Early and efficient harmonization between product design and manufacturing represents one of the most challenging tasks in engineering. Concepts such as simultaneous engineering aim for a product creation process, which addresses both, functional requirements as well as requirements from production. However, existing concepts mostly focus on organizational tasks and heavily rely on the human factor for the exchange of complex information across different domains, organizations or systems. Nowadays product and process design make use of advanced software tools such as computer-aided design, manufacturing and engineering systems (CAD/CAM/CAE). Modern systems already provide a seamless integration of both worlds in a single digital environment to ensure a continuous workflow. Yet, for the holistic harmonization between product and process design, a complete and data consistent digital twin, an adaptation of product and process design for a balanced functionality and manufacturability, as well as systematic long-term data analytics across different product and process designs are missing. This paper presents an exploration concept which couples product design (CAD), process design (CAM), process simulation (CAE) and process adaptation in a single software system. The approach provides insights into correlations and dependencies between input parameters of product/process design and the process output. The insights potentially allow for a knowledge-based adaptation, tackling well-known optimization issues such as parameter choice or operation sequencing. First results are demonstrated using the example of a blade integrated disk (blisk).

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Early and efficient harmonization between product design and manufacturing represents one of the most challenging tasks in engineering. Concepts such as simultaneous engineering aim for a product creation process, which addresses both, functional requirements as well as requirements from production. However, existing concepts mostly focus on organizational tasks and heavily rely on the human factor for the exchange of complex information across different domains, organizations or systems. Nowadays product and process design make use of advanced software tools such as computer-aided design, manufacturing and engineering systems (CAD/CAM/CAE). Modern systems already provide a seamless integration of both worlds in a single digital environment to ensure a continuous workflow. Yet, for the holistic harmonization between product and process design, the following aspects are missing: • The digital environment does not provide a complete and data consistent digital twin of the component; this applies especially to the process design and analysis environment • Due to the lack of process and part condition data in the manufacturing environment an adaptation of product and process design for a balanced functionality and manufacturability is hindered • Systematic long-term data analytics across different product and process designs with the ultimate goal to transfer knowledge from one product to the next and to accelerate the entire product development process is not considered This paper presents an exploration concept which couples product design (CAD), process design (CAM), process simulation (CAE) and process adaptation in a single software system. The approach provides insights into correlations and dependencies between input parameters of product/process design and the process output. The insights potentially allow for a knowledge-based adaptation, tackling well-known optimization issues such as parameter choice or operation sequencing. First results are demonstrated using the example of a blade integrated disk (blisk).

The main objectives in production technology are quality assurance, cost reduction, and guaranteed process safety and stability. Digital shadows enable a more comprehensive understanding and monitoring of processes on shop floor level. Thus, process information becomes available between decision levels, and the aforementioned criteria regarding quality, cost, or safety can be included in control decisions for production processes. The contextual data for digital shadows typically arises from heterogeneous sources. At shop floor level, the proximity to the process requires usage of available data as well as domain knowledge. Data sources need to be selected, synchronized, and processed. Especially high-frequency data requires algorithms for intelligent distribution and efficient filtering of the main information using real-time devices and in-network computing. Real-time data is enriched by simulations, metadata from product planning, and information across the whole process chain. Well-established analytical and empirical models serve as the base for new hybrid, gray box approaches. These models are then applied to optimize production process control by maximizing the productivity under given quality and safety constraints. To store and reuse the developed models, ontologies are developed and a data lake infrastructure is utilized and constantly enlarged laying the basis for a World Wide Lab (WWL). Finally, closing the control loop requires efficient quality assessment, immediately after the process and directly on the machine. This chapter addresses works in a connected job shop to acquire data, identify and optimize models, and automate systems and their deployment in the Internet of Production (IoP).

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