Laser chemical machining, a non-conventional processing method based on thermally activated electrochemical material dissolution, represents a promising technology for manufacturing metallic dies for micro forming applications. Prior to widespread industrial acceptance the machining quality of laser chemical machining should be characterized. For this purpose, laser chemical machining is compared with micro milling regarding both the dimensional accuracy and the surface quality. Therefore, square micro cavities exhibiting side walls between 100 μm and 400 μm in length and 60 μm in depth are machined with both manufacturing processes into the cobalt-chrome alloy Stellite 21. The geometrical features are investigated using laser-scanning confocal microscopy and scanning electron microscopy. On the one hand, laser chemical machining is more suitable for manufacturing cavities with dimensions < 200 μm due to higher shape accuracy with stable mean edge radii of (11.2 ± 1.3) μm as a result of roughing and finishing steps. On the other hand, the finish quality of micro milling with mean surface roughness Sa of 0.2 μm could not be achieved with laser chemical machining due to in-process induced waviness. Finally, the metallographic analysis of the surface-near layers reveals that both manufacturing processes ensure gentle machining without any noticeable micro structural impact.

The in situ geometry measurement of microstructures in the laser chemical machining (LCM) manufacturing process places high demands on measurement systems because the specimen is submerged in a closed fluid circuit. The steep slopes of the manufactured micro-components and the general lack of accessibility hinder the use of standard techniques such as tactile measurement or conventional confocal microscopy. A technique based on confocal fluorescence microscopy shows promise for increasing the measurability on metallic surfaces with large curvatures. By applying an intensely scattering fluorescent coating to the specimen, the surface position can be determined by the change in fluorescence signal at the boundary between specimen and coating. In contrast to the currently tested thin coatings (\100 lm) the measurements in layers thicker than 1 mm, as required for in situ application at the LCM process, show distinct dependencies on the fluorescent medium in terms of concentration and index of refraction. Hence, a fundamentally different signal evaluation approach based on a physical model of the fluorescence signal is needed to extract the surface position information from the detected fluorescence intensity signal. For the purpose of validation, the measurement of a step geometry is performed under the condition of a thick fluid layer and referenced with a tactile measurement. As a result, the model-based approach is shown to be suitable to detect the geometry parameter step height with an uncertainty of 8:8 lm for a step submerged in a fluid layer with a thickness of 2:3 mm.

Due to the challenging environment of micro-manufacturing techniques where the workpiece is submerged in a fluid, a contactless in situ capable measurement is required for quality control. However, the in situ conditions and the small specimen dimensions hinder the use of conventional metrology. Confocal fluorescence microscopy is shown to enable step height measurements of a specimen submerged in a 2.6 mm thick fluid layer with an uncertainty of 8.8 μm by fitting a model of the fluorescence intensity to the measured signal. To ascertain the potential of the proposed measurement approach, the minimal achievable uncertainty of 0.07 μm for a shot-noise-limited signal is derived.

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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.