effluent standards. To further reduce operators costs additional recovery of resources and energy is desired by industrial and municipal wastewater treatment. Hence the objective in early stage of planning of treatment facilities lies in the identification and evaluation of promising configurations of treatment units. Obviously this early stage of planning may best be supported by software tools to be able to deal with a variety of different treatment configurations. In chemical process engineering various design tools are available that automatically identify feasible process configurations for the purpose to obtain desired products from given educts. In contrast, the adaptation of these design tools for the automatic generation of treatment unit configurations (process chains) to achieve preset effluent standards is hampered by the following three reasons. First, pollutants in wastewater are usually not defined as chemical substances but by compound parameters according to equal properties (e.g. all particulate matter). Consequently the variation of a single compound parameter leads to a change of related parameters (e.g. relation between Chemical Oxygen Demand and Total Suspended Solids). Furthermore, mathematical process models of treatment processes are tailored towards fractions of compound parameters. This hampers the generic representation of these process models which in turn is essential for automatic identification of treatment configurations. Second, treatment technologies for wastewater treatment rely on a variety of chemical, biological, and physical phenomena. Approaches to mathematically describe these phenomena cover a wide range of modeling techniques including stochastic, conceptual or deterministic approaches. Even more the consideration of temporal and spatial resolutions differ. This again hampers a generic representation of process models. Third, the automatic identification of treatment configurations may either be achieved by the use of design rules or by permutation of all possible combinations of units stored within a database of treatment units. The first approach depends on past experience translated into design rules. Hence, no innovative new treatment configurations can be identified. The second approach to identify all possible configurations collapses by extremely high numbers of treatment configurations that cannot be mastered. This is due to the phenomena of combinatorial explosion. It follows therefrom that an appropriate planning algorithm should function without the need of additional design rules and should be able to identify directly feasible configurations while discarding those impractical. This work presents a planning tool for the identification and evaluation of treatment configurations that tackles the before addressed problems. The planning tool comprises two major parts. An external declarative knowledge base and the actual planning tool that includes a goal oriented planning algorithm. The knowledge base describes parameters for wastewater characterization (i.e. material model) and a set of treatment units represented by process models (i.e. process model). The formalization of the knowledge base is achieved by the Web Ontology Language (OWL). The developed data model being the organization structure of the knowledge base describes relations between wastewater parameters and process models to enable for generic representation of process models. Through these parameters for wastewater characterization as well as treatment units can be altered or added to the knowledge base without the requirement to synchronize already included parameter representations or process models. Furthermore the knowledge base describes relations between parameters and properties of water constituents. This allows to track changes of all wastewater parameters which result from modeling of removal efficiency of applied treatment units. So far two generic treatment units have been represented within the knowledge base. These are separation and conversion units. These two raw types have been applied to represent different types of clarifiers and biological treatment units. The developed planning algorithm is based on a Means-Ends Analysis (MEA). This is a goal oriented search algorithm that posts goals from wastewater state and limit value restrictions to select those treatment units only that are likely to solve the treatment problem. Regarding this, all treatment units are qualified according to postconditions that describe the effect of each unit. In addition, units are also characterized by preconditions that state the application range of each unit. The developed planning algorithm furthermore allows for the identification of simple cycles to account for moving bed reactor systems (e.g. functional unit of aeration tank and clarifier). The evaluation of identified treatment configurations is achieved by total estimated cost of each configuration. The planning tool has been tested on five use cases. Some use cases contained multiple sources and sinks. This showed the possibility to identify water reuse capabilities as well as to identify solutions that go beyond end of pipe solutions. Beyond the originated area of application, the planning tool may be used for advanced interrogations. Thereby the knowledge base and planning algorithm may be further developed to address the objectives to identify configurations for any type of material and energy recovery.

