In this contribution a framework for the computer-aided planning and optimisation of functional graded components is presented. The framework is divided into three modules -the "Component Description", the "Expert System" for the synthetisation of several process chains and the "Modelling and Process Chain Optimisation". The Component Description module enhances a standard computer-aided design (CAD) model by a voxel-based representation of the graded properties. The Expert System synthesises process steps stored in the knowledge base to generate several alternative process chains. Each process chain is capable of producing components according to the enhanced CAD model and usually consists of a sequence of heating-, cooling-, and forming processes. The dependencies between the component and the applied manufacturing processes as well as between the processes themselves need to be considered. The Expert System utilises an ontology for that purpose. The ontology represents all dependencies in a structured way and connects the information of the knowledge base via relations. The third module performs the evaluation of the generated process chains. To accomplish this, the parameters of each process are optimised with respect to the component specification, whereby the result of the best parameterisation is used as representative value. Finally, the process chain which is capable of manufacturing a functionally graded component in an optimal way regarding to the property distributions of the component description is presented by means of a dedicated specification technique.

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In this contribution, we present a framework for the computer-aided planning and optimisation of manufacturing process chains for functional graded components. The framework is divided into three modules-the "Component Description", the "Expert System" for the synthetisation of several manufacturing process chains and the "Modelling and Process Chain Optimisation". The Component Description module enhances a standard computer-aided design (CAD) model by a voxel-based representation of the intended graded properties. The Expert System synthesises manufacturing process steps stored in the knowledge base to generate alternative process chains. All these process chains are capable of producing components according to the enhanced CAD model. They consist of a sequence of heating-, cooling-, and forming manufacturing processes. The interdependencies between the component and the applied manufacturing processes as well as between the processes themselves need to be considered. For that purpose the Expert System utilises an ontology. The ontology represents all the interdependencies in a structured way and connects the information of the knowledge base via relations. The third module performs the evaluation of the generated manufacturing process chains. To accomplish this, the parameters of each process step are optimised according to the component specification, whereby the result of the best parameterization is used as a representative value. Finally the process chain which is capable of producing a functional graded component in an optimal way regarding to the property distributions of the component description is presented by means of a dedicated specification technique.

Current production control systems cannot react appropriately to unknown situations (e.g. the dispatch of rush jobs). They are only able to react on known situations with a predefined behaviour. In this paper the paradigm of self-optimisation will be transferred to the production control level by using a procedure model to design a self-optimising production control system. The production control is then able to react autonomously on changing operational conditions and to deduce new reaction strategies for occurring faults or disturbances. A rule based decision model is the core of the conceptual design. It is based on known and possible future faults and deducts reaction strategies. Simultaneously to them, a simulation model will be proposed, that simulates and evaluates suitable strategies.