Abstract Within the metal cutting industry, methods of statistical process control are used to monitor the product quality of series processes. Following the specifications of an initial test planning, produced components are inspected in a sample-like manner. Due to the large number of machining centers, the resulting high output of manufactured parts and centrally installed quality assurance departments, there is still a considerable time delay when feeding back negative trends or tolerance violations. Within this period, scrap parts may be produced. The approach presented in this paper addresses this problem. Based on a combination of process-parallel recorded sensor signals and available manufacturing data along the CAD-CAM-NC process chain, an online material removal simulation is performed next to a real milling process. Within the simulation, the properties of cutting tools, machine tool and manufacturing process directly impacting workpiece quality are compiled in efficient models that run simultaneously and further approximate the machined workpiece shape. The resulting, digital workpiece is measured virtually based on given quality requirements defined in ISO 1101 to allow an early identification of negative trends and quality issues while reducing long quality feedback loops.
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