  During the execution of handling processes in manufacturing, it is difficult
to measure the process forces with state-of-the-art gripper systems since they
usually lack integrated sensors. Thus, the exact state of the gripped object
and the actuating process forces during manipulation and handling are unknown.
This paper proposes a deep learning regression model to construct a continuous
stability metric to predict the maximum process forces on the gripped objects
using high-resolution optical tactile sensors. A pull experiment was developed
to obtain a valid dataset for training. Continuously force-based labeled pairs
of tactile images for varying grip positions of industrial gearbox parts were
acquired to train a novel neural network inspired by encoder-decoder
architectures. A ResNet-18 model was used for comparison. Both models can
predict the maximum process force for each object with a precision of less than
1 N. During validation, the generalization potential of the proposed
methodology with respect to previously unknown objects was demonstrated with an
accuracy of 0.4-2.1 N and precision of 1.7-3.4 N, respectively.
