  To enable automatic disassembly of different product types with uncertain
conditions and degrees of wear in remanufacturing, agile production systems
that can adapt dynamically to changing requirements are needed. Machine
learning algorithms can be employed due to their generalization capabilities of
learning from various types and variants of products. However, in reality,
datasets with a diversity of samples that can be used to train models are
difficult to obtain in the initial period. This may cause bad performances when
the system tries to adapt to new unseen input data in the future. In order to
generate large datasets for different learning purposes, in our project, we
present a Blender add-on named MotorFactory to generate customized mesh models
of various motor instances. MotorFactory allows to create mesh models which,
complemented with additional add-ons, can be further used to create synthetic
RGB images, depth images, normal images, segmentation ground truth masks, and
3D point cloud datasets with point-wise semantic labels. The created synthetic
datasets may be used for various tasks including motor type classification,
object detection for decentralized material transfer tasks, part segmentation
for disassembly and handling tasks, or even reinforcement learning-based
robotics control or view-planning.
