Micro milling is a flexible technique for the production of micro mechanical components like dies and moulds and process control is the key to reach the strong production requirements. Requirements, given by engineers and designers, are addressed mainly to the functional performance of the produced part, therfore topographic features are most decisive. Surface parameters, mainly of statistical origin, have been used for a long time in surface characterisation and process monitoring. Furthermore, it is known that these parameters correlate with the desired functional behaviour, but this knowledge is usually not used for a deterministic process design, uneconomic try and error approaches are still common.Mathematical investigations can use the full process flexibility for an in-process functionalization by selecting optimal conditions and process parameters with respect to a set of relevant surface parameters. In this study, micro ball-end milling is investigated and process parameters in order meet a predefined bearing ratio curve as accurately as possible are identified. Therefore, a mechanistic surface generation model has been developed and is used as a forward model for an iterative optimisation. Static and dynamic process geometry and a micro mechanical material removal operator are the main features of the model. In the first part of the paper the semi-empirical model is calibrated for certain tool and workpiece materials. In the second part optimal feed speed and width of cut are determined. Finally, an experimental validation is presented and the comparison of the predefined, the predicted and the experimental bearing ratio curves shows a good agreement.

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Precise measurement of mechanical forces is crucial to efficient micro-manufacturing. The quality of such measurements depends heavily on the properties of the noise inevitably accompanying every measurement process. In the micro-range, the signal-to-noise ratio tends to be very low, and the noise dynamic varies for different frequencies. In result, common denoising methods that assume white noise perform poorly in this setting. In this paper, a novel, easily implementable denoising method based on a local statistic of the measured data's spectrum is proposed. By testing it on a representative dataset, it is shown that the proposed method is robust and stable. Particularly, it allows for an efficient retrieval of the force signal encountered in micro-milling processes.

Quality inspection is an essential tool for quality assurance during production. In the microscopic domain, where the manufactured objects have a size of less than 1 mm in at least two dimensions, very often mass production takes place with high demands regarding the failure rate, as micro components generally form the basis for larger assemblies. Especially when it comes to safety-relevant parts, e.g. in the automobile or medical industry, a 100% quality inspection is mandatory. Here, we present a robust and precise metrology method comprised of a holographic contouring system with fast algorithms for geometric evaluation and surface defect detection that paves the way for inspecting cold formed micro parts in less than a second. Using a telecentric lens instead of a standard microscope objective, we compensate scaling effects and wave field curvature, which distort the reconstruction in digital holographic microscopy. To enhance the limited depth of focus of the microscope objective, depth information from different object layers is stitched together to yield 3D data of its complete geometry. The 3D data map is converted into a point cloud and processed by geometry and surface inspection. Thereby, the resulting point cloud data are automatically decomposed into geometric primitives in order to analyze geometric deviations. Additionally, the surface itself is checked for scratches and other defects by the use of convolutional neural networks. The developed machine learning algorithm makes it possible not only to distinguish between good and failed parts but also to show the defect area pixel-wise. The methods are demonstrated by quality inspection of cold formed micro cups. Defects larger than 2 lm laterally and 5 lm axially can be detected.

Databased prediction models are used to estimate a possible outcome for previously unknown production parameters. These forward models enable to test new production designs and parameters virtually before applying them in the real world. Cause-effect networks are one way to generate such a prediction model. Multiple inputs and stages are being connected to one large prediction model. The functional behaviour and correlation of inputs as well as outputs is obtained through data based learning. In general, these models are non-linear and not invertible, especially for micro cold forming processes. While already being useful in process design, such models have their highest impact if inverted to find process parameters for a given output. Combining methods from the mathematical field of inverse problems as well as machine learning, a generalized inverse can be approximated. This allows finding process parameters for a given output without inverting the model directly but still using inherit information of the forward model. In this work, Tikhonov functionals are used to perform a parameter identification. The classical approach is altered by changing the discrepancy term to incorporate tolerances. Thereby, small deviations of a certain pattern are being neglected and the parameter finding process is being stabilized. In addition, different types of regularization are taken into consideration. Besides theoretical aspects of this method, examples are provided to demonstrate advantages and boundaries of an application for the process design in micro cold forming processes.