Comparison of model-based methods with machine learning strategies for defect reconstruction, classification, and regression in the field of measurement technology

Automation, Industry 4.0 and artificial intelligence are playing an increasingly central
role for companies. Artificial intelligence in particular is currently enabling new methods
to achieve a higher level of automation. However, machine learning methods are
usually particularly lucrative when a lot of data can be easily collected and patterns
can be learned with the help of this data. In the field of metrology, this can prove difi-
cult depending on the area of work. Particularly for micrometer-scale measurements,
measurement data often involves a lot of time, effort, patience, and money, so measurement
data is not readily available. This raises the question of how meaningfully machine
learning approaches can be applied to different domains of measurement tasks,
especially in comparison to current solution approaches that use model-based methods.
This thesis addresses this question by taking a closer look at two research areas
in metrology, micro lead determination and reconstruction. Methods for micro lead
determination are presented that determine texture and tool axis with high accuracy.
The methods are based on signal processing, classical optimization and machine learning.
In the second research area, reconstructions for cutting edges are considered in
detail. The reconstruction methods here are based on the robust Gaussian filter and
deep neural networks, more specifically autoencoders. All results on micro lead and
reconstruction are compared and contrasted in this thesis, and the applicability of the
dierent approaches is evaluated.