Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Capturing and incorporating expert knowledge into machine learning
  models for quality prediction in manufacturing   Increasing digitalization enables the use of machine learning methods for
analyzing and optimizing manufacturing processes. A main application of machine
learning is the construction of quality prediction models, which can be used,
among other things, for documentation purposes, as assistance systems for
process operators, or for adaptive process control. The quality of such machine
learning models typically strongly depends on the amount and the quality of
data used for training. In manufacturing, the size of available datasets before
start of production is often limited. In contrast to data, expert knowledge
commonly is available in manufacturing. Therefore, this study introduces a
general methodology for building quality prediction models with machine
learning methods on small datasets by integrating shape expert knowledge, that
is, prior knowledge about the shape of the input-output relationship to be
learned. The proposed methodology is applied to a brushing process with $125$
data points for predicting the surface roughness as a function of five process
variables. As opposed to conventional machine learning methods for small
datasets, the proposed methodology produces prediction models that strictly
comply with all the expert knowledge specified by the involved process
specialists. In particular, the direct involvement of process experts in the
training of the models leads to a very clear interpretation and, by extension,
to a high acceptance of the models. Another merit of the proposed methodology
is that, in contrast to most conventional machine learning methods, it involves
no time-consuming and often heuristic hyperparameter tuning or model selection
step.

14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys