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The automated process chain of an unmanned production system is a distinct challenge in the technical state of the art. In particular, accurate and fast raw-part recognition is a current problem in small-batch production. This publication proposes a method for automatic optical raw-part detection to generate a digital blank shadow, which is applied for adapted CAD/CAM (computer-aided design/computer-aided manufacturing) planning. Thereby, a laser-triangulation sensor is integrated into the machine tool. For an automatic raw-part detection and a workpiece origin definition, a dedicated algorithm for creating a digital blank shadow is introduced. The algorithm generates adaptive scan paths, merges laser lines and machine axis data, filters interference signals, and identifies part edges and surfaces according to a point cloud. Furthermore, a dedicated software system is introduced to investigate the created approach. This method is integrated into a CAD/CAM system, with customized software libraries for communication with the CNC (computer numerical control) machine. The results of this study show that the applied method can identify the positions, dimensions, and shapes of different raw parts autonomously, with deviations less than 1 mm, in 2.5 min. Moreover, the measurement and process data can be transferred without errors to different hardware and software systems. It was found that the proposed approach can be applied for rough raw-part detection, and in combination with a touch probe for accurate detection.

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Production planning is a critical step for the implementation of sustainable production. It is necessary to consider energy and resource efficiency in all planning phases to promote sustainable production. In this paper, an approach for environmental impact assessment in all phases of process chain planning supported by process models is presented. The level of detail of the assessment is determined based on the level of detail of the planning phase. During the assessment, consumption of energy and resources is considered. This approach aims to align planning phases with the objective of sustainable production. In rough planning, the approach allows the selection of an ecologically favorable process chain. In detailed planning, process parameters can be selected based on their ecological sustainability. The approach can be integrated into the planning of process chains in order to consider ecological factors throughout all planning phases. The approach is evaluated by using an exemplary use case. The results indicate that rough planning under the consideration of uncertainties can form a reasonable prediction about resource efficiency for possible manufacturing routes. By systematically selecting a resource-efficient process chain, energy savings of up to 21% can be achieved for the presented use case.

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