The growing demand for higher quality and more sustainable products increases the need for fully digitized CAx process chains among developers and manufacturers. Although there are many good digital approaches, CAx process chains currently have large gaps in data traceability. Process planning, manufacturing and quality assurance are currently working with different and incomplete digital twins.
As a result, the design of CAx process chains requires many iteration loops to achieve a high degree of component maturity and process stability. In this context, tool path optimization is an inefficient process step because data-consistent feedback loops between process planning, manufacturing and quality assurance are missing. Under current working conditions, CAM programmers optimize toolpaths based on their empirical knowledge. Data that can be already accessed in real production today has up to now not been used to compensate process-related geometric manufacturing errors.
The aim of the "DATARAMP" research project is to develop a fully digital process chain for 5-axis milling of thin-walled components based on manufacturing and quality data. For this purpose, data from preceding milling processes is acquired, synchronized and processed. Based on these data sources, profile deviations of the machined workpiece can be evaluated. The results are then imported into the CAM system and considered in the new tool path calculation. During the project, an adaptive process chain with feedback loops is created on the basis of the evaluated data, which makes it possible to compensate profile deviations of thin-walled components during tool path calculation.
In the first stage of the project, thin-walled demonstrator components are milled at the Fraunhofer IPT. During the milling process, the machine and sensor data is continuously acquired and processed. Using the data, the researchers generate a digital twin of the component during production and evaluate the various component and process states. The goal of the data analysis is to identify profile deviations caused by static displacement of the tool and the workpiece. One challenge here is to synchronize the data from the different data sources so that all information can be assigned to a tool position or the contact point on the workpiece surface. A precise location reference of the acquired data points is the basis for evaluating local profile deviations of the workpiece.
In the next project step, optical measuring systems generate quality data that reveal profile deviations on the component in the single-digit micrometer range. The scanned demonstrator components serve as the basis for analyzing the geometric deviations in comparison to the CAD geometry. In this way, causal relationships between the geometric deviations of the workpiece and the manufacturing-related static tool and workpiece displacements can be derived.
In the third phase of the project, the Fraunhofer IPT is working with the consortium partners to develop a CAM software for optimizing the tool path for 5-axis milling in order to minimize static deflections. For this purpose, the software's calculation algorithm considers the acquired process monitoring and quality data from the first two project steps. A compensation method integrated into the CAM system ensures that the monitored and inspected machining errors are automatically identified and the original tool path is optimized. This creates an adaptive path calculation tool that CAM programmers can use to optimize initially created operations.
The DATARAMP approach promises significant improvements in productivity:
The research project "DATARAMP - Data-based Ramp-up Acceleration for Resilient Manufacturing" is funded by the German Federal Ministry for Education and Research (BMBF) within the Eurostars (Eureka) program. Funding code: E! 115645