Steigende Anforderungen an die Genauigkeit und Aussagekraft numerischer Berechnungen sowie neue Werkstoffe und Prozesse erfordern zunehmend technologische Erweiterungen und Entwicklungen innerhalb der Softwarelösungen. Neben der Ausweitung von Möglichkeiten in der Material- und Prozessmodellierung sowie der genaueren Abbildung physikalischer Prozesse, stehen dabei zunehmend Prozessketten im Fokus. Die Historie, welche Halbzeuge oder Bauteile bereits vom Urformprozess an mit sich führen, bestimmt dabei zu einem erheblichen Grad die Eigenschaften und damit auch mögliche Verfahrensgrenzen in allen nachfolgenden Prozessstufen. Dadurch wird deutlich, dass eine Betrachtung ebendieser vorangegangenen Schritte einen deutlichen Einfluss auf das Bauteilverhalten in den Folgeprozessen hat, wodurch die Aussagekraft entkoppelter Simulationen ab einem bestimmten Detailgrad dahingehend begrenzt bleibt. Der damit steigenden Komplexität von Berechnungsproblemen stehen auf der anderen Seite Forderungen einer immer leichter und intuitiver werdenden Bedienung von Simulationssoftware entgegen. Hexagons Smart Shop Softwarelösungen nehmen dabei das Problem der Prozessketten- simulation von der Umformung bis zur Assemblierung in den Fokus. Neben der Berechnung und Evaluierung von komplexen metallischen Baugruppen sowie der nahtlosen Integration physischer Messtechnik, spielt dabei die Bedienbarkeit eine bedeutende Rolle. Das Ziel ist mögliche Probleme in der Produktentwicklung zeitnah zu detektieren und zu adressieren, um physische Prototypen auf ein Minimum reduzieren zu können (Abbildung 1). Am Beispiel eines Karosseriebauteils erfolgt innerhalb des Beitrags die Darstellung eines Workflows zur Detektion möglicher Fertigungsprobleme in der Produktentwicklungsphase sowie die Beleuchtung weiterer Anwendungsfälle. / Increasing demands towards the accuracy and significance of numerical simulations as well as new materials and processes require technological enhancements and developments within the software solutions. Beyond extending possibilities for material- and process modeling as well as more accurate prediction of physical behavior, process chains get into the spotlight more and more. The history which parts already inherit as from the casting stage on determines the attributes and therefore also possible process limits in all following production stages. This illustrates that the analysis of this preceding process steps has a significant impact on the part behavior in all subsequent steps, which is why the meaningfulness of decoupled simulations is limited at a certain demand for detail. The increasing complexity of the simulation problems on one hand are facing demands for higher user friendliness and more intuitive control of the simulation software on the other hand. Hexagons Smart Shop software solutions have their focus on the process chain simulation from forming to assembly. Beyond the calculation and evaluation of complex metallic assemblies and the seamless integration of metrology devices, usability plays a major role. The aim hereby is to detect and address possible issue within the product development as early as possible and therefore reduce physical prototypes to a minimum. With the example of a body in white part, it is shown how a workflow for the detection of possible manufacturing challenges within the product development as well as other alternative use cases can look like.

During the manufacturing planning, multiple variants of process chains for the manufacturing of a product to be developed are generated by engineers. In order to select an optimal variant, multiple decision criteria specifying technical, ecological and economical properties of the process chains as well as multiple assessments of different domain experts have to be taken into account. The contribution of this article is a two-step approach that provides a multi-criteria multi-expert assessment of manufacturing process chains supporting the selection of an optimal process chain. A web-based software tool that implements the multi-criteria assessment of process chains is also presented.

Bei den Verfahren der partiellen Massivumformung, wie dem Bohrungsdrücken, erfährt der Werkstoff eine zyklische Plastifizierung. Dabei bedeutet „zyklisch“ einerseits, dass jedes Werkstoffsegment nur temporär im Umformeingriff ist und dass andererseits der Werkstoff alternierend plastifiziert wird. Inhalt der Arbeit ist die Beschreibung der Geometrie der Umformzone beim Bohrungsdrücken mit Hilfe der Computertomographie und die Untersuchung des zyklisch plastischen Werkstoffverhaltens mit verbleibendem Umforminkrement pro Umformzyklus an Hand von Torsionsuntersuchungen. Mit der Computertomographie ist es gelungen, eine Umformzone bei der partiellen Massivumformung zerstörungsfrei zu analysieren. Die Umformzone kann in zwei Verformungsbereiche aufgeteilt werden. Im Stempelbereich wird der Werkstoff unter einem hohen hydrostatischen Druckspannungsanteil einsinnig plastisch verformt. Im Walkbereich wird der Werkstoff zyklisch plastisch verformt mit einem verbleibenden Umforminkrement pro Zyklus. Das zyklisch plastische Werkstoffverhalten wird von der Verformungsamplitude, der Zyklenzahl, der Verformungsgeschwindigkeit und der Temperatur geprägt. Die Differenzen sowohl zum einsinnigen Werkstoffverhalten als auch bei verschiedenen Verformungsparametern sind in der unterschiedlich ausgeprägten Versetzungszell- und Subkorbildung begründet. Die Umformarbeit unter einsinniger Torsion steht in einem bestimmten Verhältnis zur Umformarbeit unter zyklischer Belastung. Dieses Verhältnis beschreibt der Bauschinger-Energieparameter. Er kann für die energetische Beschreibung zyklisch plastischer Verformungen verwendet werden.

During the manufacturing planning, multiple variants of process chains for the manufacturing of a product to be developed are generated by engineers. In order to select an optimal variant, multiple decision criteria specifying technical, ecological and economical properties of the process chains as well as multiple assessments of different domain experts have to be taken into account. The contribution of this article is a two-step approach that provides a multi-criteria multi-expert assessment of manufacturing process chains supporting the selection of an optimal process chain. A web-based software tool that implements the multi-criteria assessment of process chains is also presented.